# nerding.io Video Library > AI-powered software engineering tutorials and insights ## Videos (65 total) ### Multiple Ways to Use MCPs with Stripe **Published:** 11/18/2025 **Duration:** 16 minutes **YouTube:** https://youtube.com/watch?v=rtGlC0-GuJw **Full LLM Text:** /video/cmis2l9kc000004jray5su8xz/llm.txt **Description:** In this video, we explore all the ways you can use the Stripe MCP (Model Context Protocol) to supercharge your AI agents, automations, developer cycle and workflows. The Stripe MCP server exposes Stripe’s APIs — billing, subscriptions, customers, products, and invoices — as standardized MCP tools. That means your AI model (Claude, Cursor, LangGraph, n8n, or any MCP-compatible client) can interact with Stripe safely and intelligently. This lets your AI agents take action, not just talk. With standardized access to payment tools, you can give your assistants, dashboards, or automation bots transactional superpowers — securely and safely, without direct API calls. It’s the bridge between AI reasoning and real-world business operations. **Summary:** ## Stripe MCP Video Summary: Supercharging AI with Standardized Payment Tools **1. Executive Summary:** This video explains how Stripe's Model Context Protocol (MCP) can be leveraged to integrate Stripe's payment functionalities into AI agents and workflows. MCP provides a standardized, secure, and bidirectional communication channel enabling AI to interact with Stripe's APIs for actions like creating products, managing subscriptions, and retrieving customer data, thus enhancing AI's transactional capabilities. **2. Main Topics Covered:** * **Introduction to MCP:** Definition, purpose, and benefits of using MCP as a communication protocol between AI agents and servers. * **MCP vs. Traditional API Tools:** Differences between MCP's unified, standardized approach and individual API tool connections. * **Cursor Integration:** Utilizing MCP integrations (e.g., GitHub, Supabase, Stripe, Context7) within the Cursor IDE. * **Local vs. Remote MCP Servers:** Understanding the differences between standard in/out (local) and HTTP streamable (remote) MCP servers, and their security implications. * **MCP in AI Chatbots:** Integrating MCP clients within AI chatbots to enhance functionality by retrieving context and executing actions (e.g., order information, product creation) via Stripe. * **Practical Examples:** Demonstrations of using Context7 for Stripe documentation retrieval and creating a Stripe product directly from Cursor using MCP. * **Implementing MCP in Code:** Using the AI SDK to add MCP clients to existing code projects. **3. Key Takeaways:** * **Standardized Communication:** MCP offers a unified and standardized way for AI agents to interact with various services, streamlining development and integration. * **Bidirectional Interaction:** MCP facilitates two-way communication, enabling data ingestion, action execution, and notifications between clients and servers. * **Action-Oriented AI:** MCP allows AI agents to take actions (e.g., creating products, managing subscriptions) instead of just providing information. * **Enhanced Security:** MCP helps in achieving secure access to payment tools without direct API calls. * **Flexibility:** MCP servers can be installed locally or accessed remotely, offering flexibility in deployment and security. * **Improved User Experience:** MCP enhances user support and engagement by allowing chatbots to access context and execute actions via Stripe. **4. Notable Quotes or Examples:** * "MCP is basically a system that allows us to either ingest more data or take action on different kinds of data. So what this means is that it is a birectional communication protocol that allows us to have a client which is things like cursor or an AI agent and communicate to a server that has the ability to get context or take action through tools, resources or prompts." * "The difference between MCP and tools is pretty interesting. What that means is when you have a tool, you basically have a single connection to an API... what MCP is doing it is is allowing us to basically say as a unified way to send information." * Example: Creating a product directly through Cursor using the Stripe MCP integration. * "The ability to actually plug and play these different types of MCP servers gives a lot of added value to the overall experience of the application." **5. Target Audience:** * AI developers and engineers * Individuals interested in integrating AI agents with payment gateways * Developers working with Stripe's APIs * Those interested in using MCP to supercharge AI agents, automations, developer cycle and workflows. * Users of Cursor or other MCP-compatible IDEs and platforms. **Key Highlights:** - **MCP: Bidirectional Communication Protocol**: MCP facilitates two-way communication between clients (like AI agents) and servers, enabling data ingestion, action execution via tools, and notifications. - **MCP vs. Traditional API Tools**: MCP offers a unified, standardized way to send information compared to individual, one-way API tool connections, streamlining agent integration and development. - **Cursor Integration with MCP**: Cursor utilizes MCP integrations (e.g., GitHub, Supabase, Stripe, Context7) to extend its capabilities, allowing users to interact with these services directly within the IDE. - **Local vs. Remote MCP Servers**: MCP servers can be installed locally (standard in/out) or accessed remotely via URLs (HTTP streamable), impacting security and authentication methods. - **MCP in AI Chatbots**: MCP clients can be integrated within AI chatbots to access context and execute actions, such as retrieving order information or placing new orders via Stripe, enhancing user support. --- ### How to Build ChatGPT Apps with Next.js and Vercel: Full Technical Tutorial **Published:** 10/21/2025 **Duration:** 13 minutes **YouTube:** https://youtube.com/watch?v=RlXbdsvvxOI **Full LLM Text:** /video/cmis2llzw000104jrtipgmpbe/llm.txt **Description:** Want to build a full-featured, interactive app that runs natively inside ChatGPT? This video is your complete guide! We will show you exactly how to integrate a modern Next.js application with the OpenAI ChatGPT Apps SDK and the Model Context Protocol (MCP). This isn't just static content—it’s a powerful web application that works seamlessly within the chat window. --- Integrating a complex framework like Next.js into ChatGPT’s interface creates huge technical problems because of its super-strict triple-iframe security architecture. Ready to build? Let’s dive in! **Resources:** * Vercel Blog Post (Deep Dive Source): https://vercel.com/blog/running-next-js-inside-chatgpt-a-deep-dive-into-native-app-integration * Vercel ChatGPT Apps SDK Starter Template: https://vercel.com/changelog/chatgpt-apps-support-on-vercel **Summary:** Here's a comprehensive summary document for the provided video content: **1. Executive Summary** This video provides a technical tutorial on building and deploying ChatGPT apps using Next.js and Vercel, leveraging the Model Context Protocol (MCP). It demonstrates how to create interactive experiences within the ChatGPT interface by integrating a Next.js application, handling complex security architecture, and utilizing Vercel's ChatGPT Apps SDK. **2. Main Topics Covered** * **Introduction to ChatGPT App Development with Vercel:** Overview of Vercel's support for ChatGPT apps and the benefits of using MCP for interactive chatbot experiences. * **Setting up the Development Environment:** Explanation of utilizing a Next.js template, deploying it on Vercel, and understanding the core components like the MCP route, OpenAI metadata, and CORS middleware. * **Integrating with ChatGPT:** Demonstrating how to connect the deployed Next.js app to ChatGPT using connectors and configuring the necessary settings in developer mode. * **MCP Deep Dive:** Detailed explanation of the Model Context Protocol (MCP), including resource templates (HTML files), tool calls, chaining actions, and how data is passed between the ChatGPT interface and the Next.js application. * **Debugging and Inspection:** Using the MCP inspector to examine resources, tool calls, and data flow for debugging and understanding the application's behavior. **3. Key Takeaways** * Vercel simplifies ChatGPT app deployment by providing tooling and support for MCP. * MCP allows developers to create interactive and dynamic chatbot experiences by enabling real-time updates and communication with web applications within ChatGPT. * Resource templates, specifically HTML files, are used to render dynamic data within the ChatGPT app interface. * MCP supports chaining actions, allowing for complex interactions between tools and resources. * The MCP inspector is a valuable tool for debugging and understanding the data flow within ChatGPT applications. * Next.js apps integrated with ChatGPT require specific configuration for CORS and metadata to function correctly. **4. Notable Quotes or Examples** * "What this means is you could actually build out a application through MCP and host it on Verscell that will actually allow you to interact with an app on ChatgT the website." (Explains the core purpose of the tutorial.) * "We're going to do a bit of a hello world where it's going to call out to a tool, ask for a user's name, then display it on the web page." (Describes a practical example of MCP integration.) * "Basically these chat GPT apps are all running on MCP and they're returning a resource that is associated with this information that allows the chatbt to render your HTML but also use the tools to send that information Over." (Summarizes the core functionality.) * Example: The demo application is able to show a user's name on a HTML webpage based on the name the user inputs inside of the chat window. **5. Target Audience** * Web developers interested in building and deploying applications for ChatGPT. * Developers familiar with Next.js and Vercel who want to integrate their applications with the ChatGPT platform. * Individuals seeking to understand the technical aspects of ChatGPT app development and the Model Context Protocol (MCP). * Those looking for a practical guide on building interactive experiences within the ChatGPT interface. **Key Highlights:** - **Vercel Simplifies ChatGPT App Deployment**: Vercel provides tooling to easily deploy ChatGPT apps using MCP (Messaging Communication Protocol), making development more accessible. - **MCP Enables Interactive Chatbot Experiences**: ChatGPT apps use MCP to create interactive experiences. The tutorial shows how to call tools, request user info, and update webpages in real-time within the ChatGPT interface. - **Resource Templates for Dynamic HTML**: MCP leverages resource templates, in this case HTML files, allowing developers to send dynamic data to be rendered within the ChatGPT app interface. The app registers and uses a 'content widget' resource. - **Chaining Tools and Resources**: MCP supports chaining actions. A tool call can trigger a resource, or a resource can trigger another tool call, enabling complex interactions within the chatbot application. - **MCP Inspector for Debugging**: The tutorial uses the MCP inspector to show how to inspect resources and tool calls, which helps developers understand the data flow and debug their applications. --- ### Master OpenAI's NEW ChatGPT Custom MCP Integration in Minutes **Published:** 10/11/2025 **Duration:** 16 minutes **YouTube:** https://youtube.com/watch?v=Q8W-VBWc-g0 **Full LLM Text:** /video/cmis2lyko000004kwj5umdlkd/llm.txt **Description:** Use apify to create a data source: https://www.apify.com?fpr=s9bzxz 🚀 Learn how to integrate your own data sources directly into ChatGPT using OpenAI’s brand-new Model Context Protocol (MCP)! In this step-by-step tutorial, you’ll discover how to build a custom MCP server, connect it to ChatGPT’s Deep Research and Responses API, and unlock powerful search and fetch capabilities — all in just a few minutes. You’ll learn: - What the Model Context Protocol (MCP) is and why it matters - How to create your own MCP data source with search and fetch endpoints - How to connect your MCP server to ChatGPT or OpenAI’s API - How to extend Deep Research to use private or enterprise data - How to deploy a MCP on Vercel Bonus: Real examples and GitHub references to speed up your setup By the end, you’ll have a fully working custom MCP connector that allows ChatGPT to reason over your own data — just like it does with the web. 🎓 Perfect for: developers, data engineers, AI builders, and anyone exploring OpenAI’s new agentic features. 📦 Resources mentioned: - OpenAI Deep Research API Docs: https://platform.openai.com/docs/guides/deep-research - MCP Documentation: https://platform.openai.com/docs/mcp - Example GitHub: https://vercel.com/templates/next.js/openai-deep-research-compatible-mcp-with-next-js 💡 Pro tip: Combine MCP with OpenAI’s Responses API to create agents that can research, fetch, and analyze your own internal data securely. **Summary:** Here's a summary document covering the key aspects of the video: **Document Title: Master OpenAI's NEW ChatGPT Custom MCP Integration: A Summary** **1. Executive Summary:** This video tutorial explains how to create a custom Model Context Protocol (MCP) connector, which allows ChatGPT to access and utilize custom data sources for deeper research and more tailored responses. It details the process of deploying an MCP server using Vercel, connecting it to ChatGPT, and testing its functionality via the OpenAI Prompt Dashboard. **2. Main Topics Covered:** * **Introduction to MCP (Model Context Protocol):** What MCP is and its significance in integrating custom data into ChatGPT. * **MCP Architecture and Required Tools:** The limited tools available in the MCP for deep research: 'search' and 'fetch,' with their specific requirements for data handling (text, image, resource). * **Creating and Deploying a Custom MCP Server:** Instructions on creating search and fetch endpoints, emphasizing the use of the Vercel template for simplified deployment. * **Connecting MCP to ChatGPT:** Step-by-step guidance on enabling developer mode in ChatGPT settings and adding a custom connector using the MCP server's API URL. * **Testing and Refining MCP Integrations:** Leveraging OpenAI's Prompt Dashboard for testing the effectiveness of prompts with custom data sources. * **MCP Authentication:** Authorization requires using OAUTH principles. **3. Key Takeaways:** * OpenAI enables custom data source integration via MCP connectors, allowing ChatGPT to access specific, user-defined information. * MCP's for deep research are limited to 'search' and 'fetch' tools, which have specific requirements for data formats and handling. * Vercel provides a ready-made template compatible with ChatGPT's Deep Research feature, simplifying MCP server deployment. * The Prompt Dashboard offers a dedicated environment for testing and refining MCP server integrations. * Developers must enable developer mode in ChatGPT and add the custom connector with the appropriate API URL to utilize it. * MCP accepts data in text, image, and resource formats. **4. Notable Quotes or Examples:** * "OpenAI has basically made this ability to add sources...you can add custom connectors." (Illustrates the customizability of ChatGPT) * "You can only implement two tools. You can implement a search and you can implement a fetch." (Highlights the constraints of the MCP for deep research) * Demonstration of using the Vercel template to quickly deploy an MCP server and connect it to ChatGPT. * Example of using the Prompt Dashboard to test a prompt that chains both the search and fetch tools. * "Now you have the ability to make an MCP server, pull your for your own documents and share that with different people in your organization... and actually share those resources in a in a data connector way that allows you to aggregate different data from different sources." (Emphasizes the versatility of MCP). **5. Target Audience:** * Developers * Data Engineers * AI Builders * Anyone exploring OpenAI's agentic features and custom data integrations with ChatGPT. **Key Highlights:** - **Custom Data in ChatGPT via MCP Connectors**: OpenAI now allows custom data source integration via MCP connectors, enabling ChatGPT to access and utilize specific, user-defined information for deep research. - **Search & Fetch: Key Tools for Deep Research**: MCPs for deep research are limited to two core tools: 'search' and 'fetch,' each with specific requirements for data handling (text, image, resource). Authorization also requires using OAUTH principles. - **Vercel Template Simplifies MCP Deployment**: Vercel provides a pre-built template with ChatGPT Deep Researcher compatibility, simplifying the deployment of MCP servers and allowing for quick setup with minimal coding. - **Prompt Dashboard for MCP Testing**: OpenAI's Prompt Dashboard provides a dedicated environment to test and refine MCP server integrations, enabling the evaluation of prompt effectiveness with custom data sources. - **Enable Developer Mode and Connect Custom Connectors**: Developers have to enable developer mode in settings and then add the custom connector providing the relevant API URL to make use of custom created tools. --- ### Build Stunning AI Chat UIs with Vercel AI Elements + Streamdown (2025 Guide) **Published:** 10/2/2025 **Duration:** 15 minutes **YouTube:** https://youtube.com/watch?v=gHOc7FGGSOg **Full LLM Text:** /video/cmis2mc6a000104kwd19js38x/llm.txt **Description:** Join the Community: https://nas.io/vibe-coding-retreat Want to build a beautiful, streaming-first AI chat experience? In this video, I’ll show you how to use Vercel AI Elements (a React component library for AI apps) together with Streamdown (a markdown renderer built for streaming AI responses). We’ll cover: - 🚀 What AI Elements are and why they matter - 🎨 How to style and customize chat UIs with React - ⚡ Real-time markdown streaming with Streamdown - 🛠 Integrating with the Vercel AI SDK - ✅ Best practices, performance tips, and gotchas By the end, you’ll know how to build a production-ready AI chat interface that looks great, streams smoothly, and is fully customizable. Perfect for AI apps, SaaS dashboards, or dev projects in 2025. 🔗 Resources & Links: 📩 Newsletter: https://sendfox.com/nerdingio 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 💬 What’s the one social platform you’d automate first—Twitter, LinkedIn, or TikTok? 👍 Like & Subscribe for more AI + automation tutorials that save you time. **Summary:** ## Video Summary: Build Stunning AI Chat UIs with Vercel AI Elements + Streamdown (2025 Guide) **1. Executive Summary:** This video provides a practical guide to building modern AI chat interfaces using Vercel AI Elements, a React component library, and Streamdown, a markdown renderer optimized for streaming AI responses. It demonstrates how to leverage these tools to create customizable, performant, and visually appealing AI chatbots. **2. Main Topics Covered:** * **Introduction to Vercel AI Elements:** Overview of the pre-built UI components for AI chatbot development, covering elements like actions, avatars, code blocks, prompts, reasoning, and markdown display. * **Streamdown for Real-time Markdown Streaming:** Explanation of how Streamdown improves the display of markdown in React applications by streaming tokens correctly, creating a smoother user experience compared to traditional React markdown solutions. * **Integration with Vercel AI SDK:** Demonstration of how to combine Vercel AI Elements and Streamdown with the Vercel AI SDK to build a functional chatbot, including generating a PRD, outlining architecture, and providing implementation details. * **Context Management and AI Hallucinations:** Discussion of best practices for managing context in AI chatbots to maintain accuracy and performance and a warning about the security risks associated with AI hallucinating package names during development. * **Building a Chatbot Demo:** Practical example of building a simple chatbot with a persistent UI bubble in the lower-right corner of the screen, demonstrating the ease of development with Vercel AI Elements and the AI SDK, including local storage and network calls. **3. Key Takeaways:** * **Vercel AI Elements simplifies chatbot UI development:** Provides pre-packaged, customizable UI components that accelerate the creation of visually appealing and feature-rich chatbots. * **Streamdown enhances markdown rendering:** Improves the user experience by offering smoother, more fluid markdown streaming in React applications, eliminating the "popping" effect. * **Context management is crucial for AI performance:** Regularly refreshing the context of the AI chatbot can help maintain accuracy and performance over time. * **Security considerations for AI hallucination:** Developers must verify package authenticity to prevent installing malicious libraries. * **Simplified chatbot creation workflow:** The video demonstrates a streamlined process for building a chatbot using Vercel AI Elements and the AI SDK, covering design, architecture, and implementation. **4. Notable Quotes or Examples:** * "With [AI Elements], you actually have UI components that you can actually pull into your different agents and uh chat bots." - Explains the core value proposition of Vercel AI Elements. * "Streamdown...actually streams out the essentially the tokens as it was, but those elements are streamed correctly as they are styled." - Describes the benefits of Streamdown over React Markdown. * "The more context that we provide the uh the more initial value we get but as that starts to to degrade or rot then our accuracy and our performance goes down." - Highlights the importance of context management. * "AI like notoriously hallucinates all the packages and that's why we're trying to give it the implementation details of exactly what it needs to put in." - Warns of the potential security risks when AI tools suggest package names and the importance of verifying their authenticity. * The example of creating a PRD, architecture document, and implementation guide using the AI tools demonstrates the accelerated development workflow enabled by Vercel's AI ecosystem. **5. Target Audience:** * Web developers interested in building AI-powered chat interfaces. * React developers seeking to leverage pre-built UI components for AI applications. * Engineers looking to improve the streaming markdown experience in their applications. * Anyone interested in learning about the Vercel AI SDK and its capabilities. * Individuals looking for best practices and tips for building production-ready AI chatbots. **Key Highlights:** - **Vercel AI Elements: Pre-built UI for AI Chatbots**: Vercel AI Elements provide pre-packaged, customizable UI components (based on SHOD CN) for easily building AI-powered chatbots. Includes components for actions, avatars, code blocks, prompts, reasoning, and more. - **Streamdown: Improved Markdown Streaming for React**: Streamdown addresses rendering issues in React markdown by streaming tokens correctly, resulting in a more fluid and styled display of markdown content in AI chat applications. This avoids the 'popping' effect. - **Context Management is Key**: The video highlights the importance of context management in AI chatbot development, noting that excessive context can degrade accuracy and performance over time. Refreshing context by starting new chats helps maintain optimal results. - **AI Package Hallucinations: A Security Risk**: The presenter cautions about AI hallucinating package names, leading to potential security vulnerabilities. Developers should verify package authenticity to prevent installing malicious or non-existent libraries. - **Simplified Chatbot Creation**: The demo showcases how to quickly build a functional chatbot using Vercel AI SDK and Elements, covering PRD generation, architecture design, and implementation details. Includes local storage and basic network calls. --- ### Claude Code Custom Context Tooling **Published:** 9/18/2025 **Duration:** 9 minutes **YouTube:** https://youtube.com/watch?v=uS1mN8UpbkY **Full LLM Text:** /video/cmis2mntw000204kwkiabiwn9/llm.txt **Description:** Join the Community: https://nas.io/vibe-coding-retreat Unlock the hidden power of custom status lines in Claude Code to take your context engineering to the next level. In this video, we’ll walk through how the status line works, what kind of information you can display (model, token usage, costs, Git branch, project path, etc.), and how this real-time feedback helps you manage AI context like a pro. You’ll learn: - What the status line is and how to customize it - How to track token usage & avoid unexpected context drops - Ways to visualize costs, project state, and model info - Best practices for using context tooling in your workflow If you’re serious about AI coding, context management, or prompt engineering, this tutorial will give you a huge edge in keeping Claude efficient and predictable. 👉 Subscribe for more tutorials on Claude, MCP, and AI-powered coding workflows. **Summary:** ## Claude Code Custom Context Tooling: Video Summary **1. Executive Summary:** This video demonstrates how to leverage the Claude Code status line feature to enhance context awareness and improve workflow efficiency while using Claude. By customizing the status line with relevant information like Git branch, working directory, model details, and token usage, users can better manage their AI coding sessions, avoid unexpected context drops, and improve project visibility. **2. Main Topics Covered:** * **Introduction to the Claude Code Status Line:** What it is, where it appears, and how it works. * **Customization of the Status Line:** Defining what information to display (working directory, Git branch, model details, token usage, etc.) and how to format it. * **Token Usage Tracking:** Monitoring token consumption to prevent exceeding context window limits and knowing when to reset the session. * **Shell Script Integration:** Understanding how the status line uses shell scripts for advanced customization and dynamic information display. * **Practical Examples and Use Cases:** Demonstrating how the status line improves context switching, project visibility, and overall workflow in a coding environment with multiple projects/branches. * **Color Coding:** Using shell script to change the colors displayed on the status line based on particular conditions (uncommitted changes, etc.) **3. Key Takeaways:** * The Claude Code status line is a valuable tool for managing context and enhancing workflow. * Customization is key: Tailor the status line to display the information most relevant to your workflow. * Token usage tracking helps optimize context window usage and prevent unexpected behavior. * Shell scripting enables advanced customization, providing dynamic and informative displays. * The status line promotes better project visibility, especially when using Git worktrees or multiple repositories. * Color-coded status line can help you see different status changes as soon as you glance over. **4. Notable Quotes or Examples:** * "So basically, I can't really tell that this is my what project I'm in, what branch I'm on, anything like that." - Illustrates the initial problem the status line addresses. * "This is a great way for me to track when I need to reset or clear my context window so that I know that I'm approaching uh a different kind of of uh percentage." - Highlights the practical benefit of token usage tracking. * "If I'm doing things like get work tree, uh, I can have like multiple screens up and know what's going on with every single thing." - Showcases the improved project visibility in complex coding environments. * Example customization requests: "I want working directory. Get branch. Get branch color coded coded. uh in with model." and "make this branch name puke pink or fuchsia. That just seems a little hard to read for me. In yellow if there's uncommitted changes. And then also uh pipe separator model. token." * The presenter offers an example shell script for others to use as a base. **5. Target Audience:** * Developers using Claude Code for AI-powered coding. * Individuals interested in context management and prompt engineering with AI models. * Those seeking to optimize their workflow and improve project visibility when using Claude. * Users working with multiple projects, branches, or Git worktrees. **Key Highlights:** - **Context Switching Made Easy**: The Claude Code status line helps users manage multiple projects and branches simultaneously, improving workflow and reducing confusion. - **Customizable Status Information**: Users can customize the status line to display relevant information such as working directory, Git branch, model details, and token usage for better session awareness. - **Token Usage Tracking**: The status line displays token usage, enabling users to monitor their context window and reset sessions before reaching token limits. - **Shell Script Integration**: The Claude Code status line leverages shell scripts for advanced customization, allowing users to create dynamic and informative displays. - **Improved Project Visibility**: The status line provides a clear overview of the current project and branch, especially useful when using Git worktrees or working with multiple repositories. --- ### AI Social Media Scheduler: Work Smarter Not Harder **Published:** 9/3/2025 **Duration:** 8 minutes **YouTube:** https://youtube.com/watch?v=-UnfkjNrYzk **Full LLM Text:** /video/cmis2n0ie000304kwb14nqfnk/llm.txt **Description:** Join the Community: https://nas.io/vibe-coding-retreat Postiz: https://postiz.com/?ref=jd TwitterAPI IO: https://twitterapi.io?ref=nerdin Template: https://nas.io/vibe-coding-retreat/digital-files/xpga/view Tired of scrambling to post on social media every day? In this video, I’ll show you how to build an AI-powered social media scheduler that automates real-time trending content—while keeping a human in the loop for approvals. Using TwitterAPI.io, Postiz, MCP (Model Context Protocol), and n8n, we create a full pipeline that: ✅ Pulls real-time trending topics from Twitter ✅ Uses Claude via MCP to generate smart, on-brand posts ✅ Sends posts to n8n workflows for formatting & approvals ✅ Publishes automatically through Postiz once approved 🛠 What You’ll Learn: - How to fetch trending topics with TwitterAPI.io - Using MCP to connect AI models like Claude to external posting tools - Automating workflow logic in n8n (human approval step included) - Publishing across platforms with Postiz - Best practices for balancing automation + authenticity in social media 💡 Why This Matters: AI can handle the busy work of trend-hunting and drafting posts, but you remain in control. This hybrid “AI + Human” workflow saves hours while making sure your brand voice stays authentic. Perfect for indie founders, creators, and marketing teams who want to scale their reach without burnout. 🔗 Resources & Links: 📩 Newsletter: https://sendfox.com/nerdingio 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 💬 What’s the one social platform you’d automate first—Twitter, LinkedIn, or TikTok? 👍 Like & Subscribe for more AI + automation tutorials that save you time. **Summary:** ## AI-Powered Social Media Scheduler: Summary **1. Executive Summary:** This video demonstrates how to build an automated social media scheduler using AI (Claude), real-time Twitter data, and a human-in-the-loop approval process. The workflow leverages tools like TwitterAPI.io, Postiz, n8n, and Discord to streamline content creation, scheduling, and publishing while maintaining brand authenticity. **2. Main Topics Covered:** * **Workflow Overview:** Explanation of the complete process from fetching trending Twitter topics to automated posting on social media platforms. * **Tool Integration:** Detailed breakdown of how TwitterAPI.io (for trending data), Claude via MCP (for content generation), n8n (for workflow automation), Discord (for approvals), and Postiz (for scheduling and publishing) work together. * **Real-Time Data Utilization:** Leveraging TwitterAPI.io to capture real-time trending topics for relevant and timely content. * **AI Content Generation:** Using the Claude AI model to generate on-brand social media posts. * **Human-in-the-Loop Approval:** Implementing a Discord-based approval process to ensure content quality and brand consistency. * **Intelligent Scheduling with Postiz MCP:** Utilizing Postiz's Model Context Protocol (MCP) server to access advanced scheduling logic, including calendar integration and optimized posting times. * **Open-Source Advantage:** Highlighting the benefits of Postiz being open-source, enabling self-hosting and customization. * **Workflow Implementation:** A step-by-step guide on how to create and configure the automated social media scheduling workflow using the mentioned tools. **3. Key Takeaways:** * **Automation with Control:** AI can automate content creation and scheduling, but a human-in-the-loop approval process maintains brand voice and prevents inappropriate posts. * **Efficiency Gains:** The automated workflow saves significant time and effort compared to manual social media management. * **Data-Driven Scheduling:** Postiz's MCP allows for intelligent scheduling based on calendar data and pre-existing posts, optimizing engagement and preventing content overload. * **Customization and Flexibility:** Open-source tools like Postiz provide greater control and customization options compared to proprietary platforms. * **Postis MCP is a Powerful Feature:** The postis MCP server enables a lot of advanced scheduling. **4. Notable Quotes or Examples:** * "We're going to be using NAN. That's going to be our workflow...Discord so that we can use that as our approver method...And then we're going to be using MCP." (Outlines the core tools used) * "[Postiz MCP allows] going way beyond just saying post this or post this at this time. What I'm actually doing is I'm saying I want you to look at what my calendar looks like. Then I want you to decide based on the time frame, check if there's any existing uh posts because I don't want to post them too many backtoback." (Illustrates the advanced scheduling capabilities) * "If you're interested in this workflow, you can find it in the vibe coding retreat. Go ahead and click the link below." (Call to Action) * "It's saying that I've successfully posted tomorrow at 10:00 a.m. Eastern time, which is the pleasant time to tweet with positive sarcasm." **5. Target Audience:** * Indie founders * Content creators * Marketing teams * Anyone seeking to automate social media management while maintaining brand control * Individuals who are familiar with or interested in using N8N for workflow automation. **Key Highlights:** - **Automated Social Media Scheduling with Real-Time Data**: The video demonstrates a workflow that automates social media scheduling using real-time Twitter data, N8N for workflow automation, Discord for approval, and Postis for scheduling. - **Human-in-the-Loop Approval via Discord**: The system incorporates a human-in-the-loop approval process using Discord, allowing users to review and approve AI-generated content before it's published. - **Postis MCP: A Key to Advanced Scheduling**: The presenter highlights the importance of Postis' MCP server, enabling more complex scheduling logic like checking existing calendar entries and optimizing posting times based on schedule availability. - **AI-Driven Time Optimization**: The AI not only generates content but also intelligently selects optimal posting times based on schedule data, preventing back-to-back posts and maximizing audience engagement. - **Open-Source Advantage with Postis**: The presenter emphasizes Postis being open-source, enabling self-hosting and customization for more control over the social media scheduling process. --- ### Claude Connectors + n8n MCP Servers = Instant AI Automation **Published:** 8/9/2025 **Duration:** 11 minutes **YouTube:** https://youtube.com/watch?v=gp2KsWvOYyc **Full LLM Text:** /video/cmis2ncpv000404kw6924sx9k/llm.txt **Description:** Join the Community: https://nas.io/vibe-coding-retreat Want hands on training from me checkout https://www.vibecodingretreat.com/ for in person or online jam sessions. In this video, we combine Claude’s new MCP Connectors with n8n MCP servers to create instant AI-powered automations—no custom code required. With this setup, you can: ✅ Plug Claude directly into your existing n8n workflows ✅ Let Claude discover and run your MCP-exposed tools automatically ✅ Build outcome-based agent flows that complete tasks with minimal calls ✅ Trigger workflows in n8n from a simple Claude prompt 🛠 What You’ll Learn: - How Claude Connectors work with remote MCP servers - Exposing specific n8n workflows as tools for Claude - Configuring for Claude Connector integration - Testing end-to-end flows: Claude → MCP Connector → n8n workflow → Result back to Claude 💡 Why This Matters: By linking Claude directly to your n8n MCP server, you give your AI agent superpowers—access to every automation you’ve already built. From sending emails to updating CRMs or fetching live data, Claude becomes the front-end brain for your backend automations. **Summary:** ## Claude Connectors + n8n MCP Servers = Instant AI Automation - Summary **1. Executive Summary:** This video explores how to integrate Claude's new MCP Connectors with n8n MCP servers to create AI-powered automations without custom coding. The video demonstrates successful integration without authentication, highlights authentication pitfalls, and showcases a working OAuth example for leveraging tools, resources, and prompts. **2. Main Topics Covered:** * **Introduction to Claude Connectors & n8n Integration:** Setting up a basic connection between Claude Connectors and an n8n MCP server. * **Authentication Challenges:** The issues encountered when adding authentication (Bearer Token, Header Auth) to n8n MCP servers and their incompatibility with Claude Connector's expected OAuth redirect flows. * **MCP Remote Authentication limitations:** Why MCP remotes using website login for authentication don't align with Claude Connectors' authentication specifications. * **Working OAuth Example:** Using the MCP example server as a demonstration of a successful OAuth flow with Claude Connectors, using well-known endpoints. * **Leveraging MCP resources and tools:** Accessing tools, resources, and prompts from connected MCP servers within the Claude interface. **3. Key Takeaways:** * Connecting Claude Connectors to n8n MCP servers is straightforward without authentication. * Adding authentication to the n8n MCP server causes connection failures with Claude Connectors due to incompatible OAuth implementations. * Claude Connectors require a specific OAuth flow (using well-known endpoints) for successful authentication. * Using a compatible MCP server (like the example server) allows access to tools, resources, and prompts directly from Claude. * This integration allows Claude to control and interact with automations already built in n8n, granting it "superpowers." **4. Notable Quotes or Examples:** * "It's a lot simpler than you might think... but there are some pitfalls." - Highlighting the ease of basic setup vs. the complexity of authentication. * Mention of specific tools in example "Cascade, Google Docs, Lex" - Illustrating the breadth of possibilities. * "By linking Claude directly to your n8n MCP server, you give your AI agent superpowers—access to every automation you’ve already built." - Underscoring the potential and impact of the integration. **5. Target Audience:** * n8n users looking to integrate AI into their workflows. * Developers interested in using Claude as a front-end interface for their backend automations. * Individuals curious about the potential of AI-powered automation and MCP servers. * Those familiar with concepts like MCP, OAuth, and API integrations. **Key Highlights:** - **Claude Connectors & N8N Integration is Simpler Than Expected**: Connecting Claude connectors to N8N MCP servers is straightforward without authentication. The video demonstrates a basic setup using a production URL. - **Authentication Pitfalls with Claude Connectors & N8N**: Adding authentication (Bearer token, Header Auth) to the MCP server causes connection failures in Claude due to missing OAuth redirect flows. N8N's standard OAuth doesn't work. - **MCP Remote Authentication & Claude Connector Requirements**: MCP remotes proxying auth using a website login doesn't align with Claude Connectors' expected authentication specification, leading to connection errors. - **Working OAuth Example: MCP Example Server**: Using the MCP example server demonstrates a successful OAuth flow with Claude Connectors, utilizing well-known endpoints and proper authorization/authentication flows. - **Leveraging Tools, Resources & Prompts via Claude Connectors**: Connected Claude Connectors unlock access to tools, resources, and prompts from MCP servers within the Claude interface, enabling powerful AI workflows. --- ### Claude Connectors Made MCP Even Easier **Published:** 8/5/2025 **Duration:** 10 minutes **YouTube:** https://youtube.com/watch?v=mYyL4jINrek **Full LLM Text:** /video/cmis2nosr000504kwfhnpffkh/llm.txt **Description:** Join the Community: https://nas.io/vibe-coding-retreat Want hands on training from me checkout https://www.vibecodingretreat.com/ for in person or online jam sessions. In this video, discover how Claude’s new Custom Connectors dramatically simplify connecting to any MCP server—without writing a single line of client code. Whether you're using Claude Desktop, Web, Code, or API, adding remote MCP servers is now a breeze. - Learn how to access Claude’s Connectors directory to browse and install - MCP-powered tools like Notion, Slack, Asana, PayPal, and more. - Your Second Mind for a Smarter Future Setup and connect to remote MCP servers—using OAuth or API keys—and see how Claude seamlessly discovers and uses tools. Understand scope configurations (user/project/local) via .mcp.json for flexible team integration. Walk through enabling/removing connectors in Claude’s settings pane and authorizing access flows. 00:00 Introduction 01:54 Built-in Connectors 05:52 Custom MCP Connector **Summary:** Here's a structured summary document for the provided video: **Document Title: Claude Connectors Simplify MCP Integration: A Summary** **1. Executive Summary:** This video explains how Claude's new Custom Connectors feature simplifies the integration of custom MCP (My Custom Processor) servers into Claude (Desktop, Web, Code, or API) without requiring coding or direct JSON configuration. By leveraging connectors, users can easily access and utilize MCP-powered tools, resources, and prompts, greatly extending Claude's capabilities and making MCP development more accessible. **2. Main Topics Covered:** * **Introduction to Claude Connectors:** What they are and their purpose in extending Claude's capabilities. * **Built-in Connectors:** Overview of existing connectors like Google Drive, Gmail, and new additions like Stripe and Chrome. * **Custom MCP Connectors:** Demonstration of how to create and use custom connectors for personal MCP servers. * **Resource and Prompt Integration:** Highlighting the ability to integrate resources (files, images, audio) and prompts directly from a custom MCP server. * **Simplified MCP Integration:** Explaining how connectors eliminate the need for manual JSON configuration. * **Practical Examples:** Setting up a connection to a custom GitHub search tool and demonstrating how to retrieve data from a webpage using Chrome connector. * **Configuration Scopes:** Mention of scope configurations (user/project/local) via .mcp.json. * **Enabling/Removing connectors:** Review enabling/removing connectors in Claude’s settings pane and authorizing access flows. **3. Key Takeaways:** * **Simplified MCP Integration:** Claude Connectors offer a user-friendly alternative to editing JSON configurations for integrating custom MCPs. * **Extended Functionality:** Connectors unlock the ability to incorporate resources (files, images, audio) and prompts directly from a custom MCP server, enriching Claude's context and enabling complex interactions. * **Custom Authentication:** Connectors support custom authentication methods (OAuth, API keys) for secure access to MCP servers. * **Accessibility:** Connectors make MCP development and usage more accessible to a wider audience, including those without technical expertise in JSON configuration. * **HTTP MCP:** Use of HTTP MCP allows information to be pulled back into context. **4. Notable Quotes or Examples:** * "When I come in here and I save this URL, this piece, it's actually going to error." - Illustrates the problem that connectors solve. * "Now I can actually hit this URL because it's just going out and hitting the URL." - Demonstrates the core functionality of the custom connector. * "The only thing that's missing from here is that I also made resource templates, which allows you to make dynamic uh calling of resources." - Explains future MCP use cases. * Example of using Chrome connector to scrape data from a webpage (vibe coding retreat website). * Example of using a custom GP tool to search GitHub. * Example of pulling documentation for an MCP in order to create documentation context. **5. Target Audience:** * Developers building custom MCPs. * Users of Claude who want to extend its functionality. * Individuals interested in simplifying the integration of external applications and web services with Claude. * Anyone seeking a no-code/low-code solution for connecting Claude to custom data sources. * Individuals who would otherwise have to hand-edit JSON files for MCP configuration. **Key Highlights:** - **Claude Connectors: Bridging MCPs and Claude**: Claude Connectors allow integration with external applications and web services, overcoming limitations of direct URL configuration within Claude MCP settings. - **Custom Connectors: Extending Claude's Capabilities**: Users can add custom connectors for personal MCPs, enabling interaction with private services and data sources previously inaccessible. Supports custom authentication too. - **Resources and Prompts via Connectors**: Connectors unlock the ability to incorporate resources (files, images, audio) and prompts directly from a custom MCP server, enriching Claude's context and enabling complex interactions. - **Simplified MCP Integration: No More JSON Editing**: Connectors provide a user-friendly way to integrate custom MCPs without requiring users to directly edit JSON configurations, making MCP development more accessible. --- ### Use MCP to Search Millions of GitHub Repos **Published:** 7/24/2025 **Duration:** 12 minutes **YouTube:** https://youtube.com/watch?v=rEyByuxCDgQ **Full LLM Text:** /video/cmis2o13f000604kwqm3hq3p7/llm.txt **Description:** Join the Community: https://nas.io/vibe-coding-retreat Want to build your own MCP's and sell them? https://www.vibecodingretreat.com/ Unlock the power of Model Context Protocol (MCP) to search millions of GitHub repositories—right from your AI agent! In this video, we walkthrough integrating Grep’s MCP server with GitMCP and Claude Desktop, enabling semantic code search at scale. Watch how to: - Connect your AI agent to Grep’s MCP server for full-text search across 1M+ repos - Spin up Grep to expose any GitHub repo as an MCP endpoint - Perform code searches, pattern analysis, and insights using Cursor or Claude Code 💬 Which repos would you search at scale? Share below! 👍 Like & Subscribe for more AI + developer automation tutorials! **Summary:** ## Video Summary: Use MCP to Search Millions of GitHub Repos **1. Executive Summary:** This video demonstrates how to integrate the Grep MCP (Model Context Protocol) server with AI agents like Claude Code and Cursor to search millions of GitHub repositories, enabling enhanced code search and context engineering for LLMs. By leveraging Grep's MCP capabilities, developers can improve the quality and relevance of AI-generated code and documentation through comprehensive contextual information. **2. Main Topics Covered:** * **Introduction to Grep MCP (GP MCP):** What it is and its purpose (searching millions of GitHub repos). * **Context Engineering with MCP:** How GP MCP provides additional context to LLMs, improving response quality. * **Integrating GP MCP with IDEs:** Walkthrough of integrating with Cursor and Claude Code (using MCP Add). * **Demonstration of Code Search:** Using GP MCP to find information on "ant design and view.js best practices" within a project in Cursor. * **GP App Web UI:** Overview of the web interface for searching GitHub repositories using Grep, including filtering by language. * **Prompt Engineering with GP MCP:** How the information gathered can be used to refine prompts and improve LLM output for tasks like PRD building. **3. Key Takeaways:** * GP MCP allows for efficient full-text search across a vast number of GitHub repositories. * Integrating GP MCP into IDEs like Cursor and Claude Code enables direct searching within the development environment, improving workflow and efficiency. * Context engineering using GP MCP improves the accuracy and relevance of LLM responses, making them more valuable for code integration and documentation tasks. * The GP app provides a user-friendly web interface for exploring code examples and repositories based on keywords. * The information retrieved with GP MCP can be incorporated into prompt engineering strategies to enhance LLM output quality for various software development tasks. **4. Notable Quotes or Examples:** * "So, Grep is basically search, but the fact that I can then go out and take that information uh and pull that context back into whatever I'm doing, right? So, this is another example of context engineering." * (Example code search) "use the graphp [MCP], find more information on ant design and view.js best practices." * "Not only can we use this in our coding, but we can actually use this in our prompting or our context engineering so that we can actually bring in more information into how we can actually uh improve the results that we're getting from the LLM." **5. Target Audience:** * Software developers seeking to improve code search and discovery. * AI engineers and developers interested in context engineering for LLMs. * Individuals interested in integrating AI agents into their development workflow. * Anyone seeking tools to enhance code quality and accelerate software development. **Key Highlights:** - **GP MCP: Search Millions of GitHub Repos**: Leverage GP MCP to search millions of GitHub repositories for research, code integration, and context engineering for LLMs. - **Context Engineering with MCP**: Using GP MCP provides additional context to LLMs, improving the quality and relevance of the generated responses. Useful for PRD building. - **Integrating GP MCP with IDEs**: GP MCP can be integrated into IDEs like Cursor and Claude Code, allowing for direct searching of GitHub repos within the development environment. - **GP App: Web UI for GitHub Search**: GP offers a web UI for searching GitHub, allowing you to filter by language and explore code examples across repositories. - **Improved Results via Prompt Engineering**: The information obtained via GP MCP can be incorporated into prompting strategies to guide the direction of an LLM and increase the quality of outputs. --- ### Make 3k SEO Pages from 6 Templates with Claude Code and MCP **Published:** 7/11/2025 **Duration:** 20 minutes **YouTube:** https://youtube.com/watch?v=iR6o19zvZ7o **Full LLM Text:** /video/cmis2oed6000704kwhnp7rfiq/llm.txt **Description:** Join the Community: https://nas.io/vibe-coding-retreat Achieve massive organic traffic with programmatic SEO and #mcp ! In this tutorial, we'll show you how to generate 3,000+ unique landing pages from just six templates using Claude Code and structured data—ideal for directories, locales, or product catalogs. 🛠 What You’ll Learn - What programmatic SEO is and why it's essential for scaling long-tail keywords - How to design six adaptable templates for dynamic page generation - Using Claude Code to fill those templates at scale - Integrating data sources—API, MCP, or database—for rich, contextual content - Best practices to avoid thin content and maintain SEO quality - Tips to monitor and optimize generated pages over time 💡 Why It Works Programmatic SEO lets you automate thousands of targeted landing pages, capturing niche, high-intent search traffic without manual effort . With Claude Code, you bring AI-driven content generation into the process, boosting efficiency and content quality. **Summary:** ## Video Summary: Make 3k SEO Pages from 6 Templates with Claude Code and MCP **1. Executive Summary:** This video demonstrates how to leverage Claude Code, MCPs (Model Configuration Presets), and programmatic SEO techniques to generate thousands of unique landing pages from a small set of templates. The process involves automating keyword expansion, data enrichment, and content creation to capture niche, high-intent search traffic with minimal manual effort. **2. Main Topics Covered:** * **Programmatic SEO Fundamentals:** Explanation of the concept and its importance for scaling long-tail keywords. * **Template Design:** The role of adaptable templates in dynamic page generation. * **Claude Code Integration:** Utilizing Claude Code to autonomously generate content at scale. * **MCPs (Model Configuration Presets):** Customizing AI behavior for SEO strategy through MCPs. * **Data Source Integration:** Connecting to APIs (Perplexity, Firecrawl, Data SEO) for richer content. * **Keyword Expansion:** Demonstrates using Claude Code to autonomously expand keywords and enrich data for programmatic SEO * **Debugging and Problem Solving:** Addressing API key issues and adjusting AI workflows. * **Iterative Refinement:** The importance of manual review and fine-tuning of AI-generated content. * **Bottom-Up Longtail Funnel:** Generating pages from the "bottom" by identifying long tail keywords **3. Key Takeaways:** * Programmatic SEO can significantly expand organic reach by automating the creation of targeted landing pages. * Claude Code can automate keyword expansion, content generation, and data enrichment for SEO. * MCPs (Model Configuration Presets) allow for customized AI behavior tailored to specific SEO strategies, enhancing efficiency and control. * API integration with tools like Firecrawl and Perplexity can provide valuable data for content creation. * The process requires iterative refinement and manual review to ensure content quality and alignment with SEO goals. * A small number of templates can be scaled to create thousands of pages, allowing for massive visibility. * Generating from the bottom-up (long tail keywords) can create more pages than traditional methods. **4. Notable Quotes or Examples:** * "What that means is you have these URLs and they are like dynamically getting generated and we do this and the outcome is this and what we do is we have a call to action and then basically some kind of sales pitchy landing pages that will connect on the Google SEO algorithm." - Explanation of programmatic SEO implementation. * "Using different types of MCPS, we could actually do a keyword analysis and actually expand the data to a much higher range." - Highlighting the power of MCPs in scaling SEO efforts. * "From five or six base pages, we then created 2,000 pages. That's just that's that's wild." - Demonstrating the scalable outcome from the process. * Leveraging custom MCPs (presumably Model Configuration Presets) within Claude, including one inspired by the "boring marketer" to guide the AI's SEO strategy and coding process. **5. Target Audience:** * SEO professionals looking to automate and scale their content creation efforts. * Marketing professionals interested in leveraging AI for programmatic SEO. * Developers seeking to integrate AI-powered tools into their SEO workflows. * Entrepreneurs and business owners aiming to improve their online visibility and organic traffic. * Anyone interested in the intersection of AI, SEO, and content creation. **Key Highlights:** - **Autonomous Keyword Expansion with Claude Code**: The video demonstrates using Claude Code to autonomously expand keywords and enrich data for programmatic SEO, building upon a pre-existing system. The goal is to generate dynamic URLs that connect with the Google SEO algorithm. - **Leveraging Custom MCPS for SEO**: The presenter uses custom MCPs (presumably Model Configuration Presets) within Claude, including one inspired by the "boring marketer" to guide the AI's SEO strategy and coding process. These MCPs grant access to various tools and API keys. - **Overcoming Challenges with API Keys and Tool Integration**: The process involves debugging API key issues, specifically with perplexity, and adjusting the AI's workflow to effectively utilize tools like Firecrawl for data scraping and content generation, showing a real-world development process. - **From 6 Templates to 2,000 Pages**: The key achievement is expanding from a small set of templates (5 or 6) to generating approximately 2,000 SEO pages through a bottom-up longtail funnel approach, significantly increasing the potential search visibility. - **Iterative Refinement of AI-Generated Content**: The process isn't fully automated; it requires manual review and fine-tuning of the AI-generated keywords, content, and structure to ensure alignment with the overall SEO strategy and desired outcomes. --- ### Unlock Vibe Marketing with Programmatic SEO **Published:** 7/8/2025 **Duration:** 24 minutes **YouTube:** https://youtube.com/watch?v=3hb7azeeFqM **Full LLM Text:** /video/cmis2orn9000804kwevy7hfum/llm.txt **Description:** Join the Community: https://nas.io/vibe-coding-retreat Want to capture long-tail organic traffic without spending hours on manual pages? In this video, I’ll show you how to build a programmatic SEO engine using Cursor and Claude—great for directories, niches, and dynamic sites. You’ll learn how to: - Use Cursor AI to generate scalable SEO page templates - Automate keyword research and content generation with Claude - Scrape and integrate data (e.g., Google Maps, location info) for max relevance - Build, deploy, and optimize thousands of pages with low effort **Summary:** Here's a comprehensive summary document for the video, designed for quick understanding: **Video Title: Unlock Vibe Marketing with Programmatic SEO** **1. Executive Summary:** This video explains how to build a programmatic SEO system using Next.js, Cursor AI, and other tools to generate numerous optimized pages from a single template. The presenter demonstrates how to dynamically create routes, gather keyword data, and customize content to capture long-tail organic traffic without extensive manual page creation. **2. Main Topics Covered:** * **Programmatic SEO Fundamentals:** Introduction to programmatic SEO and the concept of creating dynamic routes based on parameters like city, skill, and audience. * **AI-Powered SEO Tools:** Using tools like Cursor AI, Data for SEO, Firecrawl, and Perplexity to automate keyword research, content generation, and data scraping. * **Dynamic Route Creation with Next.js:** Building dynamic routes in Next.js to generate URLs based on variable parameters, enabling tailored content display. * **Templated Page Generation:** Creating a single page template and injecting dynamic data to generate hundreds of unique, SEO-optimized pages. * **Customizable Calls-to-Action (CTAs):** Demonstrating how to tailor CTAs on dynamically generated pages based on the specific content and target audience. * **Sitemap Generation:** Dynamically creating a sitemap with numerous URLs to ensure Google can index the programmatically generated pages for improved search visibility. * **Page Customization Example:** Walkthrough of modifying the UI of generated pages using the AI tool. **3. Key Takeaways:** * Programmatic SEO allows you to generate numerous SEO-optimized pages with minimal manual effort by using dynamic routing and data injection. * AI tools can significantly streamline keyword research, content creation, and data gathering for programmatic SEO. * Next.js is an effective framework for building programmatic SEO systems due to its dynamic routing capabilities. * Customizing CTAs on dynamically generated pages can improve user engagement and conversion rates. * Generating a comprehensive sitemap is crucial for ensuring Google indexes all the pages created through programmatic SEO. * You can customize individual elements of your output by prompting the AI tool to change parameters like padding, colors, etc. **4. Notable Quotes or Examples:** * "What a programmatic SEO system is… is basically creating dynamic routes in your system to allow you to rank up based on the fact that you're saying a particular city or you're saying a particular skill or audience or whatever." * Example of using `n-integrate/postgresql` as an example of good integration page SEO * Demonstration of creating URLs such as `vibe-coding/Detroit/AI app development/product managers/beginner` to target specific niches. * "One page is generating every single one of these vibe coded page page views." (referring to the efficiency of the templated system) **5. Target Audience:** * Developers interested in SEO automation * Marketers seeking to scale content creation * Entrepreneurs and startup founders looking for efficient SEO strategies * Anyone building directories, niche websites, or dynamic content platforms **Key Highlights:** - **Programmatic SEO with Dynamic Routes**: The video explains how to build a programmatic SEO system by creating dynamic routes in Next.js, allowing for targeted content based on various parameters like city, skill, and audience. - **Leveraging AI Tools for SEO**: The speaker demonstrates using tools like Data for SEO, Firecrawl, and Perplexity to gather keyword data and generate content for programmatic SEO, streamlining the process of page creation. - **Templated Page Generation for Scalability**: The video highlights the ability to create a single page template and dynamically inject data to generate hundreds of unique pages, enhancing SEO relevance and indexing potential. - **Customizable CTAs for Specific Pages**: Demonstrates how to customize calls-to-action (CTAs) on dynamically generated pages, allowing for tailored user experiences and improved conversion rates based on the specific content and target audience. - **Generating a Sitemap for Indexing**: By dynamically creating a sitemap with a multitude of URLs, the presenter shows the importance of allowing Google to index hundreds of pages generated programmatically for improved search visibility. --- ### 15+ Tips to Make You a Vibe Coding Pro **Published:** 6/30/2025 **Duration:** 35 minutes **YouTube:** https://youtube.com/watch?v=ax99YQ-aXKk **Full LLM Text:** /video/cmis2p5zp000904kwpbjn99fm/llm.txt **Description:** Join the Community: https://nas.io/vibe-coding-retreat Vibe coding—coined by Andrej Karpathy—is the art of guiding AI to build code from natural language, letting you focus on vision and flow instead of boilerplate. In this video, I’ll walk you through 15+ practical tips and best practices with #v0 #claude and #n8n work to so you can code faster, cleaner, and with more rhythm 💥 💡 Some of What You’ll Learn: - Core Concepts - Social Share Image (OpenGraph) - SEO (Robots, Sitemap, Schema) - 404 Page - Github Sync - Local Dev w/ Cursor - Redis IP Limiting - n8n Backend 💬 Did I miss your secret vibe coding trick? Share it below & let’s build the ultimate AI flow together! 👍 Like & Subscribe for more AI-powered development hacks. **Summary:** ## Vibe Coding Pro: Summary Document **1. Executive Summary:** This video provides 15+ practical tips and best practices for "vibe coding" (AI-assisted code generation from natural language) using tools like V0, Claude, and N8N. It focuses on leveraging V0 for rapid Next.js development, integrating with backend automation, and implementing best practices for SEO, security, and collaborative workflows. **2. Main Topics Covered:** * **Introduction to Vibe Coding and V0:** Explanation of vibe coding and why V0 (with its Next.js integration and Versel backing) is a preferred tool. Comparison to other options like Bolt and Lovable. * **V0 Interface & Basic Editing:** Exploring the design and code editing tabs, allowing both visual and code-based manipulation. * **Image Handling:** Strategies for incorporating images (using external hosting like S3). * **Dynamic OG Images & SEO Generation:** Generating dynamic Open Graph images and SEO elements (sitemap, robots.txt, 404 pages) using V0. * **Version Control & Collaboration:** Using revision history, sharing versions, and GitHub integration for seamless syncing between local development and the platform. * **Local Development:** Setting up local development with tools like Cursor. * **Custom Functionality & Backend Integration:** Implementing custom features like voting systems using Redis for IP limiting, and connecting to backend automation tools like N8N for complex workflows (signup processing, email sequences). * **Stripe Integration:** Using environment variables to connect to Stripe and automatically apply discount codes. * **Workflow Automation with N8N:** Generating automated workflows with N8N to handle data inputs and improve customer engagement. **3. Key Takeaways:** * V0's tight integration with Next.js provides speed and ease of use for rapid development. * The dual design/code interface makes V0 accessible to both designers and developers. * V0 can automate the generation of SEO elements and dynamic social sharing images. * GitHub integration facilitates collaboration and version control. * Backend integration with tools like Redis and N8N extends V0's functionality and automates workflows. * IP limiting and careful SEO management are important when working with AI-generated code. **4. Notable Quotes or Examples:** * "So, the OG image is it is creating this image response on this route... This is getting generated on the fly dynamically." (Demonstrating dynamic OG image generation) * "You can connect V0 to to N8N for an automation... I'm taking this lead magnet and I want to do an automation after the fact." (Explaining backend integration for automation) * "You can actually put in a rate limit based on the vote and I look at the user's IP... a security practice of looking at a user's IP to make sure that uh they're not doing too much..." (Illustrating security measures) * The video provides an example of pasting the URL of a competitor's site to get inspiration for the design. * Provides the example of using Clause AI to come up with a potential user flow. **5. Target Audience:** * Web developers interested in using AI for code generation. * Designers and product managers seeking rapid prototyping tools. * Individuals looking to streamline their Next.js development workflow. * Anyone interested in automating web development processes and backend integrations. **Key Highlights:** - **V0: Next.js Integration & Speed**: V0 is preferred due to its tight integration with Next.js, optimized performance, and up-to-date framework support, streamlining the development process. Verscell backing ensures ease of use. - **Visual & Code Editing Flexibility**: V0 offers flexibility by allowing users to edit visually (design tab) or directly through code. This caters to both non-coders and experienced developers for rapid prototyping and customization. - **Dynamic OG Images & SEO Generation**: Leverage V0's capabilities to generate dynamic Open Graph (OG) images and SEO elements (sitemaps, robots.txt, 404 pages) directly from the chat interface, improving social sharing and search engine visibility. - **GitHub Sync & Collaborative Workflow**: V0 integrates with GitHub, enabling seamless syncing of changes between local development and the platform. This facilitates collaboration between designers, developers, and product managers. - **Custom Functionality & Backend Automation**: Extend V0's functionality by integrating with databases (like Redis) for features like voting systems and connecting to backend automation tools (like N8N) for complex workflows, such as signup processing and email sequences. --- ### Build a MCP Server of Your Favorite YouTuber with Claude + Cursor **Published:** 6/19/2025 **Duration:** 9 minutes **YouTube:** https://youtube.com/watch?v=PG5RSX3jXP0 **Full LLM Text:** /video/cmis2phfk000a04kwxu0ls0p4/llm.txt **Description:** Join the Community: https://nas.io/vibe-coding-retreat In this video, we show how to create your own MCP server that mimics your favorite YouTuber—powered by Claude and developed in Cursor using the Model Context Protocol (MCP). This setup lets you spin up an AI persona that: 🎙️ Talks like your favorite creator 📺 Responds with insights based on past content 🧠 Leverages Claude’s reasoning to act like them ⚙️ Is fully accessible by any MCP-compatible client 🛠️ What You’ll Learn: How to model an AI agent after a specific YouTuber’s tone, style, and knowledge - Using Claude to power agent responses - Setting up a local MCP server with custom tools - Building in Cursor IDE for faster dev and testing - Creating prompt templates and memory using transcripts or video metadata 💡 Use Cases: - AI assistants that mirror thought leaders - Fan-powered Q&A bots - Training agents on niche content for edutainment or support - Prototype AI-driven creator collabs 🔗 Resources & Links: 🧠 Cursor IDE: https://cursor.so 🤖 Claude: https://claude.ai 📩 Newsletter: https://sendfox.com/nerdingio 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 💬 Which creator would you turn into an AI agent? 👍 Like & Subscribe to see more weird + powerful AI experiments! **Summary:** ## Video Summary: Build a MCP Server of Your Favorite YouTuber with Claude + Cursor **1. Executive Summary:** This video demonstrates how to build a personalized AI assistant that mimics a specific YouTuber by creating a vector store from their video transcripts and leveraging Claude's AI capabilities via an MCP server. The system allows users to interact with the AI persona in coding environments like Cursor IDE to gain insights, generate workflows, and automate tasks based on the YouTuber's content. **2. Main Topics Covered:** * **Automated YouTube Vector Store Creation:** Process for extracting transcripts from YouTube videos and creating vector embeddings for conversational AI. * **MCP Server Setup:** Building and configuring an MCP server to access and query the created vector store, enabling personalized knowledge access from your favorite YouTubers. * **Workflow Generation:** Translating insights from YouTube video transcripts into executable workflows using tools like N8N. * **Integration with Claude and Cursor IDE:** Utilizing the MCP server and vector store within coding environments like Claude and Cursor for AI-assisted workflows. * **Automation with Cron Jobs:** Implementing cron jobs to automate the daily process of checking for new videos, extracting transcripts, and updating the vector store. * **Custom Tool Building:** Using a custom tool to build an ai agent based on a content creator's tone, style, and knowledge. **3. Key Takeaways:** * You can create a personalized AI assistant by leveraging a YouTuber's content using vector embeddings and Claude AI. * An MCP server allows access to the vector store, enabling querying and summarization of video content. * The system can generate workflows from video content and integrate with coding environments like Cursor and Claude. * Automated cron jobs ensure the vector store is continuously updated with new content. * The process can be replicated for other YouTubers or content creators. **4. Notable Quotes or Examples:** * "I'm going to use a MCP server that I made, which will actually leverage uh every video that comes out from him, turn it into a vector store, and then allow me to chat with it." - JD * The video demonstrates an example where the AI agent is asked to use the "Boring Marketer" MCP server to break down video workflows and then create them in N8N. * The example showcased generating N8N workflows for "ad prediction," demonstrating the practical application of the generated knowledge. **5. Target Audience:** * Software developers * AI enthusiasts * Individuals interested in automating workflows * Content creators seeking to build AI assistants * Fans of specific YouTubers or thought leaders * People looking to integrate AI with their coding environment **Key Highlights:** - **Automated YouTube Vector Store Creation**: The video demonstrates building an automated system that pulls transcripts from a YouTuber's videos, creates vector embeddings, and stores them for conversational interaction. - **MCP Server for Personalized Knowledge Access**: The presenter builds a MCP server to access information from favorite YouTubers, allowing querying and summarization of video content for practical application. - **Workflow Generation from YouTube Content**: The system translates insights from the YouTube video transcripts into executable N8N workflows, automating complex tasks described in the content. - **Integrating with Claude and Cursor**: The video showcases how the created MCP server and vector store can be accessed and utilized within coding environments like Cursor and Claude, enabling AI-assisted workflows. - **Cron Job for Daily Content Embedding**: A cron job is used to automate the process of checking for new videos daily, extracting transcripts, and updating the vector store for continuous learning. --- ### Chrome’s New AI Features Let You Build Multimodal Apps (No Backend!) **Published:** 6/18/2025 **Duration:** 21 minutes **YouTube:** https://youtube.com/watch?v=_THRY0Gyksg **Full LLM Text:** /video/cmis2pvuj000b04kw1p11hr8r/llm.txt **Description:** Join me for an in person Vibe Coding Retreat https://www.vibecodingretreat.com/ Google Chrome just got a serious AI upgrade—and we’re putting it to the test by building a multimodal AI-powered game using only browser-native tools. You’ll see how to combine: 🎤 Microphone input for voice interactions 🖼️ Image recognition from webcam, canvas or uploads 🎮 Simple JavaScript game logic 🧠 Local AI to drive real-time decisions Perfect for building privacy-first games, offline LLM apps, and creative in-browser experiences using tools like MediaPipe, WebGPU, and experimental multimodal APIs. 🔗 Useful Links: 📘 Chrome AI Dev Docs: https://developer.chrome.com/docs/extensions/ai/prompt-api 🎮 Source Code: https://github.com/nerding-io/emoji-webai 📩 Newsletter: https://sendfox.com/nerdingio 💬 Want to build games with image or voice control? Let me know in the comments! 👍 Like & Subscribe for more cutting-edge AI + WebDev projects. **Summary:** ## Chrome's New AI Features: Build Multimodal Apps (No Backend!) - Summary Document **1. Executive Summary:** This video explores Chrome's new AI capabilities, specifically focusing on the multimodal API which allows developers to analyze images and audio directly within the browser using Gemini Nano. It demonstrates building an emoji-based game entirely on the client-side, showcasing how to leverage microphone input, image recognition, and JavaScript logic for AI-powered experiences without backend dependencies. **2. Main Topics Covered:** * **Introduction to Chrome's Native AI:** Overview of Chrome's exposed JavaScript APIs for accessing Gemini Nano for AI features. * **Multimodal Input Capabilities:** Utilizing the prompt API to handle images (from various sources like webcam and canvas) and audio inputs for LLM processing. * **Prompt API Features:** Discussion of key features like system prompts for model initialization, n-shot prompts for complex conversations, user customization, tool function emulation, and multimodal input handling. * **Building a Multimodal Emoji Game:** Walkthrough of a practical example showcasing image/audio analysis and translation into emojis, encoded in a shareable URL. * **Implementation Details:** Code snippets and explanations on capturing images/audio, pre-processing data, initializing the AI model, constructing prompts, and generating output. * **Setting up the Chrome Environment:** Required Chrome Canary version, flags to enable (experimental web platform, prompt APIs for Nano and multimodal support). **3. Key Takeaways:** * Chrome now offers powerful, browser-native AI capabilities with Gemini Nano, accessible via JavaScript APIs, enabling building AI apps without a backend. * The multimodal prompt API allows developers to analyze images and audio directly within the browser, opening up opportunities for creative and privacy-focused applications. * The API offers advanced features like system prompts, multi-turn conversations, and the ability to emulate tool function calls. * Shareable AI-powered experiences can be easily created by encoding data within URLs. * This technology allows building of offline LLM apps and creative in-browser experiences. **4. Notable Quotes or Examples:** * **"We're going to look at how you can actually leverage AI in Chrome, the browser, and actually use something called multimodal. What that means is that we can actually analyze and run AI on images and audio text."** - Introduces the core concept. * **Example of using image input:** User takes a photo using the webcam, and the AI identifies it as a "person in a beanie". * **Example of using audio input:** User records voice saying "the quick brown fox jumps over the lazy dog", and the AI transcribes it to "a fox jumps over a dog" and converts into relevant emojis. * **Explanation of sharable links:** "The way that this is saving the information is we're all using like base 64 encode." **5. Target Audience:** * Web developers interested in exploring and integrating AI features into their web applications. * Developers looking to build privacy-first applications that leverage AI without relying on external servers. * Developers interested in creating multimodal experiences that utilize image and audio inputs. * JavaScript developers looking for hands-on examples of how to use Chrome's new AI APIs. **Key Highlights:** - **Chrome's Native AI: Gemini Nano in the Browser**: Chrome is exposing JavaScript APIs to leverage Gemini Nano directly in the browser, enabling AI features without backend dependencies. This includes summarization, language detection and translation. - **Multimodal Input: Images and Audio to LLMs**: The prompt API supports multimodal inputs like images (blobs, canvas, video frames) and audio (blobs, audio buffers, media streams), allowing developers to analyze diverse data types directly in Chrome. - **Prompt API: System Prompts & Multi-Turn Conversations**: The Prompt API offers features like system prompts for model initialization, multi-turn (n-shot) prompts for complex conversations, and user-specific customization within sessions. - **Tool Function Emulation for AI-Powered Actions**: The API supports emulating tool function calling, allowing developers to rewrite prompts based on function execution results, extending the model's capabilities and interaction possibilities. - **Sharable AI-Powered Experiences via Base64 Encoding**: The video demonstrates building a sharable emoji game that encodes data in base64 within a URL, allowing easy distribution of AI-driven experiences. --- ### Stream AI Responses with MCP HTTP in React & #n8n **Published:** 6/10/2025 **Duration:** 9 minutes **YouTube:** https://youtube.com/watch?v=nUYiA0CqKhM **Full LLM Text:** /video/cmis2q8b8000c04kwu8l2g1yr/llm.txt **Description:** Join the Community: https://nas.io/vibe-coding-retreat Get 50% off from http://supermachine.ai/ for 1-click install MCP servers (NERDINGIO) 📩 Newsletter: https://sendfox.com/nerdingio In this video, we explore how to use the MCP HTTP Streamable spec to enable real-time AI communication in both n8n workflows and React apps—without needing WebSockets or SSE. 🔁 HTTP Streamable is a powerful transport option in the Model Context Protocol (MCP) that works over simple POST requests with chunked responses, making it: ✅ Easier to implement ✅ Compatible with most web hosts ✅ Ideal for serverless and frontend environments 🛠 What You’ll Learn: - How HTTP Streamable works in the MCP protocol - Building streamable tools in n8n using built-in HTTP and function nodes - Connecting React frontends to MCP streamable agents - When to use HTTP Streamable vs SSE 💬 Comment below: Are you building streamable tools or sticking with SSE? 👍 Like & Subscribe for more AI workflow tips! **Summary:** ## Video Summary: Stream AI Responses with MCP HTTP in React & #n8n **1. Executive Summary:** This video tutorial demonstrates how to implement real-time AI communication using MCP (Model Context Protocol) HTTP streamable connections within both n8n workflows and React applications. It highlights the advantages of HTTP streamable over SSE, showcasing its ease of implementation, compatibility with most web hosts, and suitability for serverless and frontend environments. **2. Main Topics Covered:** * **Introduction to MCP HTTP Streamable:** Explanation of MCP and the HTTP Streamable transport option, emphasizing its benefits (statelessness, NATS compatibility). * **Backend Implementation (Node.js):** Demonstrates updating an existing SSE-based server to support MCP HTTP streamable endpoints using a custom handler to process POST requests, manage session IDs, and handle requests. * **Frontend Implementation (React):** Building an interactive React-based client for debugging and visualizing MCP interactions, allowing users to test tool invocations and inspect session details in a console-like interface. * **n8n Integration:** Showcasing the use of MCP HTTP streamable APIs within n8n workflows, using both a community node and potentially the native MCP client for listing tools and executing searches. Importance of community contributions is also mentioned. * **Backward Compatibility:** How to maintain compatibility with existing SSE resources while transitioning to streamable HTTP. **3. Key Takeaways:** * **HTTP Streamable Advantages:** MCP HTTP streamable offers a powerful and simple alternative to WebSockets and SSE for real-time AI communication, providing statelessness and broad compatibility. * **React Interactive Client:** Building a custom interactive client enables efficient debugging and visualization of MCP interactions on the frontend. * **n8n Integration Flexibility:** MCP HTTP streamable can be integrated into n8n workflows using community nodes or potentially the native client (depending on specific implementation needs). * **Community Contributions are Valued:** The video highlights the significant role of community contributions in expanding the capabilities of tools like n8n. **4. Notable Quotes or Examples:** * *"So what we're going to do is we're going to take a look at this code and basically what we need to go into is our server. So, as you can see right here, we still have our SSE, our old uh transport there, and we have the ability to pull in messages, but now we want to expand this so that we can still use these same tools, but actually use our MCP streamable."* - Illustrates the transition from SSE to streamable HTTP. * Example: Showing how to list available tools and then executing a search tool via the interactive React client to showcase real time API interaction * Shout out to Community: Highlights a contributor implementing HTTP streamable support for n8n **5. Target Audience:** * Software developers working with AI models and real-time communication. * Developers using React for frontend development and n8n for workflow automation. * Individuals interested in Model Context Protocol (MCP) and its implementations. * Those seeking alternatives to WebSockets and SSE for streaming AI responses. **Key Highlights:** - **MCP HTTP Streamable Endpoints**: The video demonstrates implementing MCP (Message Channel Protocol) using HTTP streamable connections, offering advantages like statelessness and compatibility with NATS. - **Backward Compatibility with SSE**: The video shows how to transition from the deprecated SSE (Server-Sent Events) to streamable HTTP while maintaining compatibility with existing resources and tools. - **Interactive Client for MCP Debugging**: An interactive React-based client is built to visualize and debug MCP interactions, providing a console-like interface for testing tool invocations and observing session details. - **N8N Integration with HTTP Streamable**: The video explains how to utilize MCP HTTP streamable APIs within N8N using both a community node and the native MCP client, showcasing workflows like listing tools and executing searches. - **Community Contributions Highlighted**: The presenter emphasizes the importance of community contributions to N8N, specifically acknowledging a contributor for implementing HTTP streamable support. --- ### Run Your Own AI Agent Server on Vercel with MCP **Published:** 6/5/2025 **Duration:** 7 minutes **YouTube:** https://youtube.com/watch?v=rUGvrzX0E6o **Full LLM Text:** /video/cmis2qkoz000d04kwbdq7p9tf/llm.txt **Description:** Join the Community: https://nas.io/vibe-coding-retreat Get 50% off from http://supermachine.ai/ for 1-click install MCP servers (NERDINGIO) 📩 Newsletter: https://sendfox.com/nerdingio Want to host your own AI agent server—fully MCP-compatible—with just a few commands? In this guide, we deploy a Model Context Protocol (MCP) server to Vercel, giving you a production-ready backend for Claude, LangGraph, or any other agent client. This setup enables: 🧠 Fast agent deployment 🌐 Secure, public endpoints 📨 Live prompt streaming with SSE 🔌 Integration with any MCP client (Claude, Cursor, etc.) 💡 Ideal For: - AI developer tools - Custom LangGraph flows - Claude Desktop support - Serverless agent experiments 🔗 Links: 📘 Vercel Release: https://vercel.com/changelog/mcp-server-support-on-vercel 📘 Vercel MCP Docs: https://ai-sdk.dev/cookbook/node/mcp-tools#mcp-tools 🚀 Deploy Template: https://github.com/vercel-labs/mcp-on-vercel/tree/main 📬 Newsletter: https://sendfox.com/nerdingio 💬 Ready to host your own agent? Let me know in the comments 👇 👍 Like & Subscribe for more AI agent tutorials! **Summary:** ## Video Summary: Run Your Own AI Agent Server on Vercel with MCP **1. Executive Summary:** This video tutorial demonstrates how to deploy a Model Context Protocol (MCP) server to Vercel, enabling a production-ready backend for various AI agent clients like Claude or LangGraph. By leveraging Vercel's stateless MCP support and experimental AI SDK client, users can create and integrate custom tools, connect them to a front-end application, and execute arguments on a serverless backend. **2. Main Topics Covered:** * **Introduction to Vercel's MCP Support:** Explanation of Vercel's support for stateless MCP via an HTTP adapter using SSE and HTTP streamable transports. * **Vercel AI SDK Experimental MCP Client:** Overview of the experimental client featuring standard in/standard out for argument execution on a serverless backend. * **Creating and Integrating Custom Tools:** Demonstrating how to build custom tools (e.g., adding numbers, echoing messages, getting server time) within a Vercel application using MCP transport. * **Connecting Front-End to MCP Server:** Showing how to connect a front-end application to the deployed MCP server on Vercel, invoking tools, and displaying results in real-time. * **Future Integrations:** Suggestion to integrate the MCP setup with OpenAI for more complex AI agent interactions in the future. **3. Key Takeaways:** * Vercel now supports MCP through a stateless HTTP adapter, enabling SSE and HTTP streamable transports for AI agent servers. * Vercel's AI SDK includes an experimental MCP client with standard in/standard out capabilities, allowing for argument execution on a serverless backend. * Users can create custom tools and integrate them into a Vercel application using MCP transport. * Connecting a front-end to the MCP server allows real-time interaction with tools. * Future development could involve integrating this setup with OpenAI. **4. Notable Quotes or Examples:** * "Vercel is actually supporting MCP... it is Versel is actually supporting MCP. And I found this pretty interesting since basically, uh, MCP is stateless." * Example tools demonstrated: "a way to add you can specifically go through Reddus uh in order to maintain state and do your additions as well as echoing um and get the server time." * Showing front-end integration: "We need to make sure that we have our message that's going to be our key and we can actually just put in message hello from front end and we expect to see the call uh explicitly getting executed which it is." * Future direction: "What we can do from here is we could actually add our open AI and go ahead and make it deterministic to see uh when we're actually asking a question rather than actually sending the payload, could we actually um call out to OpenAI?" **5. Target Audience:** * AI developers * Individuals interested in deploying AI agents on Vercel * Developers working with LangGraph, Claude, or other MCP-compatible AI tools * Those looking for serverless backend solutions for AI applications **Key Highlights:** - **Vercel Supports Stateless MCP**: Vercel now supports MCP via an HTTP stateless adapter, enabling the use of SSE or HTTP streamable transports for AI agent servers. - **Experimental MCP Client in AI SDK**: Vercel's AI SDK has an experimental MCP client with standard in/standard out, allowing argument execution on a serverless backend. - **Tool Creation and Integration**: The video demonstrates creating custom tools and integrating them into a Vercel application using MCP transport, showcasing front-end interaction. - **Connecting Front-End to MCP Server**: The presenter shows how to connect a front-end application to a MCP server running on Vercel, invoking tools and displaying results in real-time. - **Future: Integrate with OpenAI**: The presenter suggests future work could involve integrating the MCP setup with OpenAI for more complex AI agent interactions. --- ### Use Any MCP Server in #n8n – Supercharge Your AI Workflows **Published:** 5/28/2025 **Duration:** 13 minutes **YouTube:** https://youtube.com/watch?v=gwZcmj8hAN4 **Full LLM Text:** /video/cmis2qxuy000e04kw04hewnmj/llm.txt **Description:** Join the Community: https://nas.io/vibe-coding-retreat Get 50% off from http://supermachine.ai/ for 1-click install MCP servers (NERDINGIO) 📩 Newsletter: https://sendfox.com/nerdingio In this video, we show how to integrate any Model Context Protocol (MCP) server directly into your n8n workflows—turning your automations into powerful AI agents. Whether you're using Claude, LangGraph, or your own local LLM tools, this setup lets you: ⚙️ Connect to any MCP server 🧠 Run AI tools from within n8n 🔁 Chain tasks, prompts, and data flows 🔐 Build scalable AI workflows with full control Perfect for developers building agentic systems, data processing flows, or custom AI tools—without writing glue code. 🧠 What You’ll Learn: - What MCP is and why it matters - How to connect MCP-compatible agents in n8n - Tips for tool discovery, prompts, and sub-workflows 00:00 Introduction 00:21 Ghost CMS 03:21 Gateway 07:31 n8n 12:36 Open Source 13:22 Final Thoughts 🔗 Resources & Links: 🛠 Gateway: http://supermachine.ai/ 🛠 MCP Client Node: https://github.com/nerding-io/n8n-nodes-mcp 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA **Summary:** ## Video Summary: Use Any MCP Server in #n8n – Supercharge Your AI Workflows **1. Executive Summary:** This video demonstrates how to integrate any Model Context Protocol (MCP) server, including those with standard in/out interfaces, into n8n workflows using a gateway like Super Machine. This allows users to leverage AI tools and automate processes involving services like Ghost CMS or custom LLMs, without writing extensive custom code, by deploying and managing MCP servers in isolated environments. **2. Main Topics Covered:** * **Introduction to MCP:** Explanation of what MCP is and its importance in connecting various AI models and tools. * **Limitations of Existing n8n Integrations:** Highlighting the lack of native support for certain services (e.g., Ghost CMS) and challenges with standard in/out MCP servers in n8n. * **Super Machine Overview:** Introduction to Super Machine as a one-click deployment tool for MCP servers, acting as a secure proxy for n8n integration. * **Deploying MCP Servers via Super Machine:** Demonstrating the process of deploying both known MCP servers (e.g., Firecrawl) and custom MCP servers from GitHub repositories. * **Integrating MCP Servers with n8n:** Showing how to connect deployed MCP servers to n8n using their SSE endpoints and the native n8n MCP node. * **Practical Example: Automating Ghost CMS User Management:** Illustrating a workflow that automatically adds new users to Ghost CMS subscriptions based on external triggers (e.g., webhooks). * **Super Gateway: Self-Hosted MCP Proxy Option:** Mentioning Super Gateway as an open-source alternative for self-hosting an MCP proxy using Docker. **3. Key Takeaways:** * **Flexibility:** Integrating any MCP server, including those with standard in/out, into n8n workflows is possible using a gateway. * **Simplified Deployment:** Super Machine simplifies MCP server deployment in isolated environments, acting as a proxy for secure connection to n8n. * **Automation Power:** Integrating MCP servers with n8n enables powerful automation workflows for various tasks, like user management or content scheduling. * **Security:** Isolating MCP servers through Super Machine enhances security and avoids conflicts with the host machine's environment. * **Customization:** Super Machine allows deploying MCP servers directly from GitHub repositories, automatically configuring them and enabling connection to n8n. * **Alternative:** Super Gateway is an open-source alternative if self-hosting a proxy is preferred. **4. Notable Quotes or Examples:** * "So we're going to use this tool called Super Machine. And Super Machine is actually really awesome. It gives you one-click installs to be able to deploy your MCP servers in isolation away from your host server and actually connect to them." * Example: Demonstrating how to create an automation that adds a new member to a newsletter in Ghost CMS when a webhook is triggered, leveraging the MCP server to interact with Ghost's API. * "You can actually deploy a standard in standard out MCP server on this gateway and then you can actually connect it to N8N." **5. Target Audience:** * Developers building agentic systems and automated workflows. * AI enthusiasts and n8n users seeking to integrate custom AI models or services into their automations. * Individuals looking for efficient ways to manage services like Ghost CMS through automation. * Anyone interested in streamlining AI tool deployment and integration with n8n workflows. **Key Highlights:** - **Deploy Any MCP Server to n8n via Gateway**: Leverage a gateway (like Super Machine) to connect any MCP server, even standard in/out ones, to n8n for AI workflow automation. - **Super Machine: One-Click MCP Server Deployment**: Super Machine simplifies deploying MCP servers in isolated environments, acting as a proxy via SSE/WebSockets for secure connection to n8n. - **Custom MCP Server Integration from GitHub**: Super Machine allows deploying MCP servers directly from GitHub repositories, automatically configuring them and enabling connection to n8n. - **Automated User Management in Ghost CMS**: Demonstrates automating user creation in Ghost CMS triggered by webhooks using n8n and a custom MCP server, showcasing workflow potential. - **Super Gateway: Self-Hosted MCP Proxy Option**: Mentioned Super Gateway as an open-source option for self-hosting an MCP proxy using Docker, providing more control and customization. --- ### Fix GitHub Issues with Google Jules (Beta) **Published:** 5/23/2025 **Duration:** 9 minutes **YouTube:** https://youtube.com/watch?v=Me_4dz20mWY **Full LLM Text:** /video/cmis2r9iz000f04kwcyl0gssg/llm.txt **Description:** Get 50% off from supermachine.ai for 1-click install MCP servers (NERDINGIO) 📩 Newsletter: https://sendfox.com/nerdingio In this video, we explore the beta release of Google Jules, an AI coding agent, and test it on a real-world open source project: the n8n MCP Client Node. Watch as we hand off a GitHub issue to Jules and let it take over debugging, fixing, and pushing code—autonomously. We walk through: 🔧 An authentication bug caused by comma/space splitting 🧠 How Jules auto-plans and modifies code with context ✅ Running builds, creating PRs, and fixing test errors 🐇 Integrating with CodeRabbit for PR validation and code summaries 🧪 Using GitHub Actions + Jules to handle end-to-end dev flows 💡 Why This Is Exciting: Jules brings a new level of autonomy to AI-assisted coding. By connecting it to GitHub, we can go from issue to commit with almost no manual intervention. This is a huge step for open source maintainers and small teams managing fast-moving projects. Whether you're a contributor or lead maintainer, tools like Jules + CodeRabbit give you superpowers: - AI-generated plans & test coverage - Auto-created PRs & branch management - Issue-to-fix cycles with fewer touchpoints 🧠 What You’ll Learn: ✅ How to apply Jules to real GitHub issues ✅ How it discovers references & edge cases in code ✅ Where it succeeds—and where it might fail ✅ How it plays with GitHub Actions + PR validation ✅ Thoughts on future improvements (Notion, Jira, issue sync, etc.) 🔗 Resources & Links: 🛠 MCP Client Node: https://github.com/nerding-io/n8n-nodes-mcp 🔍 Google Jules (Beta): 🐰 CodeRabbit: https://coderabbit.ai 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA **Summary:** ## Video Summary: Fix GitHub Issues with Google Jules (Beta) **1. Executive Summary:** This video explores the beta release of Google Jules, an AI coding agent, and demonstrates its ability to automatically address and fix GitHub issues in an open-source project. The demonstration shows Jules handling tasks like bug identification, code modification, test creation, and pull request generation with minimal human intervention. **2. Main Topics Covered:** * **Introduction to Google Jules (Beta):** Overview of Jules as an asynchronous coding agent designed to automate code-related tasks. * **Real-World Application:** Applying Jules to fix an authentication bug in the n8n MCP Client Node GitHub repository. * **Jules' Workflow:** Step-by-step demonstration of how Jules analyzes the issue, creates a plan, modifies code, writes tests, and publishes branches. * **Integration with GitHub Actions:** Showcasing how Jules interacts with GitHub Actions to handle end-to-end development workflows, including build validation. * **CodeRabbit Integration:** Integrating with CodeRabbit for code review and summarizing the changes with AI generated descriptions. * **Potential Improvements:** Suggestions for future enhancements, such as automated issue detection and integration with project management tools like Jira and Notion. **3. Key Takeaways:** * **Automated Bug Fixes:** Jules can significantly reduce developer time by automatically addressing GitHub issues. * **Contextual Understanding:** Jules effectively understands complex code context, including related files and dependencies. * **Test Creation & Error Handling:** Jules can automatically generate tests and address build errors. * **Streamlined Development Workflow:** Jules facilitates a more efficient issue-to-fix cycle with fewer manual steps. * **Potential for Enhanced Automation:** Future improvements could include automated issue detection and integrations with other project management tools. **4. Notable Quotes or Examples:** * "Super impressed that it was able to go through and figure out like not only did we reference the uh uh standard in standard out, but we also it it found the HTTP streamable as well as the SSSE credentials." - Demonstrates Jules' ability to understand and navigate complex codebases. * Jules automatically detected a failing build and created a new commit to fix it, showcasing its ability to handle end-to-end development workflows. * The video demonstrates Jules generating comprehensive pull request descriptions and automatically closing related issues, improving collaboration and transparency. **5. Target Audience:** * Open-source project maintainers * Software developers * Teams managing fast-moving projects * Anyone interested in AI-assisted coding and automated development workflows **Key Highlights:** - **Google Jules Beta Automates Bug Fixes**: Jules, Google's asynchronous coding agent, can automatically address GitHub issues in open source projects, saving developer time by suggesting and implementing code changes. - **Jules Understands Complex Context**: The tool impressed by understanding not only the immediate issue but also related code in HTTP and SSE credentials and it also suggests and writes related tests automatically. - **Jules Catches and Fixes Build Errors**: Jules detected a failing build within the pull request and automatically created another commit that fixes the declarations to be defined error in the test suite. - **Potential for enhanced integrations and automation**: The presenter suggests that enhancements could include automated issue detection and integration with tools like Jira or Notion to streamline bug management workflows. --- ### Build an n8n MCP Server for Claude & Cursor – Agent-Driven Workflow Execution **Published:** 5/13/2025 **Duration:** 16 minutes **YouTube:** https://youtube.com/watch?v=QuqOL30O8Zs **Full LLM Text:** /video/cmis2rpvi000g04kwsogzoovx/llm.txt **Description:** 50% off One-click remote hosted MCP servers use NERDINGIO at Supermachine.ai https://supermachine.ai/ In this video, we explore how to turn your #n8n instance into an #mcp server, enabling AI agents like Claude or Cursor to discover, manage, and run full workflows—not just individual tools. This approach brings agent autonomy to the next level by giving your LLM assistant access to outcome-based workflows, designed for efficiency and control. 🧠 What You’ll Learn: ✅ What is an MCP server and why n8n is a great fit ✅ How to connect Claude Desktop or Cursor to your n8n workflows ✅ How to expose only approved workflows via tags like "mcp" ✅ Using Redis as agent memory to track available workflows ✅ Auto-discovering, adding, removing, and listing workflows ✅ Executing workflows via the Subworkflow trigger with parameter passthrough ✅ How to extract input schema from workflow JSON for smarter tool use 💡 Why It Matters: This setup enables AI agents to make fewer, more effective calls, focusing on outcomes rather than low-level tools. Agents using Claude or Cursor can now: - Search for tagged workflows - Add/remove workflows from use - Run production-safe, prebuilt flows autonomously - Adapt to changing tasks with fewer API calls and more context - Perfect for teams using Claude Desktop, Cursor IDE, or any MCP-compatible client. 🔗 Resources: 📘 n8n Template by Jimleuk: https://n8n.io/workflows/3770-build-your-own-n8n-workflows-mcp-server/ 📎 Model Context Protocol: https://modelcontextprotocol.io/ 🧠 Claude Desktop: https://claude.ai 📩 Newsletter: https://sendfox.com/nerdingio 📞 Book a Call: https://calendar.app.google/vx62Asp9DTk7dRLW7 **Summary:** ## Video Summary: Build an n8n MCP Server for Claude & Cursor – Agent-Driven Workflow Execution **1. Executive Summary:** This video demonstrates how to transform an n8n instance into a Model Context Protocol (MCP) server, enabling AI agents like Claude and Cursor to discover, manage, and execute n8n workflows. By exposing outcome-based workflows through an SSE endpoint, the video shows how to extend agent autonomy, allowing them to make fewer, more effective API calls and adapt to changing tasks more efficiently. **2. Main Topics Covered:** * **Introduction to MCP Servers and n8n Compatibility:** Explains the concept of an MCP server and why n8n is suitable for creating one. * **Connecting Claude Desktop and Cursor to n8n:** Shows how to integrate n8n workflows with AI tools like Claude and Cursor. * **Workflow Tagging for Controlled Access:** Explains how to use tags (e.g., "MCP") to restrict the workflows exposed to external applications, improving security and access management. * **Utilizing Redis for Agent Memory:** Details the use of Redis to store and track available workflows for the agent. * **Workflow Management (Discovery, Adding, Removing, Listing):** Demonstrates how agents can discover, add, remove, and list available workflows. * **Workflow Execution via Subworkflow Trigger:** Explains the use of the Subworkflow trigger with parameter passthrough to execute workflows. * **Super Gateway & Super Machine:** Discussion of Super Gateway, a solution for running servers over SSE/WebSockets, and Super Machine, a hosted solution that simplifies MCP server deployment and addresses scalability and security concerns. **3. Key Takeaways:** * **Enhanced Agent Autonomy:** Agents can now execute complex, pre-built workflows rather than just individual tools, leading to more effective and efficient task completion. * **Improved API Call Efficiency:** Agents make fewer API calls, focusing on outcomes and reducing unnecessary communication. * **Secure Workflow Exposure:** Tagging allows for controlled access to specific workflows, ensuring security and limiting access to sensitive processes. * **Simplified Deployment with Super Machine:** Super Machine provides a one-click solution for hosting MCP servers, removing the complexities of manual setup and offering scalability. * **Integration with Popular AI Tools:** Demonstrates seamless integration with Claude and Cursor, enabling workflow execution directly within these environments. **4. Notable Quotes or Examples:** * *"... what that means is you can actually use MCP to connect to your NAN workflows, list them, search them, and execute them."* - Defining the core functionality of the MCP server. * *"So that means that any uh workflow that is tagged right up here like this one uh with MCP will actually be identifiable to this particular uh flow. So this is another way of just kind of like gating uh your your API..."* - Explaining the use of tagging for access control. * Demonstration of connecting n8n workflow to Claude and Cursor for workflow execution. **5. Target Audience:** * n8n users looking to integrate their workflows with AI agents. * AI developers using Claude or Cursor who want to extend the capabilities of these tools. * Individuals interested in automating complex tasks using AI-driven workflow execution. * Teams seeking a secure and scalable solution for exposing n8n workflows to external applications. **Key Highlights:** - **MCP Server for n8n Workflows**: The video demonstrates building an MCP server to expose n8n workflows, enabling listing, searching, and executing them through an SSE endpoint. - **Integration with Claude and Cursor**: The MCP server can be integrated with AI tools like Claude and Cursor, allowing users to trigger and interact with n8n workflows directly from these environments. - **Super Gateway & Super Machine**: Super Gateway allows running servers over SSE/WebSockets and Super Machine offers a hosted solution for MCP servers, addressing scalability and security concerns. - **Workflow Tagging for Access Control**: Using tags (like 'MCP') on workflows allows for gating which workflows are exposed to specific applications, enhancing security and control over API access. --- ### Chat Interrupts in Action: AI Login Flow with Vercel AI SDK **Published:** 5/7/2025 **Duration:** 11 minutes **YouTube:** https://youtube.com/watch?v=xUKnWPh1fac **Full LLM Text:** /video/cmis2s1ul000h04kwihsnsxqh/llm.txt **Description:** In this video, we build a seamless AI-powered login flow using the Vercel AI SDK with chat interrupts—so your agent knows when to pause, prompt the user to log in, and resume the conversation with full context. This pattern unlocks secure, personalized experiences in your AI apps. Perfect for when your chatbot needs access to tools like Google Calendar, Notion, GitHub, or any private data source. 🧠 What You’ll Learn: ✅ How to implement chat interrupt events with Vercel AI SDK ✅ Triggering login during a live AI chat session ✅ Resuming the conversation with updated context ✅ Managing user sessions, tokens, and context injection ✅ Creating AI apps that are both interactive and secure 💡 Why This Matters: Most AI apps ignore auth—or bolt it on as an afterthought. This approach puts auth at the center of the conversation, letting your AI agent dynamically respond based on who the user is and what they’ve connected. 🧱 Stack: - Vercel AI SDK - Next.js (App Router) - Auth0 🔗 Resources & Links: 📘 Vercel AI SDK: https://sdk.vercel.ai 🔐 Auth0: https://auth0.com/ai/docs/check-google-calendar-availability 🧠 Text Yourself App: https://textyourself.app 📩 Newsletter: https://sendfox.com/nerdingio 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 📌 Chapters: 00:00 Intro 00:22 Setup 04:11 Code 07:57 Demo 11:41 Final Thoughts 💬 What tool would you connect to your AI chat experience? Calendar, CRM, Notion? Drop it in the comments! 👇 👍 Like & Subscribe for more AI chat, agent workflows, and real-time interaction patterns! **Summary:** ## Video Summary: Chat Interrupts in Action: AI Login Flow with Vercel AI SDK **1. Executive Summary:** This video demonstrates how to implement authentication interrupts within AI chat applications using the Vercel AI SDK and Auth0. By leveraging "chat interrupts," the AI agent can dynamically pause the conversation, prompt the user to log in and grant necessary permissions (like calendar access), and then seamlessly resume the conversation with updated context. **2. Main Topics Covered:** * **Introduction to AI-Powered Authentication Interrupts:** Using the Vercel AI SDK to handle authentication within GenAI workflows. * **Dynamic Scope Acquisition:** Demonstrating how to request specific scopes (e.g., Google Calendar) during the chat flow, rather than upfront at initial login. * **Implementation with Vercel AI SDK:** Showing how to use Vercel's 'execute with interrupts' feature to pause LLM execution for authentication. * **Federated Authentication Flow:** Utilizing Auth0 to federate access to the Google Calendar API and request additional scopes on demand. * **Code Walkthrough:** Reviewing the necessary code components in a Next.js application, including the interrupt handler, the Google authentication process, and the chat API route. * **Demo:** Demonstrating the chat interrupt in action, including the authentication popup and the resumption of the conversation after authentication. **3. Key Takeaways:** * Implementing chat interrupts with the Vercel AI SDK and Auth0 allows for dynamic authentication and permission handling during a chat session. * This approach enhances user experience by only requesting necessary permissions when needed. * The 'execute with interrupts' feature pauses the AI model, facilitating a seamless transition to authentication. * Federated authentication, like Auth0, simplifies managing user credentials and permission scopes. * The demo application used Next.js and highlighted how to integrate the interrupt system into a chat interface. **4. Notable Quotes or Examples:** * "This approach puts auth at the center of the conversation, letting your AI agent dynamically respond based on who the user is and what they’ve connected." * Demo of the AI agent prompting the user for calendar access mid-chat. * Explanation of the importance of the `interrupt` parameter to define when the tool executes the authentication component. * Mention of "Firebase and Nex.js starter kit with AI-powered components and built-in prompt instructions for content generation" as a related project (TextYourself App). **5. Target Audience:** * AI developers building chat applications with secure data access. * Developers interested in learning about AI authentication strategies. * Engineers seeking to improve user experience with dynamic permission requests. * Individuals curious about using the Vercel AI SDK for advanced AI agent workflows. **Key Highlights:** - **AI-Powered Authentication Interrupts**: Using Vercel AI SDK with Ozero allows for authentication interrupts within GenAI workflows, enhancing user experience. - **Dynamic Scope Acquisition**: The demo shows how to acquire calendar scopes dynamically within the chat flow, rather than upfront during initial login. - **Interrupt-Driven Execution**: Vercel's 'execute with interrupts' feature pauses the LLM execution to handle authentication and resumes seamlessly afterwards. - **Federated Authentication Flow**: Leveraging offzero to federate the access to the Google Calendar API, requesting additional scopes on demand. - **NexJS and Firebase Boilerplate**: The presenter mentions a Firebase and Nex.js starter kit with AI-powered components and built-in prompt instructions for content generation. --- ### Secure LangGraph Agents with Auth0 & Google Calendar Access **Published:** 5/5/2025 **Duration:** 19 minutes **YouTube:** https://youtube.com/watch?v=mT6HqSgWbVs **Full LLM Text:** /video/cmis2sdwu000i04kwd3j9r953/llm.txt **Description:** Checkout my GenKit boilerplate: https://aifirebasestarter.dev/ In this video, we show how to securely connect #langgraph #agents to user-specific data like Google Calendar using #auth0 for authentication and authorization. This setup lets your AI agents access personalized, permissioned data (e.g., a user’s calendar events) while maintaining secure OAuth flows and scoped access—essential for any production-ready AI assistant. 🧠 What You’ll Learn: ✅ How to authenticate users with Auth0 + OAuth2 ✅ How to access Google Calendar data securely inside Lang Graph ✅ How to persist tokens and use them inside agent steps ✅ How to make LangGraph agents context-aware with personal info ✅ Tips for scoping, refreshing tokens, and keeping it secure 🔒 Why This Matters Building AI agents that interact with real user data—like calendars, contacts, or docs—requires more than clever prompts. You need secure auth, scoped access, and clear data boundaries. This tutorial gives you a foundation to do exactly that using Auth0 + LangGraph. 🎯 Use Cases: - AI scheduling assistants - CRM-aware sales agents - Personalized productivity bots - Privacy-focused enterprise assistants 🔗 Resources & Links: 👉 Auth0 GenAI Intro: https://auth0.com/blog/introducing-auth-for-genai-launching-identity-for-ai-agents 👉 Auth0 Quickstart: https://auth0.com/blog/genai-tool-calling-build-agent-that-calls-gmail-securely-with-langgraph-vercelai-nextjs/ 👉 Assistant Repo: https://github.com/auth0-samples/auth0-assistant0 📩 Newsletter: https://sendfox.com/nerdingio 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 📌 Chapters: 00:00 Intro 00:23 Docs 02:44 Setup 07:30 Repo 09:56 Demo 14:06 Code 16:45 Tracing 19:27 Final Thoughts ⤵️ Let’s Connect: 🌐 https://nerding.io 🐦 Twitter: https://twitter.com/nerding_io 💼 LinkedIn: https://www.linkedin.com/in/jdfiscus/ 🚀 Ever Efficient AI: https://everefficient.ai 💬 How are you integrating user data into your AI workflows? Let’s talk security and scale in the comments. 👇 👍 Like & Subscribe for more AI agents, secure integrations, and advanced automation builds! **Summary:** Here's a comprehensive summary document for the video "Secure LangGraph Agents with Auth0 & Google Calendar Access": **1. Executive Summary** This video demonstrates how to securely connect LangGraph-based AI agents to user-specific data like Google Calendar using Auth0 for authentication and authorization. It provides a step-by-step guide for setting up an Auth0 application, configuring Google API access, and integrating these components into a LangGraph agent to enable secure, personalized data access. **2. Main Topics Covered** * **Auth0 Authentication & Authorization:** Using Auth0 for user authentication and authorization in AI agent workflows. * **Secure Google Calendar Access:** Accessing Google Calendar data securely within LangGraph using OAuth 2.0 and scoped permissions. * **Token Persistence & Usage:** Persisting authentication tokens and using them within agent steps to access user data. * **Context-Aware Agents:** Building LangGraph agents that are context-aware through personalized user data. * **LangGraph Project Setup:** Step-by-step instructions on setting up the Auth0 assistant zero project from GitHub. * **Tracing and Debugging with LangSmith:** Using LangSmith to understand data flow within agents. **3. Key Takeaways** * Integrating Auth0 with LangGraph enables secure access to user data, crucial for production-ready AI assistants. * OAuth 2.0 and scoped access control are essential for protecting user data and maintaining privacy. * The Auth0 assistant zero project provides a practical starting point for building secure AI agents with Google Calendar integration. * Token management (persistence and refreshing) is critical for maintaining secure and continuous data access. * Tracing tools like LangSmith are vital for debugging and understanding the flow of information within complex LangGraph agents. * GenKit boilerplate: a Firebase and Nex.js starter kit that comes with AI powered apps. **4. Notable Quotes or Examples** * "With that, let's go ahead and get started. All right, so I've actually been waiting for this to come out for a while. I really like Ozero stuff and the fact that you can actually have authentication through Ozero in your agentic workflows and AI agents is super cool in my opinion." * "...what this means is you're going to have a user, you're going to have your AI agent, and then you can use O to either pull from the uh external entities." * [Example] Querying the AI agent to retrieve calendar events: "What events do I have this week?" **5. Target Audience** * AI developers and engineers * Individuals interested in building secure, production-ready AI agents * Those who want to integrate AI agents with user-specific data, such as Google Calendar * Developers familiar with LangGraph, Auth0, and OAuth 2.0 concepts **Key Highlights:** - **Auth0 + LangGraph = Secure AI Agents**: JD explores how to integrate Auth0 for authentication and authorization within LangGraph-based AI agents, enhancing security in agentic workflows. - **Access Google Services Securely**: The video demonstrates how to use Auth0 to grant AI agents secure access to Google services like Calendar, Gmail, and Drive using OAuth 2.0 and API scopes. - **Tool Calling for Data Retrieval**: Learn how tool calling enables AI agents to pull data from external sources, such as Google Calendar, using Auth0 for secure authentication and authorization. - **Open Source Project Setup Walkthrough**: JD provides a step-by-step guide on setting up the Auth0 assistant zero project from GitHub, configuring credentials, and enabling Google APIs. - **Debugging with LangSmith**: The video highlights the importance of tracing and debugging tools using LangSmith to understand the flow of information within LangGraph agents and pinpoint issues. --- ### Build a Customer Support AI Agent with LangGraph & Portkey Prompt API **Published:** 4/25/2025 **Duration:** 12 minutes **YouTube:** https://youtube.com/watch?v=6MgPd3O3FXs **Full LLM Text:** /video/cmis2spy5000j04kwxk2aqhpx/llm.txt **Description:** In this video, we walk through how to build a customer support AI agent using #langgraph for agent orchestration and #portkey Prompt Render API for flexible, dynamic prompt management. This agent isn’t just a chatbot—it can reason, route, and respond like a human support rep by chaining tools, memory, and context-aware responses in a modular and scalable graph-based system. 🧠 What You’ll Learn: ✅ How to build a multi-step support agent with LangGraph ✅ How to dynamically render prompts using Portkey’s Prompt Render API ✅ How to chain together tools, memory, and decisions in a structured flow ✅ Use cases for real-world AI-driven customer support ✅ How to make your agents transparent, debuggable, and adaptable 💡 Why It’s Powerful: LangGraph allows you to create reliable agent workflows with proper control, observability, and modularity—no more spaghetti prompt logic. Portkey helps separate prompt content from code, making it easier to test, version, and maintain over time. 🎯 Perfect for: - SaaS support chatbots - Internal AI assistants - Lead qualification bots -Tier-1 support routing 🎥 Watch the full walkthrough and deploy your own AI agent! 🔗 Resources: 👉 LangGraph: https://www.npmjs.com/package/@langchain/langgraph-supervisor 👉 Portkey Langgraph: https://portkey.ai/docs/integrations/agents/langgraph#2-reliability 👉 Portkey Prompt Render API: https://portkey.ai/docs/product/prompt-engineering-studio/prompt-api#portkey-node-sdk 🧠 Text Yourself: https://textyourself.app 📩 Newsletter: https://sendfox.com/nerdingio 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 📌 Chapters: 00:00 Intro 00:20 Docs 03:49 Demo 05:16 Code 10:06 Observability 12:00 Final Thoughts ⤵️ Let’s Connect: 🌐 https://nerding.io 🐦 Twitter: https://twitter.com/nerding_io 💼 LinkedIn: https://www.linkedin.com/in/jdfiscus/ 🚀 Ever Efficient AI: https://everefficient.ai 💬 Have you tried LangGraph or Portkey? Drop your use cases or questions in the comments! 👇 👍 Like & Subscribe for more AI agent workflows and automation builds! **Summary:** Here's a summary document for the video "Build a Customer Support AI Agent with LangGraph & Portkey Prompt API": **1. Executive Summary:** This video demonstrates how to build a customer support AI agent using LangGraph for agent orchestration and Portkey's Prompt Render API for dynamic prompt management. The focus is on creating a multi-step, reasoning agent that can route and respond like a human support rep by leveraging tools, memory, and context-aware prompts in a modular, scalable graph-based system. **2. Main Topics Covered:** * **LangGraph Implementation:** Building a multi-step support agent using LangGraph, focusing on TypeScript implementation of the pre-built multi-agent supervisor. * **Portkey Integration:** Using Portkey as an AI gateway for prompt management, load balancing, observability, and metrics within the LangGraph setup. * **Dynamic Prompt Rendering:** Employing Portkey's Prompt Render API to dynamically load and update prompts, enabling prompt editing without code changes. * **Agent Specialization**: Each agent is able to access only specific tools that allow it to be most explicit about the information it's providing. * **Debugging and Observability:** Emphasizing the importance of transparency and debuggability in AI agent workflows, showcasing Portkey's logging capabilities. * **AI Starter Kit Mention:** A brief mention of a Firebase and Next.js starter kit with pre-built AI components for AI-powered applications. **3. Key Takeaways:** * LangGraph facilitates the creation of reliable and maintainable agent workflows, offering better control, observability, and modularity compared to traditional prompt chaining. * Portkey's Prompt Render API allows for prompt management outside of the code base, enabling easier testing, versioning, and maintenance of prompts. * Dynamic prompts can be loaded and updated from Portkey's management tool, facilitating real-time adjustments without code deployments. * While Portkey's TypeScript SDK lacks trace logging (present in the Python SDK), prompt management can still be effectively implemented. * The AI agent can route customer inquiries to specialized agents (product, pricing, shipping, general support) based on the question's context. **4. Notable Quotes or Examples:** * "LangGraph allows you to create reliable agent workflows with proper control, observability, and modularity—no more spaghetti prompt logic." * "Portkey helps separate prompt content from code, making it easier to test, version, and maintain over time." * Example Use Case: Customer asks: "What are the shipping options for New York?" The agent recognizes the need for shipping information and responds: "Here are the shipping options for New York." * "We can actually load the prompts that we're using in the port key prompt management tool and we can do that with something they released called prompt renderer." * Using API calls to dynamically load prompts instead of embedding the prompts directly in the code. This enables for ease of change and less code deployment. **5. Target Audience:** * Developers interested in building AI-powered customer support solutions. * Engineers working with LangChain/LangGraph and seeking improved agent orchestration. * Individuals looking for solutions for dynamic prompt management and AI gateway integration. * Teams building SaaS support chatbots, internal AI assistants, or lead qualification bots. * Anyone looking to enhance AI agent transparency, debuggability, and adaptability. **Key Highlights:** - **LangGraph Multi-Agent Supervisor with TypeScript**: Demonstrates using the pre-built LangGraph multi-agent supervisor with a focus on TypeScript implementation, mimicking React agents for customer support. - **Portkey AI Gateway Integration**: Highlights the integration of Portkey as an AI gateway for prompt management, load balancing, observability, and metrics within the LangGraph setup. - **Prompt Renderer API for Dynamic Prompts**: Explains how to use Portkey's Prompt Renderer API to dynamically load and update prompts from the management tool, enabling prompt editing without code changes. - **Handling Missing Trace Logging in TypeScript SDK**: Addresses the lack of trace logging in Portkey's TypeScript SDK and provides a solution to implement prompt management, a feature readily available in the Python SDK. - **AI-First Firebase and Next.js Starter Kit**: Briefly mentions a Firebase and Next.js starter kit with pre-built AI components for building AI-powered applications with features like built-in prompt instructions and a chatbot. --- ### Real-Time AI Public Speaking Feedback (Gemma + MediaPipe) **Published:** 4/10/2025 **Duration:** 11 minutes **YouTube:** https://youtube.com/watch?v=v7YJCDEfqaU **Full LLM Text:** /video/cmis2t0bc000k04kwqdooz9b0/llm.txt **Description:** 🧠 AI Starter Kit (Side Project): http://aifirebasestarter.dev/ In this project, we explore how to build a Web AI-powered public speaking coach that runs entirely in the browser—using Gemma for LLM processing, MediaPipe for facial/body analysis, and a Bluetooth heart rate monitor for real-time biofeedback. What It Does: 💬 Gives real-time feedback on your tone, and content 📊 Tracks your heart rate while speaking to assess nervousness 🗣️ Uses Gemma to provide AI-generated tips, summaries, and practice prompts 🔋 All client-side: runs in the browser with no data sent to servers 🔍 What You’ll Learn: ✅ How to connect a Bluetooth heart rate sensor in the browser ✅ How to use MediaPipe for tracking face, hands, and posture ✅ How to embed Gemma LLM locally using WebGPU ✅ How to combine these inputs into actionable, AI-generated coaching feedback ✅ Creating a privacy-first AI application—no backend required! 💡 Why This Is Cool You don’t need an expensive coach or cloud APIs to get better at public speaking. With just a browser and a heart rate monitor, you can train with a real-time, personalized AI coach that respects your privacy. 🎯 Use Cases: - Public speaking practice - Interview preparation - Speech anxiety coaching - Real-time performance review 🎥 Watch the full demo and see it in action! 🔗 Resources & Links: 🧠 AI Starter Kit (Side Project): http://aifirebasestarter.dev/ 📩 Newsletter: https://sendfox.com/nerdingio 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 📌 Chapters: 00:00 Intro 00:21 Project 01:20 Bluetooth 02:21 Demo 06:20 Code Let’s Connect & Build Together 🌐 https://nerding.io 🐦 Twitter: https://twitter.com/nerding_io 💼 LinkedIn: https://www.linkedin.com/in/jdfiscus/ 🚀 Ever Efficient AI: https://everefficient.ai 💬 What features would you add to an AI speech coach? Drop your ideas below! 👇 👍 Like & Subscribe to keep up with cutting-edge local AI tools and creative builds! **Summary:** ## Video Summary: Real-Time AI Public Speaking Feedback (Gemma + MediaPipe) **1. Executive Summary:** This video demonstrates a browser-based AI public speaking coach built using Gemma LLM, MediaPipe, and Bluetooth integration for real-time feedback on tone, content, and heart rate. The project emphasizes privacy by running entirely client-side with no data sent to servers, showcasing a powerful AI application achievable without cloud APIs. **2. Main Topics Covered:** * **Project Overview:** Explanation of the public speaking coach's functionality, combining voice, video, and heart rate data for AI-powered feedback. * **Bluetooth Integration:** Demonstration of connecting to a Bluetooth heart rate monitor in the browser to track nervousness and incorporate biometric data. * **Gemma LLM Implementation:** Explanation of embedding the Gemma 2 model locally using WebGPU and caching it for faster loading times. * **Real-time Data Analysis:** Combining voice, video, and heart rate input to generate actionable coaching feedback from the LLM. * **Code Walkthrough:** Brief overview of the codebase, including speech recognition, webcam access, Bluetooth connection, and LLM integration. * **Future Experiments:** Mention of potentially incorporating facial recognition to improve the analysis and feedback further. **3. Key Takeaways:** * It's possible to build a fully functional AI-powered application, specifically a public speaking coach, that runs entirely within the browser. * Combining LLMs (like Gemma) with sensor data (voice, video, heart rate) enables rich, real-time feedback and personalized coaching experiences. * Client-side processing ensures user privacy by eliminating the need for backend servers and data transmission. * Bluetooth integration is a viable option for gathering biometric data and using it to enhance AI-driven applications. * Gemma model caching significantly improves loading times and overall user experience. **4. Notable Quotes or Examples:** * "This is me doing public speaking and I'm a little nervous about it. So it's weird being on camera and talking about myself all at the same time." (Example of the speaker using the app and receiving feedback). * "We're going to invoke an LLM directly through the browser and nothing else." (Emphasizes the client-side nature of the project). * "[Caching the model] If I reload it's uh super fast right i now have in my debug. You saw how quick that was as before you could see it was actually loading the LM." (Highlights the performance benefit of model caching). * Discussion about leveraging facial recognition in the future to augment the feedback loop **5. Target Audience:** * Web developers interested in AI and LLMs. * Individuals seeking to build client-side AI applications. * Developers interested in integrating Bluetooth devices with web applications. * AI enthusiasts exploring privacy-focused AI solutions. * Anyone interested in the intersection of AI, public speaking, and biometric data. **Key Highlights:** - **Browser-Based LLM Public Speaking Coach**: The project demonstrates a public speaking coach built entirely in the browser using web AI, HTML, and JavaScript, invoking an LLM directly without backend dependencies. - **Real-time Data Input: Voice, Video, Heart Rate**: The coach utilizes real-time voice and video feeds, combined with heart rate data from a Bluetooth-connected monitor, to provide comprehensive feedback. This provides rich data for analysis. - **Gemma Model Caching for Fast Loading**: The Gemma 2 model is cached in the browser, enabling significantly faster loading times after the initial load, improving the user experience. - **Bluetooth Integration for Biometric Feedback**: The project successfully integrates with a Bluetooth heart rate monitor to gather physiological data, showcasing the potential of incorporating biometric feedback into AI-powered coaching systems. - **Experimenting with Facial Recognition**: Future experiments may explore facial recognition to add another layer of analysis and feedback to the public speaking coach. This will further enhance the quality of feedback. --- ### Use MCP with Images PDF's, and Databases #mcp #sse #n8n **Published:** 3/31/2025 **Duration:** 14 minutes **YouTube:** https://youtube.com/watch?v=Qydl8LbZxXk **Full LLM Text:** /video/cmis2tc5u000l04kwu4ojkoye/llm.txt **Description:** Join the Community: https://nas.io/vibe-coding-retreat 50% off One-click remote hosted MCP servers use NERDINGIO at Supermachine.ai https://supermachine.ai/ 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 📰 Newsletter: https://sendfox.com/nerdingio In this video, we dive into how to create MCP resource templates designed for Server-Sent Events (SSE) — enabling real-time, web-friendly communication between AI models and external tools. We cover dynamic resource examples including: 🗃️ Databases – Query live data and stream results 🖼️ Images – Process and serve images on-demand 📄 PDFs – Send PDFs to OpenAI 🔍 What You’ll Learn: ✅ What are MCP Resource Templates and how they power AI tools ✅ How to build SSE-compatible resources for live, browser-friendly communication ✅ Dynamic examples using databases, images & pdfs ✅ How these templates help extend your AI assistant with custom tools 💡 Why MCP + SSE? MCP over SSE allows for real-time, standardized, and web-compatible AI workflows. With templates, you can rapidly plug in new tools that make your LLMs smarter and more useful in context-aware applications. 🎥 Chapters 00:00 Introduction 00:26 SSE Server 01:24 Templates 03:49 Inspector 06:13 n8n 10:08 Image 12:44 PDF 🔗 Links Source: https://github.com/nerding-io/mcp-sse-example Spec: https://spec.modelcontextprotocol.io/specification/2024-11-05/server/tools/ ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** Here's a comprehensive summary document for the provided video, designed for quick understanding: **Document Title: MCP Resource Templates: Dynamic Data Delivery for AI Workflows** **1. Executive Summary:** This video explains how to utilize Model Context Protocol (MCP) resource templates to dynamically serve various data types (databases, images, PDFs) to AI models using Server-Sent Events (SSE) for real-time communication. It demonstrates practical applications, including database queries, image analysis via Base64 encoding, and PDF uploads to OpenAI assistants. **2. Main Topics Covered:** * **Introduction to MCP Resource Templates:** Explanation of dynamic resource creation using mustache-style templating. * **SSE Server Implementation:** Setting up an SSE server for real-time data delivery. * **Dynamic Data Examples:** Serving different data types: * Databases: Querying and streaming database results (read-only). * Images: Processing and serving images as Base64 encoded strings. * PDFs: Sending PDFs to OpenAI for embedding. * **MCP Inspector:** Using the MCP Inspector to test and validate resource templates. * **N8N Integration:** Demonstrating how to use MCP resource templates within the N8N workflow automation platform. * **Workarounds for Image Analysis:** Implementing a workaround to enable OpenAI to analyze images fetched via MCP by encoding them as Base64 and utilizing the correct OpenAI API parameters. * **PDF Uploads to OpenAI Assistants:** Uploading PDFs to OpenAI assistants using MCP for embedding and knowledge base augmentation. **3. Key Takeaways:** * **MCP Resource Templates for Dynamic Content:** Enables flexibility in serving different data types to AI models. * **SSE for Real-Time Communication:** MCP over SSE allows for real-time, standardized, and web-compatible AI workflows. * **Base64 Encoding for Images and PDFs:** Allows for sending image and PDF data to LLMs for analysis. * **Database Integration for Read-Only Access:** MCP can be used to access databases for read-only operations. * **Custom Tools & Workflows:** Templates can rapidly plug in new tools that make LLMs smarter and more useful in context-aware applications. **4. Notable Quotes or Examples:** * **Dynamic Greeting Example:** "In this instance all we're doing is just saying hello to someone but we can also actually pass back files." (illustrates basic templating) * **Database Integration:** "While we can use tools to read and write from the database there might be a an opportunity to say well I just want to do a read only from my database or my data set" * **Image analysis with OpenAI (workaround):** "when we typically send images to OpenAI we actually have to send it as a different flag...you have to actually send this through like an image media URL or send it as base 64 code." * **Uploading PDFs to Open AI Assistants:** Demonstrates how to upload PDFs accessed via MCP resource templates to OpenAI assistants for embedding and later use. **5. Target Audience:** * AI/ML Developers * Workflow Automation Engineers * Individuals interested in integrating AI models with external data sources * Developers using or considering Model Context Protocol (MCP) * N8N users looking to extend their workflows with AI capabilities **Key Highlights:** - **Dynamic Resources with MCP Templates**: MCP resource templates allow dynamic content delivery using mustache-style templating for variables. Enables flexibility in serving different data types. - **Serving Images and PDFs via MCP**: MCP can serve images and PDFs by encoding them as Base64. This allows LLMs to access and process visual data from server-side resources. - **Database Integration with MCP**: MCP can be used to access databases for read-only operations, acting as a resource template for querying and retrieving data sets, useful for read-only access. - **N8N Integration for Image Analysis**: Shows a workaround to enable OpenAI to analyze images by dynamically fetching them through MCP and encoding them in Base64. - **PDF Uploads to OpenAI Assistants**: Demonstrates how to upload PDFs accessed via MCP resource templates to OpenAI assistants for embedding and later use. --- ### Build a sales prospect chat in 5 min with MCP Resources, #n8n, and Firecrawl **Published:** 3/24/2025 **Duration:** 8 minutes **YouTube:** https://youtube.com/watch?v=28M5SwKsZpA **Full LLM Text:** /video/cmis2tnzq000m04kwjxul8li9/llm.txt **Description:** Join the Community: https://nas.io/vibe-coding-retreat 50% off One-click remote hosted MCP servers use NERDINGIO at Supermachine.ai https://supermachine.ai/ 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 📰 Newsletter: https://sendfox.com/nerdingio In this lightning-fast tutorial, we show you how to build a fully functional chatbot in under 5 minutes using: 🧠 Model Context Protocol (#mcp ) Resource 🔁 n8n Workflows 📄 LLM Full Text instructions Whether you're building for support, automation, or internal tools, this setup gives you a flexible, AI-powered chatbot that’s easy to configure and extend — no custom backend required. 🚀 What You’ll Learn: ✅ How to connect MCP Resources to your n8n workflows ✅ Using LLM Full Text nodes to craft rich, dynamic AI responses ✅ Sending and receiving chat messages via standardized MCP flows ✅ Deploying a lightweight, scalable chatbot that can run anywhere 💡 Why Use MCP + n8n? Model Context Protocol gives your chatbot standardized access to tools, data, and LLMs — all through one unified interface. Combined with n8n, you can visually automate flows, respond to user inputs, and call external services with ease. 🎥 Chapters 00:00 Introduction 00:25 Demo 02:22 Logic 03:07 Server 05:22 Message Flow 06:13 Inspector 07:04 n8n 08:08 Conclusion 🔗 Links Source: https://github.com/nerding-io/mcp-sse-example Spec: https://spec.modelcontextprotocol.io/specification/2024-11-05/server/tools/ ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## Video Summary: Build a Sales Prospect Chat in 5 Min with MCP Resources, #n8n, and Firecrawl **1. Executive Summary:** This video demonstrates how to quickly build an AI-powered chatbot using Model Context Protocol (MCP), n8n, and Firecrawl. The chatbot leverages website scraping to provide context to the AI agent, enabling it to answer questions and serve as a prospecting tool without requiring a custom backend. **2. Main Topics Covered:** * **Introduction to MCP Resources:** Explanation of how MCP resources provide context to AI agents, enabling them to answer questions based on scraped website data. * **Firecrawl for Full-Text Scraping:** Demonstration of using Firecrawl to scrape an entire website into a single markdown file, which then becomes a resource for the AI agent. * **n8n Workflow Integration:** Using n8n to connect the chat interface to the MCP resource, enabling real-time data access and dynamic chatbot responses. * **MCP Resource Subscription:** Overview of MCP's resource subscription feature, allowing clients to be notified when a resource is updated, facilitating real-time data integration. * **Resources vs. Tools:** Highlighting the difference between tools and resources for AI agents; resources provide context, similar to stdin/stdout, but directly. * **Demo of the Chatbot:** Real-time demonstration of the chatbot in action, showcasing its ability to answer questions based on scraped website data. * **Server Configuration:** Overview of how the MCP server is configured to serve the scraped website content as a resource. **3. Key Takeaways:** * MCP Resources, n8n, and Firecrawl can be used to rapidly build chatbots that leverage existing website content. * MCP provides a standardized interface for AI agents to access data and tools. * Firecrawl enables efficient scraping of entire websites into a single, usable text file. * n8n facilitates visual automation of flows, enabling easy connection of the chatbot interface to the backend resource. * MCP Resources allow for dynamic updating of chatbot responses through resource subscriptions. **4. Notable Quotes or Examples:** * "So what we're going to do is we're going to take this website and turn it into a chatbot which is pretty simple task." - Introducing the goal of the video. * "Firecrawl ...scrape the entire website and put it into a single text file...that we can then take and put into our context." - Explaining the role of Firecrawl. * "So it's very similar to like a tool except a tool our tools have been doing actions for us whether that be hitting an API or or something else um resources work the same way on SS8 as they do a standard in standard out." - Differentiating between tools and resources in AI agents. * Example Question: "What was the latest?" - Demonstrating the chatbot's ability to extract relevant information from the scraped resource. * Example Question: "Have contact?" - Demonstrating the chatbot's ability to find and extract contact information **5. Target Audience:** * Software developers * AI/ML engineers * Automation specialists * Sales and marketing professionals * Individuals interested in building AI-powered chatbots * Anyone looking to streamline information access using AI. **Key Highlights:** - **Turn Websites into Chatbots in Minutes**: The video demonstrates how to quickly convert a website into a chatbot using MCP Resources, n8n, and Firecrawl. It streamlines information access and offers a prospecting tool. - **MCP Resources for Contextual AI**: MCP resources provide context to AI agents, enabling them to answer questions based on scraped website data. It uses a static resource to gather info using LLMs. - **Firecrawl for Full-Text Scraping**: Firecrawl is used to scrape entire websites into a single markdown file, which then becomes a resource for the AI agent. This allows for efficient information retrieval. - **Resource Subscription for Updates**: MCP's resource subscription feature allows clients to be notified when a resource is updated, facilitating real-time data integration and dynamic chatbot responses. - **Resources vs. Tools for AI Agents**: The video highlights the difference between tools and resources for AI agents, noting that resources provide context, similar to how standard input/output (stdin/stdout) works, but directly. --- ### Build a MCP Client in Typescript, ReactJS & Cursor **Published:** 3/20/2025 **Duration:** 21 minutes **YouTube:** https://youtube.com/watch?v=jog5-UigoTg **Full LLM Text:** /video/cmis2tz1n000n04kwkqocmyhr/llm.txt **Description:** Join the Community: https://nas.io/vibe-coding-retreat 50% off One-click remote hosted MCP servers use NERDINGIO at Supermachine.ai https://supermachine.ai/ In this tutorial, we’ll dive into building an MCP client using #typescript, #react #cursor and Inspector, enabling seamless interaction with Model Context Protocol (#mcp) servers for AI-powered applications. What You’ll Learn: ✅ What is MCP? – How AI models interact with external tools and data ✅ Setting Up an MCP Client in ReactJS & TypeScript ✅ Using Cursor for AI-assisted coding and development efficiency ✅ Debugging & Monitoring MCP Requests with Inspector ✅ Connecting Your MCP Client to a Server for real-time AI interactions 💡 Why This Matters This setup allows you to build dynamic, AI-powered web applications that interact with MCP servers in real time. By incorporating Cursor, you can enhance development productivity, while Inspector helps streamline debugging. 🎥 Chapters 00:00 Introduction 00:30 Code 01:20 Docs 03:19 Inspector 05:59 Debugging 11:20 Typscript 18:25 Cursor 🔗 Links Source: https://github.com/nerding-io/mcp-sse-example Inspector: https://github.com/modelcontextprotocol/inspector ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## Video Summary: Build an MCP Client in Typescript, ReactJS & Cursor **1. Executive Summary:** This tutorial guides viewers through building an MCP (Model Context Protocol) client using React, TypeScript, and Cursor, enabling AI-powered web applications to interact with MCP servers. It covers the basics of MCP, debugging with the MCP Inspector, creating a React client, and integrating the client into the Cursor IDE for enhanced development. **2. Main Topics Covered:** * **Introduction to MCP Clients:** Defining what an MCP client is and its role in interacting with MCP servers, providing examples from cloud desktop applications. * **Debugging with MCP Inspector:** Demonstrating how to use the MCP Inspector (a TypeScript-based tool) for testing and debugging MCP servers, including setup, server connection, and tool/prompt testing. * **Building a React MCP Client:** A step-by-step walkthrough of creating a React-based MCP client. Includes importing necessary libraries (SSE, Client, Transport), connecting to an MCP server, listing available tools, and executing those tools with user-provided arguments. * **MCP Client Integration with Cursor:** Showing how to add the built MCP Client to the Cursor IDE with an SSE server, and showing how to interact with the server directly within the code editor. **3. Key Takeaways:** * MCP enables AI models to interact with external tools and data sources. * The MCP Inspector is a valuable tool for debugging and testing MCP server implementations. * React and TypeScript provide a robust environment for building custom MCP clients for web applications. * Integrating MCP clients into IDEs like Cursor enhances development productivity and real-time AI interactions. * Understanding SSE (Server-Sent Events) is crucial for real-time communication between the client and the MCP server. **4. Notable Quotes or Examples:** * "MCP is an open- source through cloud specifically ... it gives you ability to do different actions." * "[The MCP Inspector] gives you the ability to put in variables to actually run and send to our implementation or to our from our client through the uh through our proxy to the server." * (Talking about building the React client) "We're going to set up an MCP client we need to import so we have to import our SSE we also have to import the client so we have our client and we have our transport..." * (When showing how to create a new chat with the newly created SSE server) "That should actually go out to the tool and uh pull back information from the uh MCP tool" **5. Target Audience:** * Software developers interested in building AI-powered web applications. * Individuals looking to understand and implement the Model Context Protocol (MCP). * React and TypeScript developers seeking to integrate AI models into their projects. * Developers interested in using Cursor IDE to develop and test AI applications. **Key Highlights:** - **MCP Client Overview & Purpose**: Explains what an MCP client is and introduces the goal of building one using React, TypeScript and Cursor. Showcases examples of usage in different client interfaces and highlights its role in interacting with MCP servers. - **Debugging with MCP Inspector**: Demonstrates using the MCP Inspector, a TypeScript-based tool, for testing and debugging MCP servers. It covers setting up the inspector, connecting to a server, and testing tools, prompts, and resources. Emphasizes its value for local development. - **Building a React MCP Client**: Walks through building a React-based MCP client, including importing necessary libraries (SSE, Client, Transport), setting up the client to connect to a server, listing available tools, and executing those tools with user-provided arguments. - **MCP Client Integration with Cursor**: Illustrates how to integrate an MCP client directly into the Cursor IDE. Shows how to add an SSE server within Cursor settings and use it to execute tools and interact with the server directly within the code editor. --- ### How to build MCP SSE and deploy with Docker **Published:** 3/18/2025 **Duration:** 15 minutes **YouTube:** https://youtube.com/watch?v=UsmwdXKy9CU **Full LLM Text:** /video/cmis2ubrf000o04kwqpo05nea/llm.txt **Description:** Join the Community: https://nas.io/vibe-coding-retreat 50% off One-click remote hosted MCP servers use NERDINGIO at Supermachine.ai https://supermachine.ai/ 📞 Book a Call: https://calendar.app.google/vx62Asp9DTk7dRLW7 📰 Newsletter: https://sendfox.com/nerdingio In this tutorial, we walk through building an MCP server that uses Server-Sent Events (SSE) to enable real-time communication with AI models. You'll also learn how to deploy it using Docker, making it easy to scale and integrate into web applications. What You’ll Learn: ✅ What is MCP SSE? – Understanding Model Context Protocol over SSE ✅ Building an MCP Server – Implementing SSE for AI-powered web apps ✅ Creating Custom MCP Tools – Extending AI models with web-based capabilities ✅ Testing/Debugging MCP SSE – Using the inspector for debugging and testing ✅ Deploying with Docker – Containerizing and running your MCP SSE server 💡 Why This Matters By leveraging SSE, your AI applications can maintain a persistent, real-time connection with clients—enabling seamless interactions without polling. This approach is ideal for web-based AI assistants, chatbots, and dynamic AI workflows. 🎥 Chapters 00:00 Introduction 00:26 Code 01:05 Tool Spec 03:19 Building with LLM txt 04:10 Server 10:28 Testing 12:20 Docker 🔗 Links Source: https://github.com/nerding-io/mcp-sse-example Spec: https://spec.modelcontextprotocol.io/specification/2024-11-05/server/resources/#message-flow ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** Here's a summary document designed to quickly convey the video's content: **Document Title: MCP SSE Server Development and Docker Deployment: A Quick Guide** **1. Executive Summary:** This video provides a practical walkthrough of building an MCP (Model Context Protocol) server utilizing Server-Sent Events (SSE) for real-time AI communication. It covers the essential steps from creating custom tools to deploying the server with Docker, enabling scalable AI application integration. **2. Main Topics Covered:** * **MCP SSE Fundamentals:** Understanding the core principles of Model Context Protocol (MCP) over SSE for real-time AI interaction. Explanation of JSON-RPC spec for communication and session/tool invocation process * **Building an MCP Server:** Implementing SSE endpoints for AI-powered web applications, including message transport and tool handling. * **Creating Custom MCP Tools:** Developing and defining custom tools for AI models with example implementations: basic math function (add) and using an API (fetch). Using JSON to format return messages that contain text, images, or resources. * **Leveraging LLMs for Development:** Demonstrating how to use Large Language Models (LLMs) with the MCP documentation to accelerate server development, including prompt generation. * **Testing/Debugging MCP SSE:** Using an inspector tool to examine available tools and call functions for debugging. * **Docker Deployment:** Containerizing the MCP SSE server using Dockerfile and Docker Compose, configuring Render for deployment, and setting up environment variables. * **Integration:** Connecting the deployed server to an n8n custom node for seamless workflow integration. **3. Key Takeaways:** * SSE provides a real-time communication channel between clients and AI models, eliminating the need for continuous polling. * MCP's JSON-RPC specification offers a standardized way to manage communication, tool listing, and invocation. * LLMs can significantly speed up development by providing code suggestions and scaffolding based on documentation. * Docker simplifies deployment and scaling of MCP SSE servers. * The Model Context Protocol Inspector facilitates testing and verifying server functionality. * MCP SSE servers can integrate with platforms like n8n for automated workflows. **4. Notable Quotes or Examples:** * "In the specification, they actually have a really good graph of what uh tools are in the server...basically you have different types of protocol messages." - Explaining the core architecture of MCP. * "Another really cool trick with mCP and llms is they actually give you an llm full text...you can actually come in here copy the link address and this will give you the full website of the documentation that you can actually pull in" - Demonstrating how to leverage LLMs for rapid development using the MCP documentation. * Example API tool "Defines the code to call an external API and return text. It also demonstrates passing environment variables through the server header" * Discusses that return messages "don't necessarily have to be text, you can send images or even resources" **5. Target Audience:** * Developers building AI-powered web applications. * Engineers interested in real-time communication protocols like SSE. * Individuals looking to integrate AI models into automated workflows using platforms like n8n. * Those seeking practical guidance on deploying servers with Docker. * Anyone wanting to understand and implement the Model Context Protocol. **Key Highlights:** - **Building an MCP SSE Server**: The video details the process of building a server-sent events (SSE) server conforming to the Model Context Protocol (MCP) using JSON-RPC specs for communication, including message sessions and tool invocation. - **Leveraging LLMs for Development**: The video demonstrates how to utilize LLMs (like those in AI-assisted IDEs) with the MCP documentation to accelerate server development. Copying the URL of website documentation enables pulling in documentation or SDK readme into IDE. - **Dockerizing and Deploying the Server**: The video guides the viewer through Dockerizing the MCP SSE server using a Dockerfile and Docker Compose. It then showcases deployment via Render, highlighting configuration steps like setting root directories and environment variables. - **Testing and Integration**: The video demonstrates how to test the server using an inspector tool to examine available tools and call functions. It also shows how to connect the deployed server to an n8n custom node for seamless integration. - **MCP Specification Deep Dive**: Explanation of the MCP specification, including the architecture with server endpoints, message sessions, tool listing, and invocation process using JSON-RPC. --- ### Integrate Model Context Protocol (#mcp) in n8n **Published:** 3/13/2025 **Duration:** 16 minutes **YouTube:** https://youtube.com/watch?v=1t8DQL-jUJk **Full LLM Text:** /video/cmis2us63000p04kw8fruache/llm.txt **Description:** Join the Community: https://nas.io/vibe-coding-retreat 50% off One-click remote hosted MCP servers use NERDINGIO at Supermachine.ai https://supermachine.ai/ 📞 Book a Call: https://calendar.app.google/vx62Asp9DTk7dRLW7 In this video, I'll introduce a new n8n community node I built that allows you to integrate Model Context Protocol (MCP) into your n8n workflows. What is MCP? MCP is a protocol that enables AI models to interact with external tools, APIs, and data sources in a standardized way. With this new n8n node, you can: ✅ Connect to MCP servers directly within n8n ✅ Execute AI tools and access external resources ✅ Send prompts to AI models and receive structured responses ✅ Automate AI-powered workflows without writing custom code 💡 Why This Matters This node makes it easier than ever to integrate LLMs, AI agents, and external APIs into your automation workflows—expanding what’s possible with n8n and AI-powered automation. 🔧 What You’ll Learn in This Video: 📌 How MCP works and why it’s important for AI-driven workflows 📌 Setting up the n8n community node for MCP 📌 Connecting to an MCP server and interacting with AI models 📌 Executing tools and retrieving AI-generated data 📌 Building end-to-end AI automation with n8n 👉🏻 Text Yourself: https://textyourself.app 📰 Newsletter: https://sendfox.com/nerdingio 🎥 Chapters 00:00 Introduction 00:23 What is MCP 02:28 Config - Important! 03:18 Install 04:08 Credentials 06:06 Command Line Example 07:41 AI Agent 10:53 Server Side Events 14:43 Multiple tools 16:22 Conculsion 🔗 Links Source: https://github.com/nerding-io/n8n-nodes-mcp Registry: https://www.npmjs.com/package/n8n-nodes-mcp ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## n8n & Model Context Protocol (MCP) Integration: Video Summary **1. Executive Summary:** This video introduces a new n8n community node developed by JD (NerdingIO) that enables seamless integration of Model Context Protocol (MCP) into n8n workflows. By using this node, users can easily connect to MCP servers, execute AI tools, access external resources, and automate AI-powered workflows without extensive custom coding. **2. Main Topics Covered:** * **Introduction to MCP:** Defining MCP as a standard for AI models to interact with external tools, APIs, and data sources. * **n8n Community Node Setup:** Step-by-step guide on installing and configuring the n8n community node for MCP, including enabling community packages. * **MCP Server Connection:** Demonstrating connection to MCP servers using both command-line (standard in/standard out) and Server-Sent Events (SSSE) methods within n8n. * **Workflow Examples:** Building workflows to list available tools from MCP servers and executing tools to retrieve AI-generated data, specifically using GitHub and Brave search examples. * **AI Agent Integration:** Incorporating the MCP node as a tool within n8n AI agents, allowing agents to access and execute tools defined by MCP servers. **3. Key Takeaways:** * MCP simplifies integration of diverse data sources and prompts into AI applications by acting as "Lego blocks." * The n8n community node offers a user-friendly way to connect to MCP servers and leverage AI tools within n8n workflows. * Enabling community packages within n8n is essential for utilizing the MCP node, especially with AI agents; environment variables must be properly configured. * The node supports both command-line and Server-Sent Events (SSSE) for connecting to MCP servers. * AI agents in n8n can utilize the MCP node to access and execute tools defined by MCP servers, enabling complex tasks such as repository searching or web searches. **4. Notable Quotes or Examples:** * "mCP is model context protocol and basically what that is is it's a protocol and a standard that allows you to interact with different servers and data sources and prompts and pull them into your AI application whether that be an agent or or uh some other a model." * "It [MCP] almost acts as like Lego blocks where you can take pieces or different servers and different tools prompts resources pull them into your code and uh Plug and Play very quickly." * "In na specifically since this is a community package you actually have to allow Community package for Tool usage if you want to use this in an AI agent and a tool." * Example workflows include querying GitHub repositories using an MCP connection and querying Brave Search to find local tools. * Demonstration of passing dynamically defined parameters to tools through the AI agent to make the process fully automated. **5. Target Audience:** * n8n users looking to integrate AI models and external data sources into their automation workflows. * Developers and data scientists interested in leveraging Model Context Protocol (MCP) for AI applications. * Individuals exploring AI agent development and integration within automation platforms like n8n. * Anyone looking to automate AI-powered tasks without requiring extensive coding knowledge. **Key Highlights:** - **mCP: Lego Blocks for AI**: mCP (Model Context Protocol) is a standard enabling plug-and-play integration of diverse data sources and prompts into AI applications, simplifying complex workflows. It allows combining different servers, tools and resources into AI workflows. - **n8n + mCP Node = Automation Power**: The custom n8n node facilitates direct interaction with mCP servers, enabling various automation scenarios within the n8n platform. It enables n8n to leverage data accessed through mCP connections. - **Community Package Setup is Key**: Enabling community packages within n8n is crucial for utilizing the mCP node, especially when incorporating it as a tool within AI agents. Environment variables must be set correctly for community tool usage. - **SSSE vs. Command Line**: The video demonstrates connecting to mCP servers using both command-line and Server-Sent Events (SSSE) methods, showcasing the flexibility of the n8n node to handle different connection types. - **AI Agent Integration with mCP**: The mCP node allows AI agents within n8n to access and execute tools defined by mCP servers, enabling sophisticated tasks such as searching repositories or using web search, with parameter handling by the model. --- ### How Gibberlink works locally **Published:** 3/5/2025 **Duration:** 13 minutes **YouTube:** https://youtube.com/watch?v=Dmtl9aSZyKU **Full LLM Text:** /video/cmis2v46w000q04kwgokb260l/llm.txt **Description:** What happens when you let AI agents talk to each other using ElevenLabs' conversational voices and then switch to GGWave for ultrasonic data transfer? A fully AI-driven conversation system that can: ✅ Generate lifelike AI voices using ElevenLabs ✅ Facilitate agent-to-agent communication with LLMs ✅ Seamlessly switch to ultrasonic transmission via GGWave ✅ Run in a Next.js app leveraging LLM tools 💡 Why This Matters This setup allows for autonomous, dynamic conversations between AI agents, audible to humans but also transmittable via sound using GGWave's ultrasonic encoding. 👉🏻 Text Yourself: https://textyourself.app 📰 Newsletter: https://sendfox.com/nerdingio 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 00:27 Demo 01:50 Source 03:40 Eleven Labs 06:49 NextJS 13:02 Conculsion 🔗 Links Source: https://github.com/PennyroyalTea/gibberlink ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## Gibberlink Video Summary **1. Executive Summary:** This video dissects the Gibberlink demo, which showcases autonomous AI agent conversations using ElevenLabs for lifelike voice and switching to GGWave for ultrasonic data transfer. The video details the technology stack, setup process, and code structure, clarifying how the system achieves seamless transitions between audible conversations and inaudible data transmission. **2. Main Topics Covered:** * **Introduction to Gibberlink:** Overview of the Gibberlink demo and its core technologies (ElevenLabs, GGWave, Next.js). * **Agent Setup in ElevenLabs:** Creating and configuring AI agents in ElevenLabs, including system prompts and custom tool definition ("jib_mode"). * **Local Execution with Grock Tunneling:** Running the demo locally using `npm run dev` and exposing it publicly via Grock to simulate a caller/callee setup. * **Switching to GGWave via Function Call:** Explaining how the "jib_mode" function call, triggered by agent agreement, initiates the switch to ultrasonic communication, processing the entire LLM chat thread. * **Code Walkthrough:** Overview of the key code segments handling message processing, agent communication, and GGWave integration within the Next.js application. **3. Key Takeaways:** * Gibberlink leverages ElevenLabs for realistic AI voices, enabling dynamic conversations between agents. * The system can seamlessly transition to ultrasonic data transfer via GGWave when agents mutually agree. * The demo is built using a Next.js frontend, ElevenLabs agents, and GGWave for audio communication, demonstrating a practical application of LLMs and audio tech. * Grock provides a method of connecting the locally run program to a URL that can be accessed elsewhere. * The key to the system is the 11Labs custom tool "jib_mode", which is a function call that allows you to connect to your ggWave instance after the AI agents have agreed to use it. * The whole previous chat thread is passed along to GGWave when the LLM agents both agree to switch modes. **4. Notable Quotes or Examples:** * "The core technologies that it's built on are GGWave and 11 labs and then they're using nextjs to actually build out the uh the user interface that you're seeing." * "[Gibberlink uses] client side llm functions to then call GG wve and actually have that communication continue through the llm red right." * "[The 'jib_mode' tool] is no different uh than doing a tool in vapy or doing a tool in Lan graph or Lane chain ... they work very similar." * "We are taking the ENT enre llm chat and assigning all of those roles and then we're actually sending that as part of the conversation moving forward saying that this is the latest message." **5. Target Audience:** * Developers interested in AI agent communication and voice synthesis * Individuals curious about ultrasonic data transmission and its applications * Programmers exploring Next.js, ElevenLabs, and GGWave integration * Those looking for practical examples of LLM tool usage in conversational AI systems **Key Highlights:** - **Gibberlink Tech Stack**: Gibberlink demo leverages Next.js for the UI, 11 Labs for conversational AI agents, and GGwave for audio data communication. The name is mostly related to marketing. - **Agent Setup in 11 Labs**: Creating agents in 11 Labs involves defining a system prompt and configuring a custom tool ('jib_mode') for switching to GGwave communication via client-side functions. - **Local Execution with Grock Tunneling**: The demo runs locally with `npm run dev` on port 3003. Grock creates a public URL allowing two agents to interact, simulating a caller/callee setup. - **Switching to Gwave via Function Call**: The switch to GGwave happens via a function call in 11 Labs, triggered when both agents agree to 'gibber link' mode. The entire llm chat thread then gets processed. --- ### Create a Web AI Memory Game in the Browser **Published:** 2/26/2025 **Duration:** 15 minutes **YouTube:** https://youtube.com/watch?v=2MXk0YdrDsk **Full LLM Text:** /video/cmis2vewh000r04kw6dyr84yo/llm.txt **Description:** In this tutorial, we dive into how you can build an AI-powered memory game that runs entirely in the browser—no backend required! Using WebGPU, we load and cache Gemma 2 (a 2-billion parameter LLM) directly in the browser, enabling offline AI processing for interactive gameplay. 🔥 What You’ll Learn: - How to run AI models locally in the browser using WebGPU - Implementing LLMs for game logic without backend calls - Creating a custom memory game from markdown notes - Integrating speech recognition and file parsing into a web app - Optimizing browser cache storage for AI models 💡 Whether you’re an AI engineer, web developer, or just a tech enthusiast, this guide will show you how to harness client-side AI to create interactive, privacy-first applications. 👉🏻 Text Yourself: https://textyourself.app 📰 Newsletter: https://sendfox.com/nerdingio 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 00:20 Background 01:50 Demo 05:47 Code 14:26 Cache 15:17 Conclusion 🔗 Links Source: https://github.com/nerding-io/memgame-webai Jason Mayes: https://github.com/jasonmayes/WebAIAgent ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** Here's a summary document for the provided video information: **Video Title: Create a Web AI Memory Game in the Browser** **1. Executive Summary:** This tutorial demonstrates how to build an AI-powered memory game that runs entirely in the browser using WebGPU and a cached LLM (Gemma 2). It showcases the possibilities of client-side AI processing for interactive gameplay, offering a privacy-first and offline experience. **2. Main Topics Covered:** * **Offline AI Implementation:** Running large language models (LLMs) locally in the browser. * **WebGPU Utilization:** Using WebGPU for efficient client-side AI inference with Gemma 2 (a 2 billion parameter model). * **Custom Game Creation:** Generating memory game pairs and explanations from uploaded markdown files or voice input. * **Browser Caching:** Storing large AI models in the browser cache for offline use and reduced download frequency. * **Speech Recognition Integration:** Using voice for game input. * **File Parsing:** Reading and processing markdown files for game creation. * **Model Loading & Initialization:** Steps for initializing the LLM and checking for local/remote availability. * **UI Updates:** How to update the UI and provide user feedback based on file loading progress. **3. Key Takeaways:** * AI models can be effectively run directly in the browser using WebGPU, eliminating the need for backend calls during gameplay. * Browser caching allows for persistent storage of large AI models, enabling offline functionality and improving performance. * Users can create personalized gaming experiences by uploading custom content, which is then processed by the LLM to generate unique memory game pairs. * Client-side AI offers privacy benefits as no user data is sent to a backend server after the initial model load. * The demo showcases a fully functional web-based AI memory game, developed with HTML, CSS, and JavaScript, demonstrating the ease of integrating AI directly into a web page without a backend. **4. Notable Quotes or Examples:** * "…you can actually run a model in the browser and locally where it'll cach it offline and then you can actually use it." * "This is great for a couple of different reasons...first and foremost it's free it's uh private..." * "Generating a Json object of a unique pair of uh matching and an explanation of why they match right so that's for the hints as well as the reasoning behind it" * Mention of Jason Mayes' file proxy cache as critical to making the Gemma model accessible and usable for client-side web AI. **5. Target Audience:** * AI Engineers * Web Developers * JavaScript Developers * Tech Enthusiasts interested in client-side AI * Individuals interested in privacy-focused applications **Key Highlights:** - **Offline AI Memory Game in Browser**: The presenter demonstrates a memory game that runs entirely in the browser, leveraging a large language model (LLM) cached locally for offline functionality and privacy. No backend communication is required after initial load. - **Gemma 2 LLM Integration with WebGPU**: The game utilizes the Gemma 2 (2 billion parameter) LLM, loaded and processed using web GPU for efficient client-side inference. The model is fetched remotely initially and then cached in the browser for subsequent use. - **Custom Game Creation via File Upload**: Users can create custom memory games by uploading markdown files or using voice input. The LLM then parses the content to generate matching pairs and explanations dynamically. - **Leveraging Browser Cache for Model Storage**: The video showcases how browser caching can be used to store large AI models locally, enabling offline use and reducing the need for repeated downloads. This approach optimizes performance and reduces server load. - **File Proxy Cache by Jason Mae**: The presenter acknowledges Jason Mae's file proxy cache as critical to making the Gemma model accessible and usable, highlighting Jason's work in web AI client-side information processing. --- ### Setup a Custom Voice in Hume & Integrate It with Flutter **Published:** 2/17/2025 **Duration:** 11 minutes **YouTube:** https://youtube.com/watch?v=b-LYVfgJj8Q **Full LLM Text:** /video/cmis2vpzd000s04kw1mu8lrpq/llm.txt **Description:** Unlock the power of custom voices with Hume and bring them to life in your Flutter app! In this step-by-step tutorial, we walk you through setting up a custom voice in Hume and integrating it seamlessly into a Flutter project. This video covers: - Hume Setup: How to create and configure your custom voice. - Flutter Integration: Connecting your Flutter app to Hume’s API and using your custom voice. - Live Demo: Watch as we build and test the integration in real-time. Whether you're developing voice-enabled applications or just exploring innovative ways to enhance user experience, this guide provides all the tools and tips you need to get started. 👉🏻 Text Yourself: https://textyourself.app 📰 Newsletter: https://sendfox.com/nerdingio 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 00:29 Dashboard 01:37 API 02:31 Custom Voice 05:27 Flutter 08:44 Demo 🔗 Links https://www.hume.ai/ https://dev.hume.ai/docs/empathic-voice-interface-evi/voices https://github.com/HumeAI/hume-api-examples/tree/main/evi-flutter-example ⤵️ Let's Connect https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://everefficient.ai https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## Video Summary: Setup a Custom Voice in Hume & Integrate It with Flutter **1. Executive Summary:** This tutorial provides a step-by-step guide on creating a custom voice using Hume's platform and integrating it into a Flutter application. The video demonstrates both the Hume dashboard interface and API functionalities for voice creation and configuration, alongside the Flutter integration process using websockets for text-to-speech conversion. **2. Main Topics Covered:** * **Hume Custom Voice Creation:** Using the Hume dashboard and API to create and configure custom voices, including attribute adjustments (masculinity, assertiveness, etc.) and voice testing. * **Hume Configuration:** Creation or updating of Hume configurations to utilize custom voices, including selecting the desired voice and language model. * **Flutter Integration:** Downloading and setting up a pre-built Flutter example project provided by Hume for integrating with the custom voice. * **Environment Setup:** Configuring the Flutter app with the necessary Hume API keys and configuration ID obtained from the Hume dashboard. * **Real-Time Demo:** Showcasing a live demo of the Flutter application interacting with the Hume custom voice API to generate text-to-speech output. **3. Key Takeaways:** * **Custom Voice Creation Ease:** Hume offers a user-friendly interface and API for creating custom voices by adjusting attributes and even creating new versions of existing voices. * **Simplified Flutter Integration:** Hume provides a complete Flutter example, simplifying the integration process and allowing developers to quickly implement custom voice functionality. * **Configuration is Key:** Properly configuring the Hume setup and Flutter app with the correct API keys and configuration ID is crucial for successful integration. * **Versatile SDK Support:** Hume supports various SDKs including Flutter, Next.js, Python, and React Native, showcasing its wide range of applications and ease of use. **4. Notable Quotes or Examples:** * *"...you can actually create an a new version of the existing voice so take that voice and then uh change the parameters based on what you've already been using so it doesn't necessarily edit it it's just creating a new version which I thought was super interesting..."* - Highlights the versioning capability for custom voices. * The video walks through selecting different attributes for the voice, such as masculinity, assertiveness, calmness, and buoyancy, and testing the voice based on these attributes. * The presenter demonstrates how to grab the config ID from the Hume dashboard and place it into the environment variables of the Flutter project. * The live demo displays how the Flutter application takes text input and uses the configured custom voice to generate spoken audio. **5. Target Audience:** * Flutter developers interested in incorporating custom voice functionality into their applications. * Developers exploring text-to-speech solutions and looking to leverage Hume's platform. * Individuals interested in creating and configuring custom AI voices for various applications. * Developers looking to enhance user experience through voice-enabled features in their apps. **Key Highlights:** - **Hume Custom Voice Creation**: Demonstrates how to create a custom voice using Hume's dashboard and API, highlighting the ability to adjust voice attributes and create new versions of existing voices. - **Flutter Integration Example**: Shows how to integrate a Hume custom voice into a Flutter application, leveraging websockets for text-to-speech functionality. Includes setup and dependency installation instructions. - **Configuration and Environment Variables**: Explains how to configure the Hume integration by using the configuration ID and necessary environment variables, showing where to obtain and apply the keys. - **SDK Availability**: Highlights Hume's wide SDK support, including Flutter, Next.js, Python, and React Native, emphasizing its ease of use and comprehensive documentation. --- ### Setup Deep Research: Free, Private & Open Source **Published:** 2/11/2025 **Duration:** 14 minutes **YouTube:** https://youtube.com/watch?v=sGAThYw_BsQ **Full LLM Text:** /video/cmis2w2of000t04kwy162nhxf/llm.txt **Description:** Discover how to deploy a fully open source implementation of OpenAI's groundbreaking Deep Research agent—all for free and with complete privacy! In this step-by-step guide, we’ll show you how to harness the same advanced research capabilities without the hefty $200 price tag. Plus, customize the agent’s behavior with adjustable breadth and depth settings so it auto-adjusts whether you run it for 5 minutes or 5 hours. 👉🏻 Text Yourself: https://textyourself.app 📰 Newsletter: https://sendfox.com/nerdingio 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 01:00 Deep Research 02:42 Setup 05:03 Code 09:52 Result 10:31 Bonus: API 13:02 Human in the Loop 14:23 Conclusion 🔗 Links https://x.com/dzhng/status/1886603396578484630 https://github.com/dzhng/deep-research https://www.firecrawl.dev/ https://www.inngest.com/docs/features/inngest-functions/steps-workflows/wait-for-event ⤵️ Let's Connect https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://everefficient.ai https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## Video Summary: Setup Deep Research: Free, Private & Open Source **1. Executive Summary:** This video demonstrates how to deploy a free, open-source alternative to OpenAI's Deep Research agent using tools like Firecrawl and Vercel AI SDK. The tutorial covers setup, customization (breadth, depth), integration with OpenAI-compatible APIs (like Llama or DeepSeek), and converting the terminal-based tool into an API with potential human-in-the-loop interactions. **2. Main Topics Covered:** * **Introduction to Open-Source Deep Research Alternative:** Overview of an open-source implementation of Deep Research, avoiding the paid version's cost. * **Deep Research Functionality:** Explanation of the agent's architecture, including depth/breadth parameters, user query handling, follow-up questions, search process, and markdown report generation. * **Setup and Code Walkthrough:** Step-by-step guide to cloning the project, installing dependencies, and configuring environment variables (API keys for Firecrawl and OpenAI). * **Customization and OpenAI Compatibility:** Demonstrates how to point the agent to various OpenAI-compatible APIs, allowing the use of open-source models like Llama or DeepSeek. * **Terminal Execution and Results:** Running the Deep Research agent in the terminal, observing the interaction, and analyzing the generated markdown report. * **API Conversion (Bonus):** Creating an API endpoint using Express to access the Deep Research functionality, addressing the challenge of dynamic follow-up questions. * **Human-in-the-Loop Integration:** Exploring the use of event-driven systems (e.g., Inngest) and webhooks (e.g., Slack) to enable human interaction for answering follow-up questions in the API context. **3. Key Takeaways:** * A fully functional, cost-free, and private alternative to OpenAI's Deep Research is achievable using open-source tools. * Vercel's AI SDK can be effectively used outside of the Vercel platform for building AI applications. * The agent is highly customizable with adjustable breadth and depth settings for controlling research intensity and duration. * The agent can integrate with various OpenAI-compatible APIs, allowing for the use of different language models, including open-source options. * Transforming a terminal-based tool into an API requires careful consideration of interactive prompts and can be enhanced with human-in-the-loop mechanisms. **4. Notable Quotes or Examples:** * "What I thought was interesting is that you can run it for 5 minutes or 5 hours um and it'll just keep keep running and that that's pretty interesting to me." * "One of the things that I really love about fir crawl is you know they actually have this new feature called extract but it basically takes uh ser and puts it in into like an llm ready format." * "What's also interesting about this is you can see down here you can actually point this at any really any open aai compatibility API so you could use llama you could technically use deep seek." * [Regarding turning the tool into an API] "...we don't actually know what those follow-up answers are going to be so how would it actually handle that?" * "You could actually use wait for event in ingest...where you could actually wait for an event to be triggered that would say that uh this follow-up question was answered." **5. Target Audience:** * Developers and AI enthusiasts interested in open-source alternatives to commercial AI tools. * Individuals seeking cost-effective and private solutions for conducting in-depth research using AI. * Those familiar with JavaScript, APIs, and cloud deployment concepts. * Individuals exploring ways to integrate human interaction with AI-powered applications. **Key Highlights:** - **Open Source Deep Research Alternative**: Highlights an open-source implementation of Deep Research, offering a cost-effective alternative to the paid version and leveraging tools like firecrawl. - **Versatile AI SDK Beyond Vercel**: Showcases the unexpected usage of Vercel's AI SDK independently, outside of the Vercel platform, for building AI-powered applications. - **OpenAI Compatibility for Custom Models**: Demonstrates the flexibility to use different OpenAI-compatible APIs, enabling the integration of open-source models like Llama or DeepSeek for research tasks. - **From Terminal to API**: Explores the process of converting a terminal-based research tool into an API using Express, focusing on handling interactive prompts and potential human-in-the-loop integration. - **Human-in-the-Loop for API Interactions**: Suggests using event-driven systems (e.g., Ingest) and webhooks (e.g., Slack) to introduce human interaction for answering dynamic follow-up questions in the API. --- ### How to create a daily Reddit Trend Finder with #langgraph #deepseek #slack #n8n **Published:** 2/3/2025 **Duration:** 17 minutes **YouTube:** https://youtube.com/watch?v=usUTnRYloi8 **Full LLM Text:** /video/cmis2wep4000u04kwzn6mwlsn/llm.txt **Description:** Discover how to build an AI-powered Reddit trend analyzer that automatically detects viral content, leverages Slack emoji reactions for human approval, and triggers n8n workflows to post content—all in real time! Perfect for developers, marketers, and automation enthusiasts. - Track trending Reddit posts using LangGraph for NLP-powered trend analysis. - Integrate Slack as a "human-in-the-loop" to approve/reject content with emoji reactions (✅ for approve, ❌ for reject). - Automatically trigger n8n workflows to post approved content to social media, blogs, or databases. - Combine AI efficiency with human judgment for smarter content curation. 🔧 Key Tools & Technologies - LangGraph: Analyze Reddit posts for trends, keywords, and sentiment. - Slack API: Set up emoji-triggered actions for human moderation. - n8n: Build no-code automations to publish content instantly. - Python: Script the backend logic to connect Reddit, Slack, and n8n. 📌 What You’ll Learn - LangGraph Setup: Scrape and analyze Reddit data in real time. - Slack Integration: Configure emoji reactions to trigger approval workflows. - Human-in-the-Loop Design: Let teams vote on content before publishing. - n8n Automation: Post approved trends to platforms like Twitter, Discord, or CMS tools. - Cron to setup daily recurring research task 👉🏻 Text Yourself: https://textyourself.app 📰 Newsletter: https://sendfox.com/nerdingio 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 00:44 Langgraph 04:46 Studio 07:08 Slack 08:32 n8n 15:25 Cron 17:36 Conclusion 🔗 Links https://github.com/langchain-ai/reddit-radar https://smith.langchain.com/ https://slack.com/ http://n8n.io/ ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## Video Summary: Reddit Trend Finder with LangGraph, Slack, and n8n **1. Executive Summary:** This video demonstrates how to build an automated Reddit trend analysis system using LangGraph for trend detection, Slack for human approval, and n8n for automated posting. The system empowers users to identify trending topics, curate content, and schedule posts to various social media platforms, all driven by AI and user input. **2. Main Topics Covered:** * **LangGraph Setup:** Configuring LangGraph to scrape Reddit data, filter posts and comments based on user-defined criteria, and analyze content for trends using Large Language Models (LLMs). Specifically leveraging the pre-built `reddit-radar` project, customized for specific use cases. * **Slack Integration:** Utilizing the Slack API to post Reddit trends in a structured block format. Configuring emoji reactions (e.g., checkmark, X, bird, clock) as triggers for automated actions. * **Human-in-the-Loop Design:** Implementing a content approval workflow within Slack, allowing users to review AI-generated trends and approve or reject them using emoji reactions. * **n8n Automation:** Building no-code workflows in n8n to respond to Slack emoji reactions. This includes parsing Slack block data (subreddit, post ID) to re-query Reddit API data. Performing actions based on user-defined emoji triggers (e.g., posting to Twitter/X as a thread, LinkedIn, etc.). DeepSeek is integrated and used within the N8N workflow for content summary and enhancement. * **Scheduled Trend Discovery:** Setting up Cron jobs within LangGraph to automatically run the Reddit trend finder on a daily schedule and push results to Slack. This automation ensures consistent trend monitoring without manual intervention. **3. Key Takeaways:** * **AI-Powered Trend Analysis:** LangGraph provides a powerful tool for automated Reddit trend identification. * **Human Oversight is Critical:** Integrating Slack for human-in-the-loop moderation ensures content quality and alignment with brand guidelines. * **Automated Content Curation:** n8n enables seamless automation of posting approved trends to various platforms, saving time and effort. * **Slack Blocks for Data Parsing:** Utilizing Slack blocks provides a structured format for sending information and parsing it within n8n workflows, allowing for easy extraction of relevant data. * **DeepSeek for Improved Processing:** Leverage DeepSeek language models within N8N to improve text analysis and summarization. * **Cron jobs for consistency:** Cron jobs ensure daily or schedule based tasks for consistant content. **4. Notable Quotes or Examples:** * "Blocks are it's still a single message but it almost chunks those blocks into um sections in your slack message so it's it's really just blocks inside of a message even though it it compiles into one one big message." - Explaining the importance of Slack blocks for structured data within messages. * "We are going to trigger an event and that trigger is going to be on a reaction added." - Describing the n8n trigger based on Slack emoji reactions. * Example of using emojis (bird, thread, silhouette, clock) to trigger posting to Twitter, Threads, LinkedIn, or schedule at a certain time. * Demonstrates how to configure and modify the LLM that is being utilized within the N8N workflow. **5. Target Audience:** * Developers interested in building AI-powered automation solutions. * Marketers looking to identify trending topics and automate content curation. * Automation enthusiasts seeking to integrate LangGraph, Slack, and n8n. * Anyone seeking to enhance their social media presence and content scheduling strategies. **Key Highlights:** - **Reddit Trend Finder with LangGraph & LLMs**: The system uses LangGraph to monitor Reddit trends, leveraging LLMs (like DeepSeek) to analyze posts and comments for relevant information based on user-defined criteria. It filters the vast amount of data to pinpoint user interests. - **Human-in-the-Loop via Slack Blocks**: The system posts Reddit trends to Slack using formatted 'blocks' within a single message. This structure makes it easy to parse data programmatically in N8N, enabling 'human-in-the-loop' approval and actions on selected trends. - **Automated Actions with N8N and Slack Emojis**: N8N is used to trigger automated actions (e.g., posting to Twitter threads) based on emoji reactions in Slack. This creates a seamless workflow where users can easily act on interesting Reddit trends. DeepSeek is integrated in N8N. - **Scheduled Trend Discovery with Cron Jobs**: LangGraph offers cron job capabilities to automatically run the Reddit trend finder on a schedule. This automates the process, ensuring a daily digest of relevant trends is delivered without manual intervention. Can overwrite defaults. --- ### How to use Chrome's Prompt API in Extensions **Published:** 1/2/2025 **Duration:** 10 minutes **YouTube:** https://youtube.com/watch?v=HjPQ3hyeXQI **Full LLM Text:** /video/cmis2wu6z000v04kw393fflat/llm.txt **Description:** Uncover the latest features of Chrome's built-in Prompt API and learn how to harness its capabilities in your own browser extension. In this hands-on tutorial, you'll explore: What the Chrome Prompt API offers and why it's a game-changer. - Step-by-step guidance to integrate the API into a custom Chrome extension. - Practical examples showcasing how to create dynamic, user-friendly prompts. Whether you're an experienced developer or just diving into Chrome extensions, this video will help you elevate your projects with the power of the Prompt API. 🚀 👉🏻 Text Yourself: https://textyourself.app 📰 Newsletter: https://sendfox.com/nerdingio 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 00:37 Docs 01:31 Setup 03:37 Experimental API 04:56 Trial Origin 05:39 Code 07:36 Demo 🔗 Links https://developer.chrome.com/docs/extensions/ai/prompt-api https://github.com/GoogleChrome/chrome-extensions-samples/tree/main/functional-samples/ai.gemini-on-device https://googlechrome.github.io/OriginTrials/developer-guide.html ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** Here's a comprehensive summary document of the video, designed for quick understanding: **Document Title: Chrome Prompt API for Extensions: A Quick Guide** **1. Executive Summary:** This video provides a hands-on tutorial for developers on integrating Chrome's Prompt API into browser extensions. It covers the setup process, code walkthrough, and demonstrates a practical application of using Gemini (via the API) to generate AI-powered jokes. **2. Main Topics Covered:** * **Introduction to the Chrome Prompt API:** Explanation of what the API is, its capabilities, and why it is valuable for extension development. * **Setting Up the Extension:** Step-by-step instructions on how to download an example extension from GitHub, enable experimental APIs in Chrome, and manage extensions in the browser. * **Origin Trial Registration:** Detailed process on applying for and obtaining an origin trial token, crucial for accessing the Prompt API's functionality. * **Code Walkthrough:** Explanation of the extension's code structure, including the manifest file, HTML/JavaScript components, and API calls for creating sessions and sending prompts to Gemini. * **Practical Example (AI Joke Generator):** Demonstration of a working extension that takes user input and leverages the Prompt API to generate jokes, highlighting the API's potential. * **Potential Use Cases:** Discussion of advanced applications like automated calendar event extraction and integration with Google Calendar using AI-powered prompts. **3. Key Takeaways:** * The Chrome Prompt API enables developers to leverage AI models like Gemini directly within browser extensions. * Setting up the extension requires both enabling experimental APIs and registering for an origin trial to get a token. * The API allows developers to define system prompts and set parameters like temperature and top K to control the AI's output. * The example extension showcased a simple yet powerful use case: generating AI jokes based on user input. * Future applications could include automating tasks such as extracting and adding calendar events or performing content extraction. **4. Notable Quotes or Examples:** * "*Last time we specifically looked at some experimental apis for the web now what we're going to do today is we're actually going to take a look at the prompt API and see how you can actually integrate it into Chrome extensions*": Introduces the purpose of the video. * "*Tell me a joke about AI in the Chrome browser*": Example prompt used in the joke generator extension. * "*Why did choma get fired from pizza delivery job tried to make a recommendations on toping see never put pineapple on Pizza it's terrible*": Example of a generated joke from the extension. * "*you can actually develop a Chrome extension that would take calendar events and uh basically copy that that text and actually put it directly into Google Calendar*": Illustrates a potential advanced use case. **5. Target Audience:** * Chrome extension developers * Web developers interested in AI integration * Individuals exploring the capabilities of the Chrome Prompt API * Developers looking for practical examples of AI-powered browser extensions **Key Highlights:** - **Chrome's Prompt API for Extensions**: The video demonstrates how to integrate Chrome's Prompt API into extensions, using Gemini for AI tasks within the browser. - **Setting Up the Extension & Origin Trial**: The video details the necessary steps, including downloading an example extension, enabling experimental APIs, and registering for an origin trial to get the API token. - **Code Walkthrough & Functionality**: Explanation of the code structure, including setting prompt parameters (temperature, top K) and using the API to create sessions and send prompts. - **Practical Example: AI Joke Generator**: The video shows a working example of an extension that uses Gemini to generate jokes based on user input, demonstrating the API's potential. - **Potential Use Cases**: Discusses additional applications like extracting calendar events from text and integrating them into Google Calendar using AI-powered prompts. --- ### Set Up Genesis Locally: Power Your Robotics and AI Simulations **Published:** 12/26/2024 **Duration:** 17 minutes **YouTube:** https://youtube.com/watch?v=eEZp7yuup0U **Full LLM Text:** /video/cmis2x5ny000w04kw4r9glpum/llm.txt **Description:** Discover how to install Genesis, a cutting-edge physics engine designed for robotics and AI simulations, in this detailed guide. Genesis is an open-source platform offering advanced physics simulation, motion planning, and photo-realistic rendering, all optimized for cross-platform use on Linux, macOS, and Windows. This tutorial covers: - Prerequisites: Preparing your system with Python 3.9+ and PyTorch. - Core Installation: Easily install Genesis with a single pip command. - Examples: Enable motion planning, fluid simulation, drones, back flipping robots and soft robotics Whether you're developing robotics projects or exploring embodied AI, this video equips you to leverage Genesis for high-performance, realistic simulations on your hardware. Links to documentation and additional tools are included in the description! 👉🏻 Text Yourself: https://textyourself.app 📰 Newsletter: https://sendfox.com/nerdingio 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 01:18 Setup 02:55 Enironment 04:17 Render on Mac OS 06:57 Fluid Movement 09:11 Drone 10:40 Backflip 14:09 Bonus: Soft Robots 🔗 Links https://github.com/Genesis-Embodied-AI/Genesis https://genesis-world.readthedocs.io/en/latest/user_guide/overview/installation.html https://pytorch.org/get-started/locally/ https://genesis-world.readthedocs.io/en/latest/api_reference/index.html ⤵️ Let's Connect https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://everefficient.ai https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## Genesis Physics Engine Setup: Video Summary **1. Executive Summary:** This video provides a tutorial on installing and using Genesis, an open-source physics engine for robotics and AI simulations, focusing on setting it up locally on macOS. The guide covers necessary configurations, showcases various examples including fluid simulation, drones, backflipping robots, and soft robotics, and emphasizes the importance of documentation. **2. Main Topics Covered:** * **Introduction to Genesis:** Overview of the Genesis physics engine and its capabilities for simulating robotics, AI applications, and other physics-based environments. * **Prerequisites and Installation:** Step-by-step guide to installing Genesis, including Python 3.9+ and PyTorch setup, and using `pip install`. * **macOS Configuration:** Specific configurations needed for running Genesis locally on macOS, especially using the CPU backend and running the visualizer in a separate thread. * **Example Demonstrations:** Showcasing various examples provided by Genesis, including fluid simulation, drone flight trajectory simulation, backflipping robot simulation, and soft robotics (worm) simulation. * **Documentation and API Reference:** Highlighting the importance of consulting the Genesis documentation and API reference for successful implementation and customization. **3. Key Takeaways:** * Genesis is a powerful, cross-platform physics engine useful for robotics and AI simulations. * Local macOS setup requires specific configurations: `backend` to `GS CPU`, `visualization` parameter enabled, and simulation run in a separate thread. * Genesis offers a wide range of example simulations, including robots, liquids, and soft bodies, providing a good starting point for experimentation. * Understanding the documentation and API reference is crucial for customizing and troubleshooting Genesis. * Soft robotics simulation, exemplified by the 'worm' example, demonstrates the advanced capabilities of Genesis. **4. Notable Quotes or Examples:** * "Genesis is a physics engine that you can actually test different things like Robotics and even AI applications." * "Setting up Genesis locally on a Mac requires specific configurations, especially specifying CPU backend instead of GPU, and running the visualizer in a separate thread." * Key configurations include: setting 'backend' to 'GS CPU', utilizing the visualization parameter, and executing the simulation in a separate thread for Mac OS. * "Genesis allows simulation of Soft Robotics which are notoriously difficult. The 'worm' example demonstrates muscle expansion and movement in a physics environment." * "Successfully running Genesis requires consulting the documentation, understanding the examples provided, and adapting them for your specific needs, particularly on different platforms." * Examples include simulating a drone following a flight trajectory, a robot performing a single or double backflip, and a soft robot worm moving within a bounded space. **5. Target Audience:** * Robotics researchers and developers * AI researchers and developers, particularly those interested in embodied AI * Students learning about physics simulation and robotics * Anyone interested in using a physics engine for simulating robots, liquids, and soft bodies. **Key Highlights:** - **Genesis Project Overview**: Genesis is a physics engine for robotics and AI simulation, allowing testing of interactions in a physics-based environment. It supports robots, liquids, and soft bodies. - **Local Mac Setup Hurdles**: Setting up Genesis locally on a Mac requires specific configurations, especially specifying CPU backend instead of GPU, and running the visualizer in a separate thread. - **Key Configuration Steps**: Essential configurations include: setting 'backend' to 'GS CPU', utilizing the visualization parameter, and executing the simulation in a separate thread for Mac OS. - **Soft Robotics Simulation**: Genesis allows simulation of Soft Robotics which are notoriously difficult. The 'worm' example demonstrates muscle expansion and movement in a physics environment. - **Importance of Documentation**: Successfully running Genesis requires consulting the documentation, understanding the examples provided, and adapting them for your specific needs, particularly on different platforms. --- ### How to Use Chrome’s Built-in #AI: Translation, Language Detection & Summary **Published:** 12/11/2024 **Duration:** 20 minutes **YouTube:** https://youtube.com/watch?v=8ybAFjH7bpM **Full LLM Text:** /video/cmis2xh1j000x04kwppo5arcl/llm.txt **Description:** Discover the latest features of Chrome's AI Assistant, including real-time transcription, advanced language detection, and powerful APIs. - AI assistance for styling - Translator API for seamless multilingual support. - Language Detector API for smart content customization. - Summarizer API for concise content insights. 📰 FREE Code & Resources: https://sendfox.com/nerdingio 👉🏻 Ranked #1 Product of the Day: https://www.producthunt.com/posts/ever-efficient-ai 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 00:37 Web Summit AI 01:45 AI Assistant 03:30 Built-in 08:31 Translation 13:20 Language Detection 16:37 Summary 🔗 Links https://www.youtube.com/playlist?list=PLNYkxOF6rcIAEVKJ98bDkQRkwvO4grhnt https://developer.chrome.com/docs/devtools/ai-assistance/styling https://developer.chrome.com/docs/ai/built-in#benefits-on-device https://developer.chrome.com/docs/ai/built-in-apis https://chrome.dev/web-ai-demos/translation-language-detection-api-playground/ https://chrome.dev/web-ai-demos/summarization-api-playground/ ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** Here's a summary document designed to quickly communicate the core content of the video. **Video Summary Document: How to Use Chrome's Built-in #AI: Translation, Language Detection & Summary** **1. Executive Summary:** This video explores the latest AI advancements integrated directly into the Chrome browser, focusing on the Gemini Nano APIs for translation, language detection, and summarization. The presenter demonstrates how developers can leverage these features locally within the browser via origin trials, enhancing security and reducing latency. **2. Main Topics Covered:** * **Introduction to Chrome's AI Capabilities:** Overview of the Web AI Summit and Google's efforts to integrate AI (specifically Gemini Nano) into Chrome. * **AI-Powered Styling Assistant:** Introduction to using AI for styling in Chrome's developer mode, allowing real-time AI collaboration for front-end development. (Requires enabling experimental flags.) * **Gemini Nano API Access via Origin Trials:** Detailed explanation of Chrome's exploratory APIs (Translator API, Language Detector API, Summarizer API) and how to access them through origin trials. * **Local AI Processing Benefits:** Advantages of running AI tasks locally within the browser, emphasizing security, reduced latency, and smaller model sizes. * **Practical Demonstrations:** Step-by-step instructions on enabling experiment flags in Chrome, registering for origin trials, and implementing the translation and summarization APIs using sample code. **3. Key Takeaways:** * Chrome is integrating Gemini directly into the browser, empowering developers with local AI capabilities. * The AI Styling Assistant in Developer Mode offers a co-pilot experience for front-end tasks. * Developers can access Gemini Nano for translation, language detection, and summarization through Chrome's exploratory APIs. * Origin Trials provide a way to test these APIs now, but require signing up for trials and enabling appropriate flags. * Local AI processing offers enhanced security, reduced latency, and potentially smaller, more efficient models. Each API (translator, language detector, summarizer) needs its own separate origin trial sign-up. **4. Notable Quotes or Examples:** * "*...Google's been releasing a lot of different features that in Chrome you can actually leverage uh Gemini and other different apis in the browser...*" * (Regarding local AI benefits) "*...security if we think about the fact that we're sending data over the wire..."* * "*...basically in a few lines of code we're able to translate directly in the web...*" (Demonstrates the simplicity of the translation API.) * Translation API code example: Uses Javascript to connect to the translator object and call the translate function. **5. Target Audience:** Web developers, front-end engineers, Chrome extension developers, AI/ML enthusiasts, and anyone interested in exploring the potential of client-side AI and the integration of Gemini into the Chrome browser. Specifically beneficial for those looking to use AI for translation, language understanding, and summarization. **Key Highlights:** - **Chrome's Built-in AI Capabilities**: Chrome is integrating AI features like Gemini directly into the browser, offering new possibilities for developers. - **AI-Powered Styling Assistance**: Chrome offers an AI assistant for styling within developer mode, enabling real-time collaboration with AI for front-end development. Enable through experiment flags. - **Gemini Nano API Access**: Developers can access Gemini Nano through Chrome's exploratory APIs for tasks like translation, language detection, and summarization, all executed locally in the browser. - **Origin Trials: Try AI Features Now**: Chrome's Origin Trials program allows developers to test experimental AI APIs like translator and summarizer by signing up for a trial and enabling flags. - **Local AI Processing Benefits**: Running AI tasks locally offers advantages like enhanced security, reduced latency, and the potential for smaller, more efficient models. All processed with in-browser APIs. --- ### How to Build Multi-Agent AI Systems with OpenAI Swarm & Secure Them Using Portkey **Published:** 10/29/2024 **Duration:** 23 minutes **YouTube:** https://youtube.com/watch?v=9qLkAEJol9A **Full LLM Text:** /video/cmis2xwpr000y04kwomeh5heb/llm.txt **Description:** In this video, I demonstrate how to create a multi-agent AI system using OpenAI Swarm, an educational framework for managing collaborative AI agents, and secure the system using Portkey, an AI Gateway designed for enhanced observability and security. Here's a breakdown of the setup: - OpenAI Swarm coordinates AI agents to tackle complex tasks. - Agents work together through defined hand offs to achieve shared goals. - Portkey ensures secure, test oriented, agent activity. - Portkey provides real-time insights into AI decisions. This approach enables building robust AI solutions that are transparent and secure, making it ideal for educational projects, research, or any scenario where multi-agent systems are needed. By combining OpenAI Swarm and Portkey, you get the best of both multi-agent management and top-tier security. Some potential use cases include simulations, collaborative AI research, or secure AI-driven platforms. The educational framework empowers developers to experiment, while the AI Gateway keeps your operations safe. 📰 FREE Code & Resources: https://sendfox.com/nerdingio 👉🏻 Ranked #1 Product of the Day: https://www.producthunt.com/posts/ever-efficient-ai 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 00:40 OpenAI Swarm Overview 🔗 Links https://github.com/openai/swarm https://portkey.ai/ https://cookbook.openai.com/examples/orchestrating_agents ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## Video Summary: Building & Securing Multi-Agent AI Systems with OpenAI Swarm & Portkey **1. Executive Summary:** This video demonstrates how to build and secure a multi-agent AI system using OpenAI Swarm for agent orchestration and Portkey as an AI Gateway for observability, security, and advanced configurations. It showcases a practical example of a trip planner application with agents communicating and delegating tasks while highlighting the benefits of using Portkey for security, logging, and A/B testing. **2. Main Topics Covered:** * **OpenAI Swarm Overview:** Introduction to OpenAI Swarm as an educational and experimental framework for building multi-agent AI systems. * **Agent Setup and Function Calls:** Explanation of setting up agents with names, instructions (prompts), functions (tools), and transfer mechanisms for communication. * **Portkey Integration:** Demonstrating how to integrate Portkey as a custom client within OpenAI Swarm by modifying the base URL and headers. * **Portkey Features:** Exploring Portkey's capabilities, including logging, cost tracking, security guardrails, virtual keys, load balancing, and canary testing. * **Complex Example: Trip Planner:** Walking through a complex multi-agent system example – a trip planner application – where agents handle weather inquiries, flight booking, and hotel reservations. * **Observability and Security:** Showing how Portkey provides real-time insights into AI decisions, potential biases, and code generation, as well as offering security measures like key rotation. **3. Key Takeaways:** * **OpenAI Swarm simplifies multi-agent system development:** Provides an easy-to-use framework for managing and orchestrating AI agents, suitable for educational purposes and research. * **Portkey enhances AI system security and observability:** Adds crucial features like security guardrails, real-time logging, and cost tracking to AI applications. * **Custom client integration allows seamless Portkey integration:** Modifying the base URL and headers in OpenAI Swarm enables easy integration with AI gateways like Portkey without significant code changes. * **Agent handoffs and function calls create complex workflows:** Demonstrates how to design agents with specific tools and transfer functions for effective communication and task delegation. * **Portkey enables A/B testing and Load Balancing:** Load Balancing across multiple keys to handle rate limiting, A/B tests or canary tests using Portkey allow for experimentation and optimization of AI models. **4. Notable Quotes or Examples:** * "What this is actually doing is it's allowing you to orchestrate and do handoffs of different routines in the uh with open Ai." - Describing the core functionality of OpenAI Swarm. * "Port key is basically uh almost like a proxy or a middleman that allows you to have obser observability out of the box" - Explaining Portkey's role as an AI Gateway. * Example: The Trip Planner application, showcasing a practical multi-agent system where agents collaborate to book flights, hotels, and provide travel recommendations. * Describing the Guardrails functionality of Portkey, it can detect biases or whether the agent is providing code during normal Q&A interaction. **5. Target Audience:** * AI/ML developers and engineers * Students and researchers interested in multi-agent systems * Individuals looking to improve the security and observability of their AI applications * Developers using OpenAI models and seeking better management and control over API keys and traffic. **Key Highlights:** - **OpenAI Swarm for Multi-Agent Systems**: OpenAI Swarm provides an educational framework for building and orchestrating multi-agent AI systems with handoffs and function calls between agents. - **Portkey: AI Gateway for Observability & Security**: Portkey acts as an AI gateway, offering observability (logging, cost tracking), security features (guardrails, key rotation), and configurations like Canary tests for AI applications. - **Custom Client Integration**: OpenAI Swarm allows passing custom clients (like Portkey), enabling the use of AI gateways by modifying the base URL and headers without significant code changes. - **Agent Design & Transfer Functions**: The video demonstrates structuring agents with specific tools and transfer functions, enabling communication and task delegation within the multi-agent system. Agent can act solo or as part of the swarm. - **Load Balancing and Canary Tests with Portkey**: Portkey enables load balancing across multiple API keys and conducting canary tests by routing a small percentage of traffic to different models or configurations for evaluation. --- ### Learn How ROSA Works in #Robotics with #Langchain **Published:** 10/7/2024 **Duration:** 20 minutes **YouTube:** https://youtube.com/watch?v=igQImiy9TM4 **Full LLM Text:** /video/cmis2y8ry000z04kwrsyydxpz/llm.txt **Description:** Unlock the potential of AI-driven #robotics with ROSA, the AI assistant built for #ROS1 and #ROS2 systems! Powered by the Langchain framework, ROSA allows you to interact with #robots using natural language, making robotics development more intuitive and accessible. Key Highlights: - Communicate with #ROS based robots through natural language queries - Supports both #ROS1 and #ROS2 for seamless integration across systems - Built on Langchain, leveraging the power of #AI for efficient robotics development - Simplifies complex robotic operations, saving time and effort for developers Transform the way you work with robotic systems and elevate your development process with ROSA. Like, comment, and subscribe for more cutting-edge #robotics insights! 📰 News & Resources: https://sendfox.com/nerdingio 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 00:38 Turtlebot 01:34 Architecture 03:27 Demo 05:05 Navigation 06:04 ROS 08:50 ROSA 10:41 Demo 13:18 Code 18:14 LLM 19:53 Outro 🔗 Links https://github.com/nasa-jpl/rosa https://github.com/nasa-jpl/rosa/wiki/Guide:-TurtleSim-Demo https://github.com/nasa-jpl/rosa/wiki/Developer-Documentation ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## ROSA: AI-Driven Robotics with Langchain - Summary **1. Executive Summary:** This video introduces ROSA, a ROS (Robot Operating System) package from JPL that integrates Langchain to enable AI-powered interaction and control of robots using natural language. ROSA leverages Langchain's agent capabilities to understand user commands, plan tasks, and control robots by publishing messages to ROS topics, simplifying complex robotic operations and debugging processes. **2. Main Topics Covered:** * **Introduction to ROSA:** What ROSA is, its compatibility with both ROS1 and ROS2, and its foundation on the Langchain framework. * **Turtlebot Example:** A real-world example of a robot (Turtlebot) and a previously built system to control it via Alexa and web interface. * **ROS Basics:** Explanation of the Robot Operating System (ROS), its pub/sub architecture (topics, publishers, subscribers), and its role in robotics. * **Langchain Integration:** How Langchain agents and Large Language Models (LLMs) can be used to control robots by defining ROS topics as tools or APIs, allowing for dynamic task planning. * **ROSA Architecture and Demo:** A detailed look at ROSA's architecture, including how it leverages Langchain's agent functionality to understand commands, plan tasks (e.g., drawing a star), and execute them by publishing messages to relevant ROS topics. * **Customizable Robot Personas:** Utilizing LLMs to create robot personas with personalized greetings, instructions, constraints, and even humor. * **Code Walkthrough:** Examination of the ROSA code, showcasing how Langchain agents are integrated with ROS, including defining tools for robot movement and utilizing LLMs for prompt generation. **3. Key Takeaways:** * ROSA simplifies robot interaction by enabling natural language commands. * Langchain provides a framework for AI agents to control robots by interacting with ROS topics. * AI agents can dynamically plan and execute complex tasks. * LLMs enable the creation of customizable robot personas. * ROSA enhances debugging and interactive experiences with robots. * The use of Python in both ROS and Langchain facilitates seamless integration. * This tool could also potentially use other LLMs besides OpenAI, such as Claude. **4. Notable Quotes or Examples:** * "We can actually invoke through our agent to show a list of topics that have Publishers but no subscribers so you can actually ask information about your robot and its system which is incredibly awesome because previously you'd have to either manually or programmatically build in very specific questions." * "If you think of move forward as the function of an API and your tool and it's saying based on this NLP – I know I need to move a distance of two units." * Example of drawing a star: The AI agent autonomously plans the steps, including calculating angles and distances, to execute the drawing task. * Example of using a LLM to include a funny turtle joke when it greets the user. **5. Target Audience:** * Robotics developers working with ROS (ROS1 or ROS2). * AI/ML engineers interested in applying Langchain and LLMs to robotics. * Researchers exploring AI-driven robot control and human-robot interaction. * Anyone interested in learning how to integrate AI with ROS for more intuitive robotics development. **Key Highlights:** - **ROSA: ROS Agents with Langchain**: Introduces ROSA, a package from JPL that integrates ROS (Robot Operating System) with Langchain, enabling the use of AI agents and LLMs in robotics for enhanced control and interaction. - **Leveraging Langchain for Robot Control**: Explains how Langchain can be used to send information to ROS's publish-subscribe model, effectively controlling a robot by defining tools as APIs to publish data to various topics. - **Interactive Robot Debugging with ROSA**: Highlights ROSA's ability to provide interactive debugging experiences, allowing users to query the robot's system (e.g., listing topics) and potentially build chat interfaces for robot interaction. - **Dynamic Task Planning with AI Agents**: Demonstrates how AI agents can dynamically plan and execute complex tasks (e.g., drawing a star) by understanding ROS topics and autonomously generating the necessary commands. - **Customizable Robot Personas with LLMs**: Shows how LLMs can be used to create customizable robot personas, including setting up greetings, instructions, constraints, and even incorporating humor into the robot's responses. --- ### How to Prepare a Dataset for Hume's Custom Models **Published:** 8/26/2024 **Duration:** 7 minutes **YouTube:** https://youtube.com/watch?v=PP_EhGQfZbs **Full LLM Text:** /video/cmis2yjis001004kwiapgyg6z/llm.txt **Description:** Unlock the power of predictive AI with Hume’s Custom Model API! Now you can predict well-being, satisfaction, mental health, and more by creating custom multimodal models using just a few labeled examples. Key Highlights: - Integrate dynamic patterns of language, vocal, and facial expressions into your models - Leverage Hume’s pretrained AI, fine-tuned on millions of videos and audio files - Achieve accurate predictions with just a few dozen examples - Deploy and share models tailored specifically to your needs Start predicting the outcomes that matter most to your users with minimal effort. Like, comment, and subscribe for more AI insights! 📰 News & Resources: https://sendfox.com/nerdingio 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 🔗 Links https://www.hume.ai/ https://www.hume.ai/blog/hume-custom-model-api ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ #chatgpt #voice #ai #programming **Summary:** ## Hume AI Custom Model Creation: Video Summary **1. Executive Summary:** This video provides an overview of Hume AI's custom model building capabilities, focusing on preparing a dataset for creating personalized AI models that can predict traits like well-being, satisfaction, or mental health from multimodal data (audio, video, text). It highlights the ease of use of the Hume AI platform and how to prepare data for custom model creation. **2. Main Topics Covered:** * **Hume AI Custom Model Overview:** Introduction to Hume AI and its custom model building features. * **Playground Exploration & Key Features:** Demonstration of Hume AI's playground features including expression measurement via webcam, voice burst detection, and speech prosody analysis with embedding plots. This section showcases real-time emotion and speech analysis. * **Custom Model Dataset Preparation:** A detailed explanation of how to structure and prepare a dataset for custom models. * **Building Custom Models:** A process to create custom models within Hume AI's platform. Getting and labeling the data correctly, then uploading, are key steps. **3. Key Takeaways:** * Hume AI allows users to build custom AI models with minimal effort by leveraging pre-trained AI fine-tuned on millions of videos and audio files. * Custom models can predict a variety of outcomes related to well-being, mental health, and satisfaction using multimodal data. * Preparing a dataset involves organizing image, video, or audio files into labeled subfolders (e.g., 'attentive' and 'distracted') within a root directory. * Creating a custom model is a relatively straightforward process on the Hume AI platform once the data is correctly structured and labeled. * The most challenging aspect is data acquisition and accurate labeling of that data. **4. Notable Quotes or Examples:** * "Sort your image your video and your audio files into subfolders based on their labels..." - Example of dataset preparation instructions from Hume AI. * The video showcases examples of Hume AI's playground functionality, including real-time facial expression analysis and voice burst detection, highlighting the platform's capabilities. * An example is given of creating a model that detects "attentive" vs. "distracted" behavior. **5. Target Audience:** * AI Developers and Engineers * Data Scientists interested in Multimodal AI * Researchers exploring AI-driven insights into human behavior * Anyone interested in building custom AI models for specific prediction tasks. **Key Highlights:** - **Hume AI Custom Model Overview**: The video introduces Hume AI and its custom model building capabilities, covering exploration of the playground and understanding different model types offered by the platform. - **Playground Exploration & Key Features**: The video demonstrates Hume AI's playground features including expression measurement via webcam, voice burst detection, and speech prosody analysis with embedding plots, showcasing real-time emotion and speech analysis. - **Custom Model Dataset Preparation**: Explains how to prepare a dataset for custom models by organizing image/video/audio files into labeled subfolders (e.g., 'attentive' and 'distracted') within a root directory, then uploading it to Hume AI. - **Building Custom Models: A Simple Process**: The process to create custom models is relatively straightforward within Hume AI's platform. Getting and labeling the data correctly, then uploading, seems to be the most challenging step. --- ### How to create a Custom LLM Integration with Hume's EVI **Published:** 8/19/2024 **Duration:** 14 minutes **YouTube:** https://youtube.com/watch?v=uOo7qTCleT4 **Full LLM Text:** /video/cmis2yv76001104kw2mtvf3g0/llm.txt **Description:** Elevate your user experiences with Hume’s Empathic User Interface (EVI) by integrating your custom language models! This feature is perfect for developers seeking deep configurability and personalized interactions. Key Highlights: - Seamless integration with your custom language models - Full control over conversation flow and text outputs - Ideal for advanced use cases like conversation steering, regulatory compliance, and context-aware text generation - Leverage real-time data and Retrieval Augmented Generation (RAG) to enhance interactions Discover how to tailor your user interfaces with precision using Hume’s EVI. Like, share, and subscribe for more advanced development tutorials! 🎥 Chapters 00:00 Introduction 🔗 Links https://www.hume.ai/ https://github.com/HumeAI/hume-api-examples/tree/main/evi-custom-language-model https://dev.hume.ai/docs/empathic-voice-interface-evi/tool-use#create-a-configuration https://smith.langchain.com/ ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ #chatgpt #voice #ai #programming **Summary:** ## Hume AI Custom LLM Integration: Video Summary **1. Executive Summary:** This video tutorial demonstrates how to integrate a custom Language Model (LLM) with Hume AI's Empathic User Interface (EVI) using websockets, Langchain, and Langsmith. It highlights the benefits of custom LLM integration, including enhanced conversation steering, context awareness, and emotion analysis capabilities within Langsmith tracing. **2. Main Topics Covered:** * **Introduction to Custom LLM Integration with Hume AI:** Overview of the advantages and use cases for connecting custom LLMs to Hume's EVI, such as conversation steering, regulatory compliance, and improved context. * **Setting up Websocket Connection:** Explanation of the requirement for a websocket connection for custom LLM integration and how to achieve it using tools like poetry, uvicorn, and ngrok. * **Configuration within Hume AI:** Step-by-step guide to configuring the EVI in Hume AI to connect to the custom LLM via the websocket endpoint. * **Leveraging Langchain and Langsmith:** Demonstrates the use of Langchain for orchestrating the AI flow and Langsmith for tracing, debugging, and visualizing emotion analysis of both inputs and outputs. * **Code Walkthrough:** Exploration of the code structure, focusing on agent definition, prompt templates, and handling Hume AI message structures. * **Testing and Demonstration:** A live demo of the integration, showcasing the interaction between Hume AI and the custom LLM, and examining the traces in Langsmith. **3. Key Takeaways:** * **Websocket Requirement:** A functional websocket connection is crucial for integrating custom LLMs with Hume AI. The LLM needs to be deployed and accessible via a websocket endpoint. * **Custom LLM Benefits:** Using a custom LLM offers greater control over the conversation flow, allows for the integration of RAG, and provides the flexibility to choose different LLMs (e.g., Claude, Mistral). * **Emotion Analysis via Langsmith:** The integration enables emotion analysis of both user input and LLM output, which can be traced and visualized in Langsmith for insights and improvements. * **Langchain Orchestration:** Langchain facilitates the AI flow, including tool calling, prompt management, and integration with external resources. * **Real-time Data Integration:** Custom LLMs can access real-time data and integrate with RAG (Retrieval Augmented Generation) to enhance the quality and relevance of interactions. **4. Notable Quotes or Examples:** * "The custom model needs a socket connection, and so this socket connection is going to be how we actually hit the model and then we're using it to actually structure our payloads and send it back to Hume." * "Using Lan chain and be able able to be more deterministic about your workflow uh by programming that it gives you more uh flexibility on controlling the the steering like the conversation itself" * (In context of Langsmith tracing) "It is showing the emotion in the responses...it's just really interesting to be able to if you wanted to annotate on any of that information or do some sort of data set of any of your findings since you are using a custom llm this would be a really interesting way to kind of associate that with it" * Demonstrates that the code shows how custom LLMs allow you connect to other Open AI-compatible models as well as those that include RAG. **5. Target Audience:** * Developers interested in integrating custom language models with Hume AI. * AI engineers looking to enhance user experiences through emotion analysis and personalized interactions. * Those seeking greater control over conversation flow and adherence to regulatory compliance within AI applications. * Individuals interested in using Langchain and Langsmith for building and debugging AI applications with Hume AI. **Key Highlights:** - **Custom LLM Integration with Hume AI**: The video demonstrates integrating a custom LLM with Hume AI using websockets, Langchain, and Langsmith for tracing and debugging. This approach offers advantages such as advanced conversation steering and better context awareness. - **Websocket Connection Requirement**: A key takeaway is the necessity of a websocket connection for the custom LLM integration. The LLM needs to be deployed on a websocket instance, as Hume expects communication via this protocol. - **Langsmith Tracing for Emotion Analysis**: The integration allows emotion analysis of both inputs and outputs using Hume's emotion detection capabilities within Langsmith traces. This provides valuable insights for improving the LLM's interaction and response. - **Flexibility with Custom LLMs & RAG**: Using a custom LLM with Hume enables flexibility in model selection (e.g., Claude, Mistral) and integration with Retrieval Augmented Generation (RAG) for enhanced context and knowledge retrieval. --- ### How to Use HumeAI's EVI Next.js Function Calling API #aitools **Published:** 7/29/2024 **Duration:** 11 minutes **YouTube:** https://youtube.com/watch?v=exAICcpzXMc **Full LLM Text:** /video/cmis2z7i8001204kwlfw6750i/llm.txt **Description:** Unlock the power of emotion AI with HumeAI! In this video, we walk you through the EVI Next.js function calling API, providing step-by-step instructions on how to integrate and utilize it in your projects. Perfect for developers looking to add emotional intelligence to their applications. 📌 Key Highlights: - Overview of HumeAI's EVI API - Setting up Next.js for function calls - Code examples and practical implementation - Tips for optimizing your emotional AI integration Don't forget to like, comment, and subscribe for more tech tutorials! 📰 News & Resources: https://sendfox.com/nerdingio 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 00:29 Repo 01:36 Create Configration 03:00 Create Tool 04:30 Code 06:57 Demo 09:36 Playground 🔗 Links https://www.hume.ai/ https://github.com/HumeAI/hume-api-examples/tree/main/evi-next-js-function-calling https://dev.hume.ai/docs/empathic-voice-interface-evi/tool-use#create-a-configuration ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ #chatgpt #voice #ai #programming **Summary:** Here's a comprehensive summary document of the video "How to Use HumeAI's EVI Next.js Function Calling API #aitools": **1. Executive Summary:** This video tutorial explains how to integrate HumeAI's Empathic Voice Interface (EVI) function calling API into a Next.js application, enabling the AI to interact with external APIs, using a weather API as a practical example. The video covers the necessary steps from setting up the environment and creating configurations to debugging and testing the implementation both in the Next.js application and HumeAI's playground. **2. Main Topics Covered:** * **Overview of HumeAI's EVI API and Function Calling:** Explains the purpose and functionality of the EVI API, emphasizing the ability to extend AI capabilities by connecting to external services. * **Next.js Setup and Environment Configuration:** Details the process of setting up a Next.js project, including the necessary environment variables (API keys from HumeAI and Geoapify), and downloading the necessary files. * **Creating and Configuring Tools in HumeAI:** Demonstrates how to create a tool (e.g., a weather API) within the HumeAI dashboard, including defining the API's name, description, and required parameters (e.g., location, format). * **Code Implementation and Explanation:** Walks through the code in a Next.js client component, explaining how it handles tool calls, fetches data from the weather API based on provided arguments, and returns a response. * **Debugging and Troubleshooting:** Highlights common errors that can occur during integration (e.g., incorrect tool names) and provides guidance on how to identify and resolve them. * **Testing in the HumeAI Dashboard Playground:** Shows how to use HumeAI's playground to test function calls directly, allowing for interactive validation of API integrations, including sending success and error responses. * **Voice Provider Integration:** Explanation of how the voice provider is connected to handle tool calls, allowing the system to interpret spoken commands and generate appropriate responses based on the received parameters. **3. Key Takeaways:** * HumeAI's function calling feature is similar to OpenAI's functions, allowing AI models to interact with external APIs. * Setting up the correct environment variables and API keys is crucial for successful integration. * Careful configuration of tools within the HumeAI dashboard is essential for defining the API's capabilities. * Debugging tools and testing environments are available to validate the integration. * Voice interaction is integrated with tools using call handling, allowing spoken commands to invoke external APIs. **4. Notable Quotes or Examples:** * "Tooling is just like it is with Open AI functions where you're trying to basically get or create an API call and make your prompt or your AI aware that it can actually use these types of tools..." * Example Weather Tool parameters: Location (string), format (Celsius or Fahrenheit). * Demonstrates troubleshooting by identifying that the "tool name" in the Next.js code must match the name defined in the HumeAI dashboard. * Successful demonstration: "Can you tell me the temperature outside in Detroit?" "The current temperature in Detroit is 65f." **5. Target Audience:** The target audience for this video is developers with basic experience in: * Next.js * API integrations * Understanding of AI concepts (specifically LLMs and function calling/tool use) * Interest in adding emotional intelligence and voice interaction to their applications using HumeAI. **Key Highlights:** - **HumeAI Function Calling API Overview**: The video provides an overview of HumeAI's function calling API, enabling AI models to interact with external APIs, similar to OpenAI functions. - **Weather Tool Example**: The tutorial demonstrates using a weather API as a practical example of how to configure and implement function calling within the HumeAI framework. - **Debugging and Troubleshooting**: The video highlights the debugging process, showing how to identify and resolve errors related to tool names and API responses within the Next.js application. - **Testing in Dashboard Playground**: HumeAI's dashboard offers a playground environment for testing function calls directly, allowing for interactive testing and validation of API integrations. - **Voice Provider and Tool Integration**: The video explains how the voice provider is integrated with the tool call, enabling the system to handle tool calls and generate responses based on the parameters received. --- ### Get Started with HumeAI's EVI Next.js Starter Kit **Published:** 7/22/2024 **Duration:** 10 minutes **YouTube:** https://youtube.com/watch?v=0WCx93I3Pas **Full LLM Text:** /video/cmis2zirg001304kwiws18pa8/llm.txt **Description:** Kickstart your journey with emotion AI using HumeAI's EVI Next.js Starter Kit! This tutorial provides a comprehensive guide to setting up and using the starter kit to build emotionally intelligent applications with ease. 📌 Key Highlights: - Introduction to HumeAI's EVI - Setting up the Next.js starter kit - Step-by-step code walkthrough - Best practices for emotional AI integration Don't forget to like, comment, and subscribe for more cutting-edge tech tutorials! 📰 News & Resources: https://sendfox.com/nerdingio 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 00:35 Dashboard 01:57 Starter 04:18 Code 07:25 Demo 🔗 Links https://www.hume.ai/ Repo: https://github.com/HumeAI/hume-evi-next-js-starter ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## HumeAI EVI Next.js Starter Kit Video Summary **1. Executive Summary:** This video provides a concise introduction to HumeAI's Empathetic Voice Interface (EVI) and demonstrates how to quickly build emotionally intelligent applications using the provided Next.js starter kit. It covers setup, code walkthrough, and highlights the ease of integration and real-time emotion analysis capabilities of the platform. **2. Main Topics Covered:** * **Introduction to HumeAI and EVI:** Overview of HumeAI's core components: Empathetic Voice Interface, Expression Measurement, and Custom Model creation. Emphasis on understanding and responding to human emotion. * **Setting up the Next.js Starter Kit:** Cloning the repository, obtaining API keys from the HumeAI dashboard, and running the application. * **Code Walkthrough:** Examination of key code components, including API key management, fetching access tokens, WebSocket implementation, and context usage. * **Demo and Functionality:** Demonstrating the real-time emotion analysis capabilities through voice interaction and visualizing the data flow via WebSockets. * **Interrupt Handling and Future Features:** Highlighting the starter kit's interrupt handling capabilities and mentioning future exploration of custom modeling and function calling within the API. **3. Key Takeaways:** * HumeAI offers a powerful toolkit for building applications with emotional intelligence. * The Next.js starter kit simplifies the integration of HumeAI's EVI, allowing for rapid prototyping and development. * Real-time emotion analysis is achieved through WebSocket communication, providing immediate feedback and insights. * HumeAI's API is user-friendly, well-documented, and provides excellent examples for developers. * The starter kit includes features like interrupt handling for enhanced user experience. * The Next.js starter code is clean, well-structured, and makes extensive use of React Context. **4. Notable Quotes or Examples:** * "(HumeAI) has three different parts: it has an empathetic voice interface, an expression measurement, and then it even has the ability to create custom models..." - Defining HumeAI's core offerings. * "The fetch access token is already a function written for you, so you just need your API key and your client secret and it'll actually generate an access token..." - Describing the starter kit's ease of use. * "As you could see we're not getting like fetch calls coming across, we're actually opening up a websocket..." - Explaining the real-time data transmission. * "I really liked how everything is is mapped correctly and like defined so each one of these colors gets its own color..." - Highlighting the well-structured and organized code. **5. Target Audience:** * Developers interested in building applications with emotion AI capabilities. * Individuals looking for an introduction to HumeAI's EVI and its features. * Developers familiar with Next.js and React. * AI enthusiasts seeking tools for understanding and responding to human emotion in digital interactions. **Key Highlights:** - **HumeAI Overview: Empathetic AI Toolkit**: HumeAI offers three core components: Empathetic Voice Interface, Expression Measurement, and Custom Model creation, all aimed at understanding and responding to human emotion. - **Next.js Starter: Easy Setup & Clean Code**: The Next.js starter kit simplifies integration with HumeAI's API. Its clean code, context usage, and pre-built functions (like fetching access tokens) enable rapid prototyping and development. - **WebSocket Communication for Real-time Emotion Analysis**: The demo utilizes WebSockets for real-time communication, allowing developers to analyze audio input and receive emotion scores directly, as shown in the network tab analysis. - **API Accessibility & Excellent Documentation**: HumeAI boasts a user-friendly API with well-organized documentation and practical examples, making it easy to start building applications that leverage emotional intelligence. - **Interrupt Handling & Advanced Features Preview**: The starter kit includes built-in features like interrupt handling for voice interaction. Future videos will delve into custom modeling and function calling within the API. --- ### Streaming Live With Digital Avatars Using Next.js **Published:** 7/15/2024 **Duration:** 15 minutes **YouTube:** https://youtube.com/watch?v=3e5Fixl_nQE **Full LLM Text:** /video/cmis2zuok001404kwro8xkq9e/llm.txt **Description:** Explore the future of digital avatars with our comprehensive tutorial on live streaming digital avatars using Next.js! In this video, we'll walk you through the HeyGen Official Streaming Avatar Next.js Demo, showcasing how you can implement and customize your own digital avatars for live streaming applications. 📌 Key Highlights: - Setting up the Next.js environment for avatar streaming - Integrating the HeyGen Streaming Avatar library - Customizing avatar interactions and appearances - Real-time avatar rendering and performance optimization Whether you're a developer looking to enhance your live streaming capabilities or a tech enthusiast curious about the latest in digital avatar technology, this tutorial has something for everyone. Don't forget to like, comment, and subscribe for more cutting-edge tech tutorials! 📰 News & Resources: https://sendfox.com/nerdingio 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 🔗 Links https://www.heygen.com/ https://github.com/HeyGen-Official/StreamingAvatarNextJSDemo/tree/main https://huggingface.co/docs/transformers.js/main/en/api/pipelines#module_pipelines.AutomaticSpeechRecognitionPipeline ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## Video Summary: Streaming Live With Digital Avatars Using Next.js **1. Executive Summary:** This video explores the HeyGen Streaming Avatar Next.js Demo, demonstrating how to implement and customize digital avatars for live streaming applications. It covers setting up the Next.js environment, integrating the HeyGen library, customizing avatar interactions, and highlights potential security concerns with API key exposure, suggesting alternative solutions. **2. Main Topics Covered:** * **Introduction to HeyGen:** AI-powered video creation at scale using dynamic digital avatars. * **Setting up the HeyGen Streaming Avatar Next.js Demo:** Cloning the repository, installing dependencies, and configuring API keys (HeyGen and OpenAI). * **Security Considerations:** Risks associated with exposing the OpenAI API key in the browser and alternative solutions like server-side transcription or Transformer.js. * **Using the Vercel AI SDK:** Utilizing the `useChat` hook for real-time streaming between the client and server for interactive avatar conversations. * **Live Avatar Interaction:** Demonstrating real-time text input, voice input (with a discussion of in-browser transcription vs. server-side or TransformerJS), avatar responses, and session management. * **Potential Applications:** Discussing the possibility of creating interactive customer GPTs powered by digital avatars. * **Code Walkthrough:** Reviewing the relevant code snippets in the Demo, covering areas like token generation, session management, media stream handling, and the integration of `useChat`. **3. Key Takeaways:** * HeyGen offers a platform for creating and streaming digital avatars, which can be integrated into Next.js applications. * The provided Next.js demo allows for real-time interaction with the avatar through text and voice, showcasing a practical implementation. * Security is paramount; exposing API keys client-side is a significant risk that needs to be addressed in production environments. Employing server-side transcription using open-source models like Transformer JS is a viable alternative. * The Vercel AI SDK's `useChat` hook streamlines the process of managing streaming conversations between the client and server. * Streaming avatars open up opportunities for creating engaging customer service experiences, personalized assistants, and other interactive applications. **4. Notable Quotes or Examples:** * "They [Haen] tout it as AI-powered video creation at scale... they're actually creating digital avatars and videos... that you can then uh change dynamically." * "This [OpenAI API Key in the browser] is a little concerning uh but it's a demo so it is what it is but if you were going to put this in production you definitely want to do something different uh you don't want to expose your opening I ke very easy to figure that out..." * "...you could actually create a conversation back and forth in the browser, right? So you could send information... imagine that this simple chat right here is actually a uh an assistant or a customer GPT..." **5. Target Audience:** * Developers interested in integrating digital avatars into web applications. * Tech enthusiasts exploring the use of AI and live streaming technologies. * Next.js developers looking for practical examples of using the Vercel AI SDK. * Individuals considering using HeyGen or similar services for avatar-driven video creation. * Anyone interested in customer GPTs and AI-driven customer experiences. **Key Highlights:** - **Haen: AI-Powered Video Creation at Scale**: Haen offers dynamic digital avatars and video creation tools, suitable for automated video generation based on CRM data or other triggers. - **Next.js Streaming Avatar Demo**: The video explores a Next.js demo using Haen to stream digital avatars, showcasing real-time text interaction and voice input. - **Security Concerns with OpenAI Key Exposure**: The demo exposes the OpenAI API key in the browser, posing a security risk. Alternatives like server-side transcription or Transformer.js are suggested for production environments. - **Vercel AI SDK for Streaming**: The project uses Vercel's AI SDK and `useChat` hook for real-time streaming between the client and server, enabling interactive conversations with the avatar. - **Potential Applications: Customer GPTs**: The streaming avatar implementation opens possibilities for creating interactive customer GPTs, offering assistance or representing a company in a personalized way. --- ### How to use Real Time In-Browser Transcription with WebGPU Whisper and Transformer.js **Published:** 6/24/2024 **Duration:** 11 minutes **YouTube:** https://youtube.com/watch?v=LAFOhwwccgo **Full LLM Text:** /video/cmis306pu001504kwg3sbpkpl/llm.txt **Description:** Say goodbye to slow and insecure speech transcription with WebGPU Whisper and Transformer.js! This cutting-edge technology offers seamless real-time transcription and translation for over 100 languages, all while running directly in your browser. Thanks to Transformers.js, your data stays on your device, ensuring unparalleled privacy. In this video, we explore the technological marvels behind WebGPU Whisper, showcasing its blazingly-fast performance and exceptional accuracy. Whether you're a developer or a tech enthusiast, you'll discover the immense potential of this in-browser speech transcription tool. Watch now to see how WebGPU Whisper is changing the game! 🔔 Make sure to like, comment, and subscribe for more tutorials and updates! 📰 News & Resources: https://sendfox.com/nerdingio 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 00:26 Demo 02:27 Setup 04:13 Code 06:10 Worker 10:36 Conclusion 🔗 Links https://huggingface.co/spaces/Xenova/realtime-whisper-webgpu https://github.com/xenova/transformers.js/tree/v3/examples/webgpu-whisper ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## Video Summary: How to use Real Time In-Browser Transcription with WebGPU Whisper and Transformer.js **1. Executive Summary:** This video explores the implementation of real-time, in-browser speech transcription using WebGPU and Transformer.js, specifically the Whisper base model. It demonstrates how to set up and understand the core code logic, highlighting the benefits of local processing for privacy and speed. **2. Main Topics Covered:** * **Introduction to WebGPU Whisper and Transformer.js:** Overview of the technology and its capabilities for real-time, in-browser speech transcription. * **Demo of Real-Time Transcription:** Showcases the live transcription of speech using the Whisper base model running entirely in the browser. Network activity is monitored to prove no external API calls. * **Hugging Face Integration:** Utilizing a Hugging Face Space for demonstration and accessing the Whisper base model (73M parameters). * **Setup and Local Implementation:** Guides viewers on how to clone the project, install dependencies (npm install), and run the application locally (npm run Dev). * **Code Walkthrough (Worker.js):** Detailed examination of the `worker.js` file, which handles the core transcription logic using Transformers.js V3 experimental branch. This includes model loading, audio processing, and transcription generation. * **Audio Processing Pipeline:** Explanation of how audio chunks are streamed, converted, processed by the Whisper model, tokenized, and sent back to the main thread for display. * **Explanation of Data Flow:** From microphone input to displayed text, detailing how the `app.js` and `worker.js` communicate via message passing. **3. Key Takeaways:** * WebGPU and Transformer.js enable real-time, in-browser speech transcription, eliminating the need for backend servers and ensuring data privacy. * The Whisper base model (73M parameters) can be efficiently run in the browser using WebGPU for accelerated processing. * The Transformer.js V3 branch provides the necessary tools and APIs for implementing the speech recognition pipeline. * The `worker.js` script is crucial for handling computationally intensive tasks like model loading and audio processing in a separate thread. * The video demonstrates a practical application of modern browser technologies for advanced AI tasks. **4. Notable Quotes or Examples:** * "Again we didn't fetch anything from our back end this was just loaded through the browser" * "This is crazy fast for doing real time uh transcription in the browser super helpful if you were trying to do uh like chat based systems where you actually wanted to have voice in with your chat as without having to go out to open AI or some other um closed Source or even open source model that uh requires a backend call" * (Explanation of `worker.js` functionality) "...the generate function this function is what really is going to be taking the audio and taking the language and then actually processing it" * (Data streaming/chunking) "...we're going to take a blob and chunk it and so then we're going to make a file reader and read those and then send that on when the file is being read" **5. Target Audience:** * Web developers interested in implementing real-time speech recognition in their applications. * Developers using the Transformer.js library or interested in WebGPU and browser-based AI. * AI enthusiasts and researchers exploring the possibilities of running machine learning models directly in the browser. * Individuals concerned about data privacy and seeking solutions that avoid sending audio data to external servers. **Key Highlights:** - **Real-time In-Browser Transcription**: The video demonstrates real-time speech recognition using WebGPU and Transformer.js directly in the browser, eliminating backend calls. - **Whisper Base Model with WebGPU**: It leverages the Whisper base model (73M parameters) and WebGPU for fast, efficient processing within the browser environment. - **Transformer.js V3 Branch**: The implementation utilizes the experimental V3 branch of Transformer.js, showcasing the latest advancements and capabilities. - **Worker.js for Processing**: The core logic resides in worker.js, handling model loading, audio processing, and generating transcriptions on the fly using Transformers. - **Data Streaming & Tokenization**: The video highlights the process of streaming audio chunks, converting them, performing transcription, tokenizing the output, and posting the information back to the main thread for display. --- ### What is WebGPU Whisper ML Powered In Browser Transcription with Transformer.js **Published:** 6/17/2024 **Duration:** 9 minutes **YouTube:** https://youtube.com/watch?v=YuYf-MWQbTo **Full LLM Text:** /video/cmis30ian001604kwcc67m7w6/llm.txt **Description:** Explore the revolutionary WebGPU Whisper and Transformer.js in this video! Released exactly one year after Whisper Web, this technology supports multilingual transcription and translation across 100 languages. Leveraging WebGPU technology, this integration provides blazingly-fast performance and exceptional accuracy directly within your browser. Thanks to Transformers.js, the model runs entirely locally in your browser, ensuring that no data leaves your device – a huge win for privacy! 🔔 Make sure to like, comment, and subscribe for more tutorials and updates! 📰 News & Resources: https://sendfox.com/nerdingio 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 00:31 Demo 01:55 Setup 02:54 Code 🔗 Links https://huggingface.co/spaces/Xenova/whisper-webgpu https://github.com/xenova/whisper-web/tree/experimental-webgpu ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## Video Summary: What is WebGPU Whisper ML Powered In Browser Transcription with Transformer.js **1. Executive Summary:** This video explores the implementation of WebGPU Whisper and Transformer.js for fast and private in-browser audio transcription across 100 languages. Unlike real-time transcription, this approach utilizes a pipeline and tasks for offline transcription from files, URLs, or recordings, ensuring all processing remains local and data doesn't leave the user's device. **2. Main Topics Covered:** * **Introduction to WebGPU Whisper:** Showcasing the offline transcription capabilities of WebGPU Whisper, built upon the earlier Whisper Web project. * **Demo of Functionality:** Demonstrating transcription from a URL, recording directly in the browser, and exporting the results. * **Codebase Overview:** A walkthrough of the codebase, focusing on the experimental WebGPU branch and highlighting key files (app.jsx, useTranscribe.jsx, worker.js). * **Implementation Details:** Explanation of the use of Transformer.js, Web Workers, and the pipeline factory pattern for managing transcription tasks. * **Audio Chunking and Processing:** Detailing how audio input is chunked, processed within the web worker, and transcribed. **3. Key Takeaways:** * **Offline Transcription:** WebGPU Whisper enables offline audio transcription directly in the browser without backend processing. * **Privacy:** Leveraging Transformer.js ensures that all data processing, including audio and transcription, remains local to the user's device, enhancing privacy. * **Performance:** WebGPU offers significantly faster processing compared to previous iterations, facilitating quick transcription times. * **Task-Based Pipeline:** The system employs a pipeline factory for automatic speech recognition, streamlining the transcription process and allowing for pre-defined tasks. * **Web Workers for Parallelism:** Web Workers facilitate parallel execution of tasks, enhancing performance and responsiveness. **4. Notable Quotes or Examples:** * "…nothing is it's getting loaded to the back end at all. It's saving everything in the browser so from the loading the audio file to actually getting the transcription is all done within the browser using uh Transformer JS." (Illustrates the local processing aspect.) * "…we're actually going to use tasks which is different than the real time example." (Highlights the difference from earlier implementations.) * Explanation of how the automatic speech recognition pipeline factory extends the pipeline factory defining the task like a "pre-trained or predefined task." * Mention of the ability to export transcriptions in STT or JSON formats. **5. Target Audience:** * Web developers interested in implementing in-browser audio transcription. * Machine learning engineers exploring the use of Transformer.js and WebGPU. * Privacy-conscious developers seeking to minimize data transmission. * Individuals interested in the advancements in in-browser ML processing. **Key Highlights:** - **WebGPU Whisper: Offline Transcription**: The video showcases Whisper WebGPU's ability to perform offline transcription tasks directly in the browser, utilizing files, URLs, or recordings, eliminating backend processing. - **Transformer.js and Web Workers**: The implementation leverages Transformer.js for ML processing and Web Workers for parallel execution, keeping all data processing localized within the browser. - **Pipeline Factory for Task Management**: The system uses a pipeline factory pattern with automatic speech recognition to define and manage the transcription task, streamlining the process. - **Audio Chunking for Processing**: The video explains how audio input is chunked and processed within the web worker, highlighting the data flow and steps for transcription. --- ### How to use multiple languages in VAPI **Published:** 6/3/2024 **Duration:** 17 minutes **YouTube:** https://youtube.com/watch?v=009YgFNkM5w **Full LLM Text:** /video/cmis30txp001704kwd680tz0l/llm.txt **Description:** Continuing from our previous video, we will delve into multilingual support for your AI voice bots. 🌐✨ In this part, you'll learn how to: - Enable multilingual capabilities in your VAPI voice app - Configure language detection and translation features - Enhance user experience with seamless language switching Stay tuned as we explore how Vapi and Next.js can help you build a truly global application, reaching users in their preferred languages. Don't miss out on this exciting journey into the future of AI-driven communication! 🚀 🔔 Make sure to like, comment, and subscribe for more tutorials and updates! Course: https://forms.gle/PN3RpKD8eHsicL9Z7 📰 News & Resources: https://sendfox.com/nerdingio 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 00:37 Vapi Documentation 03:26 Vapi Dashboard 06:46 11labs Setup 07:56 Demo 09:19 Local code 14:11 Dynamic Language Locale 15:14 Twilio 16:25 Bonus 🔗 Links https://docs.vapi.ai/multilingual https://dashboard.vapi.ai/ https://console.deepgram.com/ https://elevenlabs.io/app/voice-lab https://www.twilio.com/en-us/guidelines/br/voice https://www.twilio.com/en-us/guidelines/br/regulatory https://docs.vapi.ai/phone-calling ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** Here's a summary document based on the provided video information: **Video Summary: How to Use Multiple Languages in VAPI** **1. Executive Summary:** This video tutorial builds upon previous content, demonstrating how to implement multilingual support in VAPI voice applications using providers like Deepgram, 11Labs, and Twilio. It covers configuring language detection and translation both in the VAPI dashboard and dynamically within the code, showcasing how to create a more global and user-friendly AI voice experience. **2. Main Topics Covered:** * **Multilingual Support in VAPI:** Enabling and configuring multiple languages for transcription (speech-to-text) and text-to-speech (TTS) in VAPI applications. * **Transcription Provider Configuration:** Configuring Deepgram (focus on HIPAA compliance) and Talkscriber. * **Text-to-Speech Provider Options:** Discussing options like 11Labs (emphasizing voice cloning), Play.ht, Azure, and Deepgram. * **Dynamic Language Switching in Code (Next.js Example):** Implementing dynamic voice selection using voice IDs in a Next.js application for real-time language and accent changes. * **Integration with Twilio for Local Phone Numbers:** Connecting VAPI to Twilio to facilitate inbound and outbound calls using localized phone numbers. * **Navigating Regulatory guidelines:** Understanding and meeting the requirements to acquire local numbers **3. Key Takeaways:** * VAPI offers flexible multilingual support configurable both through the dashboard and dynamically in code. * Deepgram is a strong transcription provider, especially when HIPAA compliance is needed. * 11Labs enables voice cloning to add unique options to TTS. * Voice IDs can be used to dynamically select languages and accents within the code, improving personalization. * Integrating with Twilio allows VAPI applications to use localized phone numbers, requiring adherence to local regulations. * Utilize the "Ask Vapi" button for troubleshooting, which uses a custom chatbot. **4. Notable Quotes or Examples:** * "The two things that it specifies are you need to have your transcribers and you need to have your voice so your text to speech." * "You can configure this in the dashboard which would almost mean that it's hardcoded or you can actually configure the multilingual support in uh the Json object that you're actually sending" * Code snippets and examples are demonstrated using Next.js. * The Ask Vapi chatbot tool is a great resource to use when working with the API **5. Target Audience:** * AI Voice Bot Developers * VAPI Users and Enthusiasts * Next.js Developers integrating with VAPI * Individuals interested in building multilingual AI applications * Developers who are familiar with using Vapi and Next.js **Key Highlights:** - **Multilingual Support in VAPI**: VAPI supports multiple languages for both transcription and text-to-speech. Configuration can be done in the dashboard or dynamically within the JSON object. - **Transcription Providers: Deepgram & Talkscriber**: Deepgram and Talkscriber are mentioned as transcription providers. Deepgram stands out because of its HIPAA compliance features. - **Text-to-Speech Providers: Options Galore**: Various TTS providers like 11Labs, Play.ht, and Azure are discussed, with 11Labs highlighted for voice cloning. - **Dynamic Voice Selection in Code**: Voice IDs can be dynamically passed in the code (e.g., in a Next.js app) to change the language and accent on the fly, allowing for personalized user experience. - **Connecting VAPI to Twilio for Phone Numbers**: Integrating VAPI with Twilio allows for inbound and outbound calls with localized numbers. Considerations include ISO codes and local regulations. --- ### How to configure Vapi Ai Voice Bot Functions With Next.js **Published:** 5/2/2024 **Duration:** 12 minutes **YouTube:** https://youtube.com/watch?v=q9eZZA4VCNM **Full LLM Text:** /video/cmis3162l001804kw7i4kjyvd/llm.txt **Description:** 🗣️ Discover the incredible potential of AI voice bots with Vapi and Next.js! 🤖 In this video, we'll guide you through the process of integrating Vapi's powerful AI voice bot functions into your Next.js applications. You'll learn how to: - Setup a Vapi NextJS example - Learn about display and webhook functions - Create a transcription download button Unlock a new level of user engagement and interactivity by harnessing the combined power of Vapi and Next.js. 💪 Course: https://forms.gle/PN3RpKD8eHsicL9Z7 📰 News & Resources: https://sendfox.com/nerdingio 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 00:29 Dashboard 01:21 Demo 03:07 Setup 05:28 Code 09:09 Transcription 10:57 Final 🔗 Links https://vapi.ai/ https://docs.vapi.ai/resources https://github.com/VapiAI/web https://github.com/VapiAI/client-side-example-javascript-next ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## Vapi AI Voice Bot with Next.js: Video Summary **1. Executive Summary:** This video provides a comprehensive overview of Vapi, an AI voice bot platform, and demonstrates its integration with Next.js for creating interactive and dynamic voice applications. It covers setting up a Vapi Next.js example, leveraging functions for dynamic UI updates, and generating transcript downloads. **2. Main Topics Covered:** * **Introduction to Vapi:** Overview of the platform and its capabilities as a developer-friendly AI voice bot. * **Vapi Dashboard Walkthrough:** Quick tour of the Vapi dashboard for creating and testing voice assistants. * **Next.js Integration:** Setting up a local Next.js example provided by Vapi. * **Functions and Webhooks:** Explanation of how Vapi utilizes functions and webhooks to dynamically update the UI based on conversation flow. * **Model Flexibility:** Ability to switch between different AI models, including OpenAI-compatible ones. * **Transcription and Automation:** Generating real-time transcripts and creating a download button, opening doors for post-call automation. * **Assistant Configuration:** Building assistants via the dashboard or JSON objects. **3. Key Takeaways:** * **Vapi simplifies AI voice bot development:** Offers a developer-friendly approach with excellent integrations like Next.js. * **Functions enable dynamic UI:** The use of functions and webhooks provides the ability to dynamically update the user interface during a voice interaction. * **OpenAI Compatibility:** Supports a wide range of AI models, including those compatible with the OpenAI chat completion API. * **Transcription opens automation possibilities:** Real-time transcription and download capability enables post-call automation and data analysis. * **Flexible configuration:** Assistants can be configured via the dashboard or programmatically through JSON definitions. **4. Notable Quotes or Examples:** * "[Vapi is] basically a voice bot that integrates really well so it's perfect for developers to go ahead and put together a AI voice bot application" * "We can actually use different providers... or other providers, but we can also use OpenAI compatible models which is really interesting." * "[Functions allow you] just like the assistant API to actually do a call out to something else." * "We can now change the display based on the functions that are going back and forth from our call." * Demonstration of booking Broadway show tickets using a voice assistant built with Vapi and Next.js. **5. Target Audience:** * Web developers interested in integrating AI voice bot functionality into their Next.js applications. * Developers exploring voice-based UI and conversational AI. * Individuals looking for a developer-friendly AI voice bot platform. * Individuals interested in creating automated transcriptions. **Key Highlights:** - **Vapi: Voice Bot with Developer-Friendly Integrations**: Vapi provides an easy-to-integrate voice bot solution tailored for developers, with support for various ecosystems like Next.js. It allows building AI voice bot applications quickly. - **Functions Enable Dynamic UI Updates**: Vapi utilizes functions to dynamically change the user interface based on the conversation flow. Webhooks are triggered by specific functions, updating the display based on the responses. - **Flexible Model Support with Open AI Compatibility**: Vapi offers the flexibility to switch between different AI models, including those compatible with the OpenAI chat completion API, allowing developers to choose the best model for their needs. - **Real-time Transcription and Post-Call Automation**: Vapi provides real-time transcription and the ability to extract messages and create transcripts for download, enabling post-call automation and analysis. - **Customizable Assistants via Dashboard or JSON**: Vapi assistants can be built either through the dashboard interface or by defining them as JSON objects, providing flexibility in configuration and management. --- ### Steps to Create Music With Transformer.js **Published:** 4/26/2024 **Duration:** 11 minutes **YouTube:** https://youtube.com/watch?v=-c0WnXylgSw **Full LLM Text:** /video/cmis31lhd001904kwz615amjx/llm.txt **Description:** 🎵 Create AI-generated music in your browser with Transformer JS! 🎧 In this video, we explore the power of Transformer.js for generating unique musical compositions. You'll learn how to: - Set up Transformer JS - Generate and customize melodies, harmonies, and rhythms - Integrate AI-generated music into your web apps Whether you're a music enthusiast or a web developer, discover the future of music creation with TransformerJS. 🚀 Course: https://forms.gle/PN3RpKD8eHsicL9Z7 📰 News & Resources: https://sendfox.com/nerdingio 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 00:20 Demo 03:07 Model 04:48 Code 08:03 Generate 10:16 Final 🔗 Links https://huggingface.co/spaces/Xenova/musicgen-web https://huggingface.co/Xenova/musicgen-small https://github.com/xenova/transformers.js/tree/v3 ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** Here's a comprehensive summary document for the "Steps to Create Music With Transformer.js" video: **1. Executive Summary** This video tutorial demonstrates how to generate AI music directly in the browser using Transformer.js, specifically leveraging the MusicGen small model from Hugging Face. It covers setting up the environment, adapting the pre-trained model for use in JavaScript, and generating a .wav music file client-side. **2. Main Topics Covered** * **Introduction to Transformer.js for Music Generation:** Overview of using Transformer.js for creating music in the browser. * **Demo Walkthrough:** Exploring the MusicGen demo on Hugging Face Spaces. * **Model Explanation:** Details about the MusicGen small model and its implementation. * **Code Implementation:** A step-by-step guide on setting up a JavaScript environment (using npm), importing necessary libraries (transformers.js, wavefile), and writing the code to load the model, process the prompt, and generate music. * **Generating Music:** Running the JavaScript code to generate a .wav file and testing the output. * **Adapting Hugging Face Spaces code:** How to clone the repo, reverse engineer, and adapt the HF Spaces code for use in a local Javascript setting. **3. Key Takeaways** * **Client-Side Music Generation:** Transformer.js enables music creation within the browser, eliminating backend costs and latency. * **Pre-trained Models:** Utilizes Hugging Face's MusicGen small model for easy integration into projects. * **WebAssembly (WASM) Power:** Emphasizes the role of WASM for efficient client-side processing. Future technology, WebGPU, could significantly improve latency. * **Practical Implementation:** Provides a clear code example to download, set up, and use Transformer.js for music generation. * **Reverse Engineering a Compiled Implementation:** The presenter explains how to take the compiled code from a Hugging Face space and adapt it for local use. **4. Notable Quotes or Examples** * "The fact that this is happening in the browser and happening for free is super cool." * Explanation of adapting the HuggingFace implementation: "Although the HF Spaces code is compiled, the presenter reverse engineers, shows how to clone the repo, and adapt the code to generate music in a local javascript setting." * Code examples provided throughout the video show how to import libraries, load the model, and generate the .wav file. **5. Target Audience** * Web developers interested in integrating AI-generated music into their applications. * Music enthusiasts curious about the capabilities of AI in music creation. * Individuals familiar with JavaScript, npm, and basic machine learning concepts. **Key Highlights:** - **Music Generation in the Browser**: The video demonstrates how to generate music directly in the browser using Transformer.js, eliminating backend costs and dependencies. - **Leveraging Pre-trained Models**: The tutorial utilizes the MusicGen small model from Hugging Face, showcasing how to download and implement a pre-trained model for music generation. - **Client-Side Processing with WASM**: The process relies on WebAssembly (WASM) for efficient client-side computation. WebGPU is mentioned as a future technology that could improve latency. - **Implementation in JavaScript**: The video walks through the JavaScript code needed to load the model, process the prompt, and generate the music file, even generating a .wav file with 'wavefile' npm package. - **Adapting Hugging Face Spaces code**: Although the HF Spaces code is compiled, the presenter reverse engineers, shows how to clone the repo, and adapt the code to generate music in a local javascript setting. --- ### How to use Langsmith's Unique Custom Traces! #langchain #ai **Published:** 4/4/2024 **Duration:** 12 minutes **YouTube:** https://youtube.com/watch?v=NGVLL6-w2K4 **Full LLM Text:** /video/cmis31wnu001a04kwk9dokfc9/llm.txt **Description:** In this video, we'll cover how to easily customize various attributes of the traces you log to LangSmith, LangChain's open-source tracing and debugging tool. You'll learn how to: - Set custom trace names - Add extra metadata to traces - Tag traces for easier filtering - Adjust the level of detail captured Customizing traces allows you to track and debug your LangChain applications more effectively. With just a few lines of code, you can enrich your traces with the exact information you need. Whether you're a LangChain beginner or power user, this quick guide will level up your tracing and debugging workflow with LangSmith. Course: https://forms.gle/PN3RpKD8eHsicL9Z7 📰 News & Resources: https://sendfox.com/nerdingio 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 00:36 Understanding Traces 01:27 Custom Attributes 02:20 NextJS Example 05:45 Trace Attributes 07:28 Masking inputs and outputs 🔗 Links https://github.com/nerding-io/langchain-nextjs-example https://docs.smith.langchain.com/tracing/faq/customizing_trace_attributes https://smith.langchain.com/ ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## Langsmith Custom Trace Attributes Video Summary **1. Executive Summary:** This video tutorial explains how to customize Langsmith traces, LangChain's open-source tracing and debugging tool, for enhanced application monitoring and debugging. It covers customizing trace names, adding metadata and tags, and masking sensitive input/output data, enabling developers to better organize and filter their trace data. **2. Main Topics Covered:** * **Understanding Langsmith Traces:** Overview of how Langsmith logs and traces function within LangChain, particularly in the context of JavaScript and Next.js applications. * **Custom Trace Attributes:** Modification of trace attributes like project name, run name, metadata, and tags directly within the code. Defining tracers, metadata and tags when invoking chains. * **Next.js Example:** Demonstrates implementation of custom trace attributes in a Next.js application, including routes for traces, masking, and project management. * **Masking Input/Output Data:** Explanation of how to selectively hide input or output data within Langsmith traces for privacy or security reasons, differing from traditional PII masking. * **Switching Between Filtered and Unfiltered Tracers:** Demonstrates the ability to alternate between filtered and unfiltered tracers within the same Langsmith project for different levels of detail. **3. Key Takeaways:** * Langsmith allows dynamic customization of trace attributes at runtime for enhanced debugging and organization. * Metadata and tags can be added and modified at runtime during chain invocations, enabling contextual logging. * Selective masking of input/output data is possible, providing control over logged information for security or privacy. * Developers can switch between filtered and unfiltered tracers within the same project for flexible logging detail. * Customizing run names provides clarity and easy identification within the Langsmith dashboard. **4. Notable Quotes or Examples:** * "So what that means is we can change things like project name run name even things like metadata or tags but most importantly we can also actually mask different type of input and output information." * Example of defining the Langchain Tracer project name in code: `tracer.project_name = "LangSmith code for my project";` * Example of hiding inputs and output using the Langsmith client: `hide_inputs=True`, `hide_outputs=True` * Showed example of adding Metadata in the llm invoke. **5. Target Audience:** * LangChain developers and engineers * AI application developers using Langsmith for debugging and tracing * Individuals seeking to enhance their LangChain debugging workflow * Developers interested in customizing trace attributes for better monitoring **Key Highlights:** - **Customizing Langsmith Trace Attributes**: Langsmith allows customizing trace attributes like project name, run name, metadata, and tags for enhanced debugging and organization of logged data. - **Dynamic Metadata & Tag Configuration**: Metadata and tags can be added and modified at runtime during chain invocations, enabling contextual logging and easier identification of specific log entries. - **Masking Sensitive Input/Output Data**: Langsmith enables selective masking of input or output data in traces, allowing control over which information is logged for privacy or security reasons. Not the same as pii masking but removes info. - **Switching Between Filtered and Unfiltered Tracers**: It's possible to switch between filtered and unfiltered tracers within the same Langsmith project, providing flexibility in logging different levels of detail. --- ### How to Stream With OpenAI's Assistant API **Published:** 3/27/2024 **Duration:** 14 minutes **YouTube:** https://youtube.com/watch?v=JnWlw5jIzFY **Full LLM Text:** /video/cmis329l3001b04kwozn0hn3u/llm.txt **Description:** In this tutorial, we'll walk you through the process of setting up a scalable and efficient backend architecture that leverages the power of serverless computing and artificial intelligence. You'll learn how to integrate OpenAI's API with Vercel's AI package, enabling functions and tools with streaming experiences. Course: https://forms.gle/PN3RpKD8eHsicL9Z7 📰 News & Resources: https://sendfox.com/nerdingio 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 00:20 New Release 01:44 Review Streaming 03:45 Home Automation Example 04:18 OpenAI Assistant Setup 06:53 Vercel AI 08:10 Streaming Example 10:37 Frontend 12:01 OpenAI Functions 14:27 Conclusion 🔗 Links https://github.com/vercel/ai/releases https://github.com/vercel/ai/blob/main/examples/next-openai/app/api/assistant/assistant-setup.md https://platform.openai.com/assistants ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## Video Summary: How to Stream With OpenAI's Assistant API **1. Executive Summary:** This tutorial demonstrates how to leverage Vercel AI's new streaming capabilities for OpenAI's Assistant API, enabling real-time and interactive experiences with custom tools and functions. The video provides a practical example of a home automation assistant, walking through setup, code implementation, and error handling. **2. Main Topics Covered:** * **Introduction to Vercel AI's Streaming for OpenAI Assistants:** Discusses the new features released in the Vercel AI package, focusing on streaming, forward stream response type, and the `useAgent` hook. * **OpenAI Assistant Setup:** Guides users through setting up an assistant in the OpenAI platform, including instructions, function definitions (using JSON schemas), and obtaining the assistant ID. * **Vercel AI Implementation:** Explains how to clone the Vercel AI repository, install dependencies, and configure environment variables (OpenAI API key and Assistant ID). * **Streaming Example (Home Automation Assistant):** Demonstrates a practical application involving a home automation assistant that controls room temperature, showcasing function calling and data handling. * **Frontend Implementation and Error Handling:** Discusses the frontend code using the `useAssistant` hook, highlighting its ability to handle errors (e.g., 429 errors) gracefully and providing appropriate responses to the user. * **Function Calling and Data Role:** Explains how to define and call functions in the OpenAI assistant, and how to use the 'data' role to style and present function results within the chat interface. **3. Key Takeaways:** * Vercel AI's streaming support for OpenAI Assistants enables real-time responses and interaction with custom tools. * The `useAssistant` hook allows for easier integration, error handling, and customization of the frontend experience. * Function calling enables the assistant to interact with external APIs and perform specific tasks. * The 'data' role is crucial for properly formatting and displaying function results in the chat interface. * The video provides a clear step-by-step guide to setting up and implementing a functional streaming OpenAI assistant with Vercel AI. **4. Notable Quotes or Examples:** * **On the new patch:** "Instead of the message context we're actually pulling in this forward stream so our data is kind of staying the same but instead of our run we're actually running a stream." * **On Function Definition:** Explanation of the JSON schemas used to define functions like `get_room_temperature` and `set_room_temperature`, including enum-based validation. * **On the benefits of `useAssistant`:** "What's really interesting about this is if it fails say you get a 429 or something like that or some other error you could actually capture that and have your front end respond with an appropriate response." * **On the 'data' role:** "If the role is data then what we're going to do is a pre- element and actually put the JSON stringified result in here... that's how we're getting our our uh styling around it." **5. Target Audience:** * Developers interested in building AI-powered applications with OpenAI's Assistant API. * Frontend and backend engineers looking to implement streaming capabilities in their applications. * Individuals familiar with JavaScript/TypeScript, React, Next.js, Vercel, and OpenAI. * Developers seeking a practical example of integrating OpenAI Assistants with custom tools and functions. **Key Highlights:** - **Streaming OpenAI Assistant API**: Vercel AI package now supports streaming with the OpenAI Assistant API, enabling real-time responses and interactions. - **Forward Stream Type & Use Agent Hook**: New features include a forward stream response type and the ability to use the use agent hook for set messages, providing more control and type safety. - **Home Automation Assistant Example**: The video demonstrates a practical example using a home automation assistant to control room temperature, showcasing function calling. - **Error Handling with Use Assistant**: The `useAssistant` hook allows for graceful error handling, enabling the front end to respond appropriately to issues like 429 errors. - **Data Role for Structured Responses**: Returning data with a 'data' role allows for specific styling and presentation of function results within the chat interface. --- ### Steps to Build a Real-Time Voice Bot with Deepgram, Langchain, and ChatGPT **Published:** 2/8/2024 **Duration:** 16 minutes **YouTube:** https://youtube.com/watch?v=EgNerWaeZz0 **Full LLM Text:** /video/cmis32l91001c04kwzozmojjc/llm.txt **Description:** In this video, I show an experiment example of a real-time voice bot using Websockets, Deepgram for speech recognition, Langchain for natural language processing, and ChatGPT by OpenAI for dialogue generation. The flow works like this: - User speaks into a microphone - Deepgram transcribes the audio into text in real-time - Langchain processes the text to understand the user's intent - ChatGPT generates a relevant voice response - Text to speech converts the response into an audio clip - The audio clip is played back to the user So essentially, it allows you to have a natural conversation with an AI assistant by speech. The bot understands what you're saying and responds back verbally in real-time. Some use cases could be creating smart speakers, voice interfaces for apps, or even phone call bots. The real-time aspect makes it more convenient than typing back and forth. 📰 FREE Code & News: https://sendfox.com/nerdingio 👉🏻 Ranked #1 Product of the Day: https://www.producthunt.com/posts/ever-efficient-ai 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 00:28 Deepgram 02:33 Real Time Bot Example 04:10 Setup 05:58 Client 10:02 Server 13:07 Testing 14:32 Langsmith 🔗 Links https://deepgram.com/ https://github.com/deepgram-starters/live-nextjs-starter https://github.com/deepgram-previews/real-time-voice-bot https://js.langchain.com/docs/integrations/chat/openai https://smith.langchain.com/ ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## Summary Document: Steps to Build a Real-Time Voice Bot with Deepgram, Langchain, and ChatGPT **1. Executive Summary:** This video demonstrates how to build a real-time voice bot using Deepgram for speech-to-text, Langchain for natural language processing and prompt management, and ChatGPT for generating conversational responses. The tutorial covers the setup, code structure, and demonstrates the bot's functionality, showcasing its real-time transcription speed and conversational capabilities. **2. Main Topics Covered:** * **Introduction to Real-Time Voice Bots:** Explanation of the concept and potential use cases (smart speakers, voice interfaces, phone call bots). * **Deepgram for Real-Time Transcription:** Overview of Deepgram's capabilities, focusing on its speed, accuracy, and live streaming transcription feature. * **Websocket Implementation:** Explanation of how websockets maintain a persistent, two-way connection for real-time communication between the client and server. * **Langchain Integration:** Description of how Langchain is used for prompt management, including model selection and defining agent behavior. * **ChatGPT for Dialogue Generation:** Overview of how ChatGPT is used to generate relevant and engaging voice responses based on user input. * **Code Walkthrough:** Detailed explanation of the client-side (Next.js, Javascript, Socket.io) and server-side code (Node.js, Deepgram TTS API, Langchain) implementation. * **Langsmith Tracing:** Demonstration of using Langsmith to monitor latency, token usage, and the overall flow of the conversation within the Langchain sequence. * **Socket ID Management:** Importance of using Socket IDs for managing concurrent chat sessions and consolidating chat history for each individual user. **3. Key Takeaways:** * **Deepgram's speed makes it ideal for real-time voice bots.** Its real-time transcription capabilities allow for a more natural conversational experience. * **Websockets are crucial for maintaining persistent connections and reducing latency** in real-time applications. * **Langchain simplifies prompt management** and allows for easy integration with LLMs like ChatGPT. * **Server-side processing optimizes performance** by handling computationally intensive tasks like TTS and prompt handling. * **Langsmith provides valuable insights into latency and token usage**, enabling optimization and debugging. * **Socket IDs are essential for managing multiple concurrent chat sessions**, ensuring that each user has a personalized experience. **4. Notable Quotes or Examples:** * "So you could see that the transcript was happening in real time right here..." - Demonstration of Deepgram's real-time transcription. * "...it's almost like a conversational dot but in the sense that it's using audio and you could see that very quickly it was responding there wasn't as much latency as you would typically see which I found really interesting" - Highlighting the speed and low latency of the system. * Example Chat Interaction: Demonstrates the bot's ability to understand the user's intent and provide relevant responses (therapist scenario). **5. Target Audience:** * Developers interested in building real-time voice-based applications. * Individuals exploring the integration of speech recognition, NLP, and LLMs. * Anyone seeking to understand the use of Deepgram, Langchain, ChatGPT, and Websockets in a practical project. * Those interested in conversational AI and voice interfaces. **Key Highlights:** - **Real-Time Transcription Speed with Deepgram**: Deepgram's real-time transcription capabilities are highlighted as exceptionally fast, making it a strong contender for voice-based conversational bots. The live streaming transcription feature is emphasized. - **Websockets for Persistent Connection**: The use of websockets for maintaining a persistent, two-way connection between the front-end and back-end is crucial for the real-time voice bot's functionality and reducing latency. - **Langchain Integration & Langsmith Tracing**: The integration of Langchain for prompt management and the ability to trace the entire process, including latency and token usage, through Langsmith is a key aspect of the project. - **Socket ID for Chat Management**: The video stresses the importance of using socket IDs to manage multiple concurrent chat sessions and consolidate chat history for each individual user within the Langchain sequence. - **Server-Side Processing for Speed**: The bulk of heavy processing, especially TTS and prompt handling, is executed server-side. Utilizing buffer arrays for streaming audio allows near real-time audio feedback from the bot. --- ### How to Secure Data in AI: Essential Guide to PII Masking in LangChain **Published:** 1/31/2024 **Duration:** 19 minutes **YouTube:** https://youtube.com/watch?v=54vUy5kvHRI **Full LLM Text:** /video/cmis32xa6001d04kw497zhhn6/llm.txt **Description:** Discover key strategies for securing sensitive data in AI systems with LangChain's experimental feature of PII masking. This video offers a deep dive into an advanced masking parser and transformer tool, crucial for protecting personal information in customer support interactions. Ideal for AI professionals and enthusiasts, learn how to navigate data privacy in the AI realm effectively, ensuring compliance and safeguarding customer trust. Dive in for practical insights into AI data security! 📰 FREE Code & News: https://sendfox.com/nerdingio 👉🏻 Ranked #1 Product of the Day: https://www.producthunt.com/posts/ever-efficient-ai 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Intro 00:51 Real World Scenario 02:07 Getting Started 03:22 Basic Example 05:52 Kitchen Sink Example 10:49 Customer Support Example 17:36 LangSmith 19:00 Conclusion 🔗 Links https://github.com/nerding-io/langchain-nextjs-example https://js.langchain.com/docs/modules/experimental/mask/ https://smith.langchain.com/ ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## Video Summary: How to Secure Data in AI: Essential Guide to PII Masking in LangChain **1. Executive Summary:** This video provides a practical guide to using LangChain JS's experimental PII masking feature to protect sensitive customer data in AI applications, specifically customer support systems. It demonstrates how to implement customizable regex-based masking, utilize rehydration capabilities, and integrate with LangSmith to ensure data privacy throughout the LLM process. **2. Main Topics Covered:** * **Introduction to LangChain JS Masking:** Overview of the experimental masking feature for PII removal. * **Real-World Scenario:** Application in customer support systems for masking sensitive user data before it's processed by LLMs. * **Basic Masking Example:** Demonstrates simple text masking using regex and the masking parser, focusing on masking and rehydrating email addresses and phone numbers. * **"Kitchen Sink" Example:** Shows a more complex scenario with various regex patterns for different PII types (names, bank accounts, etc.) and event hooks for masking and hydrating stages, highlighting potential challenges with regex accuracy. * **Customer Support Stream Example:** Uses a full chat history with PII to demonstrate masking in a streaming context. This includes using a prompt template for summarizing conversations, masking the chat history, and ensuring sensitive data is masked when passed to the LLM. * **LangSmith Integration:** Illustrates how masked data flows through LangChain runnable sequences and is visualized in LangSmith, preventing sensitive information from being stored in third-party LLMs. * **Rehydration Capabilities:** Demonstrates how to reverse the masking process to access the original data when needed, while acknowledging potential security considerations. **3. Key Takeaways:** * LangChain JS offers an experimental masking feature to protect PII. * Regex is used to customize masking patterns, but requires careful attention to accuracy. * Masking is reversible (rehydration), enabling access to original data when necessary. * Integration with LangSmith ensures data privacy throughout the LLM pipeline. * Masking can be applied to chat histories in streaming scenarios. * Careful planning of regex patterns is crucial to avoid unintended masking and maintain accuracy. * While powerful, the rehydration capability poses a potential security risk if not properly managed. **4. Notable Quotes or Examples:** * "…you're actually getting information from users and you need to scrub that data so that when you're putting it into your llm it's actually masked from the actual information that it needs…" (Explanation of the problem PII Masking solves) * The video showcases specific examples for email, phone numbers, names, bank account numbers, driver's license numbers, and passport numbers being masked using regex. * The Customer Support Example provides a specific code example which utilizes a full UI, stream, and runnable sequence to summarize data after it has been properly masked. **5. Target Audience:** * AI professionals * Machine learning engineers * Data scientists * Developers working with LangChain and LLMs * Individuals interested in data privacy and security in AI **Key Highlights:** - **LangChain JS Masking Feature**: The video focuses on LangChain JS's experimental masking feature for PII removal, showcasing its application in customer support scenarios to protect sensitive user data before it reaches LLMs. - **Regex-Based Masking Customization**: The masking process leverages regex patterns, allowing for flexible customization of what data gets masked. However, the presenter notes that some regex patterns may require adjustments for proper functionality. - **Rehydration Capability**: The masking is reversible (rehydration), which is key for accessing the original data when needed. This isn't a one-way process and has some potential security implications if not handled correctly. - **LangSmith Integration for Masking**: The video demonstrates how masked data is passed through LangChain's runnable sequences and visualized in LangSmith, ensuring that sensitive information is not stored in third-party LLMs. - **Stream Route with Utilities**: Addresses the use case of chat history masking, where an entire conversation with sensitive PII data can be summarized while protecting this information from the LLM. --- ### How to Convert AI Models for Local Use in Your Browser with Transformer.js **Published:** 1/2/2024 **Duration:** 19 minutes **YouTube:** https://youtube.com/watch?v=CA_d08EIGCc **Full LLM Text:** /video/cmis339w3001e04kw9cv5m1vn/llm.txt **Description:** In this video, I will guide you through the process of converting and running Transformer JS models locally. We will start by exploring the Transformer.js documentation and understanding how it uses the Onyx runtime to run models in the browser. Then, we will learn how to convert pre-trained models using Optimal and configure the local model settings. I will demonstrate the steps to clone the repository, create a virtual environment, install the requirements, and run the scripts to export the models. Finally, we will test the local model loading and make necessary configurations. No action is required from you, but this video will provide you with valuable insights into running AI models offline. 📰 FREE eBooks & News: https://sendfox.com/nerdingio 👉🏻 Ranked #1 Product of the Day: https://www.producthunt.com/posts/ever-efficient-ai 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Intro 01:22 Custom Usage 02:50 Convert Script 04:24 Supported Models 05:25 Commands 07:11 Model ID's 07:58 Conversion 09:21 NextJS Client Example 11:17 Browser Testing 12:20 Browser Generation 13:36 Configuration 15:31 Browser Debugging 19:00 Conclusion 🔗 Links https://huggingface.co/docs/transformers.js/index#convert-your-models-to-onnx https://huggingface.co/docs/transformers.js/custom_usage https://github.com/xenova/transformers.js/blob/main/scripts/convert.py ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## Video Summary: How to Convert AI Models for Local Use in Your Browser with Transformer.js **1. Executive Summary:** This video provides a step-by-step guide on converting pre-trained AI models to run locally in a web browser using Transformer.js and ONNX runtime. It covers model conversion using Optimal, configuration of local model settings, and debugging techniques for ensuring models load from the local server instead of remote sources like Hugging Face. **2. Main Topics Covered:** * **Introduction to Transformer.js and Local Model Usage:** Overview of using Transformer.js to run AI models directly in the browser. * **Model Conversion using Optimal:** Cloning the repository, creating a virtual environment, installing requirements, and running Python scripts (`convert.py` and `supported_models.py`) to export models in ONNX format. * **Configuration for Local Model Loading:** Modifying the `worker.js` file in a Next.js project to prioritize local models by setting `allowLocalModels` to true and `allowRemoteModels` to false. * **Testing and Debugging:** Using browser developer tools (Network tab) to verify that models are loaded from localhost instead of remote servers and using Application -> Storage -> Clear Site Data for force re-download to test. * **Application Beyond the Browser:** Discusses the potential for integrating locally run models into Chrome extensions, React Native apps, and Electron apps for offline AI functionality. **3. Key Takeaways:** * Transformer.js allows running AI models locally in the browser using ONNX runtime, eliminating the need for constant API calls to remote servers. * Utilizing the `convert.py` script is essential for converting pre-trained models into a browser-compatible format. * The `supported_models.py` script provides a crucial list of supported models and tasks, aiding in proper configuration. * Setting `allowLocalModels` to true and `allowRemoteModels` to false in `worker.js` is critical for forcing the browser to use the locally stored model. * Browser developer tools are invaluable for debugging model loading issues and ensuring models are served from localhost. * Local model hosting unlocks possibilities for embedding AI functionality in diverse applications beyond the browser. **4. Notable Quotes or Examples:** * **(Regarding model ID)** "This is actually going out and looking at either uh the tasks that you are that you may want to use or the um the models specifically that they already have supported through hugging face because what happens is when you load this model it's actually making an API call to the through the browser to hugging face to pull this model back" * **(Local model usage)**: The video emphasizes that enabling local models opens the door for integrations with Chrome extensions, React Native applications, and Electron apps. * **(Debugging method)** : "what we can do is go to this application click on storage go ahead and clear your site and then when we go back to the network we'll actually see it load again" **5. Target Audience:** * Web developers interested in implementing AI functionalities in their applications. * Data scientists and machine learning engineers looking to deploy models directly in the browser for offline use cases. * Developers working on Chrome extensions, React Native apps, or Electron apps that require local AI processing. * Anyone interested in exploring the capabilities of Transformer.js for client-side AI model deployment. **Key Highlights:** - **Converting AI Models for Local Use**: Transformer.js allows converting pre-trained AI models to run locally in a browser using ONNX runtime, eliminating reliance on remote API calls. - **Key Configuration: Local vs. Remote Models**: Setting 'allowLocalModels' to true and 'allowRemoteModels' to false in the worker.js file ensures the browser uses the locally stored model, improving speed and enabling offline use. - **Finding Supported Models and Tasks**: The 'supported_models.py' script provides a list of supported models and their associated tasks, crucial for configuring the conversion process and model loading. - **Debugging with Browser Developer Tools**: The network tab in browser dev tools helps verify that models are loaded from localhost instead of remote servers like Hugging Face. Clearing site data forces re-download for testing. - **Expanding Beyond the Browser**: Locally running models enables integration into Chrome extensions, React Native apps, and Electron apps, facilitating offline AI functionality and local data processing. --- ### How to: Visualizing Your Neural Networks **Published:** 12/3/2023 **Duration:** 9 minutes **YouTube:** https://youtube.com/watch?v=qh6oTF1Dj38 **Full LLM Text:** /video/cmis33l2e001f04kwub440hek/llm.txt **Description:** 🚀 Elevate Your AI Game with Netron! 🧠💻 Dive into the world of AI with our latest video, where we explore Netron - a powerhouse tool for visualizing neural networks, deep learning, and machine learning models. Netron isn't just any visualizer; it supports a wide range of formats, including ONNX, TensorFlow Lite, Core ML, Keras, and more, even extending experimental support to PyTorch, TensorFlow, and others. 🔍 Want to see AI models in action? Netron's got you covered. Sample model files are available for a hands-on experience with formats like ONNX, TensorFlow Lite, and Keras. Just download or open them using the browser version. Also, we will look at an ONNX file converted to be used in the browser. Lastly, we use ChatGPT Vision to explain the visual concepts in further detail. 📰 FREE eBooks & News: https://sendfox.com/nerdingio 👉🏻 Ranked #1 Product of the Day: https://www.producthunt.com/posts/ever-efficient-ai 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 00:37 Netron 01:56 Browser View 02:39 Connections 03:55 Electron 04:58 Onnx 06:25 Bert 07:47 GPT Vision 09:28 Conclusion 🔗 Links https://github.com/lutzroeder/netron https://github.com/lutzroeder/netron/blob/main/source/tflite.js https://netron.app/?url=https://media.githubusercontent.com/media/onnx/models/main/vision/classification/squeezenet/model/squeezenet1.0-3.onnx https://netron.app/?url=https://huggingface.co/thelou1s/yamnet/blob/main/lite-model_yamnet_tflite_1.tflite https://netron.app/?url=https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolo.cfg https://chat.openai.com/c/826fefcb-ca8e-4e23-ad88-810bc934c6c ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## Video Summary: How To: Visualizing Your Neural Networks **1. Executive Summary:** This video introduces Netron, a powerful visualization tool for neural networks, deep learning, and machine learning models. It showcases how Netron can be used to understand model architecture, logic, and connections, even demonstrating its capabilities through browser-based implementations and leveraging ChatGPT Vision for deeper analysis. **2. Main Topics Covered:** * **Introduction to Netron:** Overview of Netron as a visualizer for neural networks supporting various formats like ONNX, TensorFlow Lite, and others. * **Browser-Based Visualization:** Demonstrates using Netron in a browser, accessing sample models directly from URLs, and exploring their structure interactively. * **Model Connections and Attributes:** Explains how to examine connections between nodes in the network and view the attributes of each node. * **Local Application (Electron):** Shows how to use the Netron desktop application to visualize models stored locally, specifically an ONNX model used in a Chrome extension. * **ONNX Conversion and WebAssembly:** Highlights the process of converting TensorFlow models to ONNX for browser compatibility using WebAssembly, enabling client-side model execution. * **ChatGPT Vision Integration:** Explores using ChatGPT Vision to analyze screenshots of model architectures (YOLO, BERT), interpreting layers, parameters, and functions. **3. Key Takeaways:** * Netron simplifies understanding complex neural network architectures by providing a visual representation of models. * Netron supports a wide range of formats, making it a versatile tool for various machine learning projects. * Browser-based Netron allows easy access and exploration of models without the need for local installation. * Converting models to ONNX format facilitates browser-based execution through WebAssembly. * ChatGPT Vision can assist in interpreting model architectures and understanding the function of individual layers and parameters, especially useful when documentation is lacking. **4. Notable Quotes or Examples:** * "[Netron allows you to] actually see what is the logic that is happening here. You can click and dive into some of the more details of like what are the attributes, the connections which we'll kind of go through." - JD, Video Presenter * Demonstration of visualizing ONNX, TensorFlow Lite, and YOLO models directly in the browser. * Explanation of how the ONNX model (originally Tensorflow) from the Chrome extension runs client-side in the browser via WebAssembly. * ChatGPT Vision identifying layers and parameters in a YOLO architecture screenshot. * ChatGPT Vision explaining Layer Normalization and token ID processes in an ONNX BERT model screenshot. **5. Target Audience:** * Machine learning engineers and researchers * AI developers working with neural networks * Individuals seeking to understand and visualize model architectures * Developers interested in browser-based AI applications and WebAssembly. **Key Highlights:** - **Neutron: Visualizing Neural Network Architecture**: Neutron is a tool for visualizing neural networks, supporting formats like ONNX and TensorFlow Lite. It allows users to explore model logic, connections, and attributes, enhancing understanding of the model's inner workings. - **Browser-Based Visualization with WebAssembly**: The video demonstrates how to use ONNX models in the browser via WebAssembly (WASM). This enables client-side model execution without needing server-side APIs, showcasing a Chrome extension example using a sentiment analysis model. - **Leveraging ChatGPT Vision for Model Explanation**: The video explores using ChatGPT Vision to analyze screenshots of neural network architectures (YOLO, BERT). ChatGPT Vision can identify layers, parameters, and functions, aiding in understanding complex models when documentation is lacking. - **Converting Models to ONNX for Browser Compatibility**: The presenter highlights the ability to convert models, such as those originally in TensorFlow format, into the ONNX format. This conversion is crucial for enabling efficient execution within a browser environment using WebAssembly. --- ### Would You Try a GPT-4 Vision to Web Development Tool? **Published:** 11/20/2023 **Duration:** 7 minutes **YouTube:** https://youtube.com/watch?v=sj9KZtFfF1E **Full LLM Text:** /video/cmis342f7001g04kwge2oypbn/llm.txt **Description:** Use GPT-4 Vision Transform Screenshots into HTML, CSS, JS Code 🌟 Discover the magic of "screenshot-to-code", an open source AI tool that converts screenshots into HTML, Tailwind CSS, and JavaScript code. This tutorial delves into the capabilities of OpenAI's GPT-4 Vision and DALL-E 3 in web design and development. What You'll Learn: - AI in Web Development: Unveiling the potential of GPT-4 Vision and DALL-E 3 in coding. - Easy Code Generation: How to convert designs into clean HTML, Tailwind CSS, and JavaScript. - Practical Application: Real-world examples of the tool in action. 📰 FREE eBooks & News: https://sendfox.com/nerdingio 👉🏻 Product of the Day: https://www.producthunt.com/posts/ever-efficient-ai 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 00:23 Screenshot to Code 01:08 Dashboard 01:38 Configuration 02:12 Code streaming 02:32 Generation 03:04 Repository 04:17 Prompt 06:18 Image API 06:50 Logging LLM runs 07:05 Conclusion 🔗 Links https://github.com/abi/screenshot-to-code/tree/main https://picoapps.xyz/free-tools/screenshot-to-code ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## Summary: GPT-4 Vision Powered "Screenshot-to-Code" Tool **1. Executive Summary:** This video explores "screenshot-to-code," an open-source AI tool powered by GPT-4 Vision and DALL-E 3, that converts screenshots into functional HTML code using Tailwind CSS and JavaScript. The video demonstrates its capabilities, discusses its cost, and highlights opportunities for customization and expansion. **2. Main Topics Covered:** * **Introduction to Screenshot-to-Code:** Overview of the tool, its purpose, and open-source nature. * **Live Demonstration:** Using the tool to convert a screenshot of an OpenAI API usage graph into HTML. * **Code Generation and Preview:** Observing the live code streaming, generated HTML, and preview output. * **DALL-E Integration:** Understanding the use of DALL-E to generate placeholder images, and the option to disable this feature. * **Repository Overview:** Examining the underlying code, including Python backend and React/Tailwind frontend. * **Prompt Engineering:** Analysis of the AI prompt, highlighting its instructions for Tailwind development, screenshot replication, and code commenting. * **Image API:** How the tool uses the GPT-4 vision preview. * **Logging LLM runs:** Explanation of tracking model usage. * **Customization & Expansion:** Potential for modifying the prompt to improve accessibility (WCAG compliance) and tailor the output. **3. Key Takeaways:** * **GPT-4 Vision's Capabilities:** Demonstrates the power of GPT-4 Vision in transforming visual designs into functional code. * **Accessibility:** The tool acts as a no-code solution for quick web development. * **Customization:** The AI prompt is customizable, allowing users to fine-tune the output and add features like WCAG compliance. * **Low cost of generation:** The tool is very inexpensive, costing around 21 cents to generate an image. * **Real-time Code Streaming:** Live feedback on code generation and HTML output. * **Integration:** Can be used with other tools for greater customization. **4. Notable Quotes or Examples:** * "It's kind of like a no code solution but it's open source and what it's using is chat GPT Vision 4 to be able to take a screenshot and convert it into HTML specifically using Tailwind CSS." * "Generating the HTML from the screenshot cost about 21 cents." * The prompt is defined as a "Tailwind developer... building single page apps using Tailwind HTML and JavaScript" to focus the output. * Discussing prompt customization: "One thing that we could talk about here in this prompt is we could actually tell it to be more compliant with WCAG." **5. Target Audience:** * Web developers interested in AI-assisted coding. * No-code/low-code enthusiasts looking for innovative development tools. * Designers seeking a way to quickly prototype designs into functional HTML. * Anyone interested in the capabilities of GPT-4 Vision and DALL-E 3 for web development. **Key Highlights:** - **GPT-4 Vision Powers Screenshot-to-Code**: The tool uses GPT-4 Vision to convert a screenshot into HTML code using Tailwind CSS. It's open source and acts as a no-code solution. - **Live Code Generation & Preview**: The tool streams the generated code in real-time, allowing users to see the HTML being built and preview the output simultaneously. CDN scripts & comments are generated. - **DALL-E Integration for Image Generation**: The tool uses DALL-E to generate images for placeholders in the HTML, although this feature can be disabled. Useful for populating dynamic content. - **Customizable Prompt Engineering**: The prompt defines the AI as a Tailwind developer, focusing on exact screenshot replication, background colors, and code comments. The prompt can be modified for accessibility (WCAG) compliance. - **Low Cost of Generation**: Generating the HTML from the screenshot cost about 21 cents. This is highlighted as a reasonable cost for the outcome. --- ### Create Your Own GPT Turbo with OpenAI Assistant **Published:** 11/7/2023 **Duration:** 12 minutes **YouTube:** https://youtube.com/watch?v=OKcHeOqHf4c **Full LLM Text:** /video/cmis34fk3001h04kwxhameq99/llm.txt **Description:** Join us as we explore ChatGPT Turbo OpenAI's groundbreaking announcement from Dev Day, showcasing the revolutionary feature that allows you to create your very own ChatGPT AI Assistant using both the dashboard and an API. This in-depth tutorial covers everything you need to know to harness the power of the latest GPT4 Turbo model, making AI development more accessible than ever. 🔥 What's Inside: ✅ Look at the New Dashboard from OpenAI Dev Day 2023 ✅ Step-by-Step Guide on Setting Up Your AI Assistant ✅ Review the Logging ✅ Real-World Applications Demo 🎥 Chapters 00:00 Introduction 00:17 UI Updates 01:00 Discover 01:49 Assistant 02:51 Playground 06:06 Add vs Run 06:51 Logging 07:30 Modals 09:16 API 11:49 Review 🚀 Whether you're a seasoned developer or just starting out, this video will provide you with the tools and knowledge to dive into the world of AI-assisted technology. Get ready to unlock the potential of custom AI Assistants for your projects and workflows. 👍 Don't forget to like, subscribe, and hit the bell icon to stay updated on all our latest content. Comment below with your thoughts on this new feature or any questions you have – we love hearing from you! 📺 Watch next: https://www.youtube.com/watch?v=6p2T9iZUkn0 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 📰 FREE snippets & news: https://sendfox.com/nerdingio 👉🏻 Ranked #1 Product of the Day: https://www.producthunt.com/posts/ever-efficient-ai 🔗 Links https://chat.openai.com/gpts/discovery https://platform.openai.com/playground https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo https://platform.openai.com/docs/assistants/overview ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## Video Summary: Create Your Own GPT Turbo with OpenAI Assistant **1. Executive Summary:** This video provides a practical guide to leveraging OpenAI's latest Dev Day announcements, specifically the new Assistant API and enhanced features of GPT Turbo, to create custom AI assistants. It demonstrates building an assistant both through the OpenAI playground UI and by referencing the new API. **2. Main Topics Covered:** * **OpenAI Dev Day 2023 UI Updates:** Overview of the new OpenAI dashboard interface, focusing on access to GPT-4, browsing, analysis tools, and plugin integration. * **Creating Custom GPT Assistants via Playground:** A walkthrough of using the OpenAI Playground's Assistant feature as a workaround to the waitlist for the Custom GPTs feature, using file uploads for data retrieval. * **Assistant Configuration & Tooling:** Explanation of options for customizing assistants, including setting names, instructions, choosing models (GPT Turbo & GPT 3.5), and adding tools like code interpreter, file retrieval, and custom function calls. * **Importance of Logging:** Emphasizing the crucial role of the Playground's logs in debugging and understanding the assistant's execution flow, highlighting HTTP requests, tool calls, and code interpreter actions. * **GPT Turbo Model Details:** Discussion of GPT Turbo's capabilities, including its 128k context window, 4k output token limit, and integration with vision (image handling). * **Assistant API Overview:** Breakdown of the Assistant API, including creating assistants programmatically, managing threads for context, adding messages and files, and running the assistant to generate responses. **3. Key Takeaways:** * The OpenAI Playground offers a user-friendly interface for creating custom AI assistants by uploading custom data and using different models. * Logs within the Playground are essential for debugging and understanding how the assistant processes information and interacts with different tools. * GPT Turbo offers a large context window (128k tokens), but its output is limited to ~4k tokens. * The Assistant API allows for programmatic creation and management of AI assistants, mirroring the features available in the Playground UI. * Thread management is key in the Assistant API for managing conversation history and context. **4. Notable Quotes or Examples:** * **Creating a Web Accessibility Bot (WAG) Demo:** The video demonstrates building a chatbot using an ebook on web accessibility standards. This example showcases how to upload custom data and use the retrieval tool to answer user questions based on the document's content. * **"The thing that's the coolest for me is actually watching the log seeing the information that's coming back through so that we can find oh this is where it's actually trying to run the weather function that's pretty sweet but being able to look at this log and see the execution makes it really important and really helpful for what we're going to look at coming up which is the API call"**: This highlights the value of logging for understanding assistant behavior. * **GPT Turbo Token Context:** The video clarifies that although GPT Turbo has a 128k token input context window, it is limited to about 4k output tokens. **5. Target Audience:** * Software developers and AI enthusiasts looking to build custom AI assistants. * Individuals interested in understanding and utilizing OpenAI's latest Dev Day announcements, specifically the GPT Turbo model and Assistant API. * Those seeking a practical guide on creating AI assistants using both a UI and an API approach. * Anyone exploring the possibilities of AI-assisted technology for various projects and workflows. **Key Highlights:** - **Custom GPTs via Playground Assistant**: OpenAI's playground allows users to create custom GPT assistants using their own data (e.g., PDFs) as a workaround while the official custom GPT feature is in beta and closed off for some users. - **Importance of Logs for Debugging**: The playground provides detailed logs of the assistant's operation, including HTTP requests, tool calls, and code interpreter actions, which are crucial for debugging and understanding the assistant's logic. - **GPT Turbo Token Window**: GPT Turbo has a large 128k token context window, but it's important to note the maximum return is limited to around 4k output tokens, which impacts the length of the assistant's responses. - **Assistant API Details**: The Assistant API mirrors the UI features, allowing programmatic creation of assistants with names, instructions, tools and model selection. Requires updated SDKs with beta object. - **Thread Management for Context**: The Assistant API utilizes threads to manage context and conversation history, adding messages and running them to generate responses. Files can be optionally added to threads for context. --- ### NLP Made Easy: ChatGPT Tokenizer The Building Blocks Of Natural Language Processing **Published:** 11/6/2023 **Duration:** 12 minutes **YouTube:** https://youtube.com/watch?v=6p2T9iZUkn0 **Full LLM Text:** /video/cmis34qtj001i04kwlgj3biq3/llm.txt **Description:** Dive into the fascinating world of NLP with our latest video on ChatGPT tokenizer and how they work in Javascript! 🤖💬 Understand how machines break down and interpret human language with ease. We'll unravel tokenization the mystery behind word tokens, subword tokens, and byte pair encoding (BPE) tokens used in advanced models like GPT-3, and GPT-4. 🧠🔍 🔑 Key Highlights: ✅ What is Tokenization in AI? ✅ Different Types of Tokens in Natural Language Processing ✅ Byte Pair Encoding (BPE) Demystified ✅ Real-World Applications of Tokenization Whether you're a tech enthusiast, a student stepping into machine learning, or simply curious about how AI understands language, this video is your gateway to the core concepts of tokenization. 🚀 Don't forget to like, comment, and subscribe for more insightful content on artificial intelligence and machine learning. 👍🔔 📰 FREE snippets & news: https://sendfox.com/nerdingio 👉🏻 Ranked #1 Product of the Day: https://www.producthunt.com/posts/ever-efficient-ai 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 00:17 OpenAI Limits 01:18 Playground 03:26 Sub Words 04:31 GPT Tokenizer 06:06 Advanced Tools 06:51 NextJS 10:41 Typescript 11:54 Conclusion 🔗 Links https://gpt-tokenizer.dev/ ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ #Tokenization #NLP #MachineLearning #GPT3 #AIExplained #TechEducation #LearnAI #ArtificialIntelligence #DataScience #Coding #Programming #token **Summary:** Here's a summary document designed to help someone quickly understand the video content. **NLP Made Easy: ChatGPT Tokenizer The Building Blocks Of Natural Language Processing - Summary Document** **1. Executive Summary:** This video provides a practical introduction to tokenization in NLP, explaining how machines break down human language into tokens for processing. It covers different types of tokens, explores the GPT tokenizer in Javascript, and demonstrates its usage with code examples within Next.js and React environments. **2. Main Topics Covered:** * **Introduction to Tokenization:** What tokenization is in the context of AI and NLP. * **Token Limitations in AI Models:** Discussion of token limits in various AI models (e.g., GPT-3, GPT-4) and their impact on text processing. * **Types of Tokens:** Exploration of word tokens, subword tokens, and Byte Pair Encoding (BPE) tokens, illustrating how they differ. * **OpenAI Visualizer:** Using the OpenAI playground to visually understand how text is broken down into tokens. * **GPT Tokenizer JavaScript Package:** Overview of the `gpt-tokenizer` package in JavaScript, its functionalities (encoding, decoding, token limit checking), and its advantages compared to other tokenizers (like TikToken). * **Practical Implementation:** Demonstrations of using the `gpt-tokenizer` package in JavaScript within Next.js API routes and directly in React frontend components. The focus is on encoding, decoding, and managing token limits. **3. Key Takeaways:** * Tokenization is crucial for AI models to understand and process human language. * Token limitations affect the amount of text an AI model can handle at once, and can be handled with chunking functions in Javascript * Different models use different tokenization methods, impacting performance and cost. * The `gpt-tokenizer` JavaScript package offers a convenient way to work with tokens in JavaScript applications. * You can implement tokenization on the backend (Next.js API) or directly on the frontend (React), allowing for flexible application design. * Byte Pair Encoding (BPE) creates tokens from subwords to better handle rare words and improve language understanding. **4. Notable Quotes or Examples:** * "Tokens are just pieces of text that are broken down you can think of them as small pieces." * "About four characters of text is roughly one token so 100 tokens could be about 75 words" - Rule of thumb for token estimation. * HTML/Code generates a ton of tokens due to its structured nature. * Explanation of Subword tokenization and its advantages (using examples from Hugging Face). * Code snippets showcasing the usage of `gpt-tokenizer` for encoding, decoding, and checking token limits. **5. Target Audience:** * Tech enthusiasts interested in NLP. * Students learning about machine learning and AI. * Developers building applications using large language models (LLMs). * Anyone curious about how AI processes and understands human language. **Key Highlights:** - **Tokenizers and Limitations in AI Models**: AI models have token limitations that impact text processing. Tokenization isn't simply character or space-based; algorithms break text into 'tokens,' affecting input capacity and potentially influencing the model used. - **Understanding Tokenization with OpenAI Visualizer**: OpenAI provides a visualizer tool to understand how text is broken down into tokens, revealing that punctuation and spaces can be significant tokens. HTML code generates many tokens due to its structured nature. - **GPT Tokenizer JavaScript Package Overview**: The GPT tokenizer package in JavaScript offers encoding and decoding functionalities, along with features like token limit checking. It is similar to TikToken but with additional features, including async generators and a playground for visualization. - **Using GPT Tokenizer in JavaScript/Next.js**: The package allows encoding/decoding text, checking token limits, and processing chat logs. Chunking and generator functions help manage large texts. This can be implemented server-side in API routes or client-side for real-time analysis. - **Frontend Tokenization Example in React**: The video demonstrates using the GPT tokenizer directly within a React application in a code sandbox. This enables front-end encoding and decoding of text, bypassing the need for backend processing in certain situations. --- ### How To Get Started With ChatGPT Vision: Tips For Beginners **Published:** 10/27/2023 **Duration:** 5 minutes **YouTube:** https://youtube.com/watch?v=plXOgLdCRkM **Full LLM Text:** /video/cmis356ie001j04kwhnh8vrgc/llm.txt **Description:** ChatGPT Vision is an exciting new AI assistant from OpenAI that can be incredibly helpful for a wide range of tasks. But if you're new to conversational AI, it can be a bit daunting to figure out how to make the most of ChatGPT Vision. In this video, I'll provide some tips for beginners to start using ChatGPT Vision effectively. I'll go over the basics of how it works, ideas for use cases, how to structure prompts, best practices for iterating and providing feedback, limitations to be aware of, and how to gradually integrate it into your workflows. Whether you want to leverage ChatGPT Vision to explain concepts, summarize documents, generate content, or automate tasks, this video will give you a starter guide to getting the most value. I'll provide examples of simple prompts to begin with and discuss how to build up complexity over time. The goal is to give you the core knowledge you need to feel comfortable getting hands-on with ChatGPT Vision as a beginner. Soon you'll be having productive conversations with this remarkable AI assistant! 📰 FREE snippets & news: https://sendfox.com/nerdingio 👉🏻 Ranked #1 Product of the Day: https://www.producthunt.com/posts/ever-efficient-ai 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 00:17 Usage 00:51 Attach Images 01:16 Hot Commands 02:08 Prompt 04:22 Mobile 05:10 Conclusion 🔗 Links https://chat.openai.com/ https://openai.com/blog/chatgpt-can-now-see-hear-and-speak ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## ChatGPT Vision for Beginners: A Quick Start Guide (Video Summary) **1. Executive Summary:** This video provides a beginner's guide to using ChatGPT Vision effectively. It covers the basics of how to access and utilize Vision, offers practical tips for prompt engineering, and demonstrates how to leverage its capabilities across both desktop and mobile platforms for image analysis. **2. Main Topics Covered:** * **Accessing and Using ChatGPT Vision:** Explanation of how Vision is integrated into the default GPT-4 model and how to upload images. * **Desktop Workflow:** Using screenshot to clipboard functionality (Command+Control+Shift+4 on Mac) for quick image input. * **Prompt Engineering:** Importance of defining a role for ChatGPT (e.g., "act as an image specialist") to improve analysis. * **Mobile Vision Input:** Utilizing the image icon to upload photos, take new ones, or copy/paste images from browsers. * **Example Use Case:** Identifying comic book characters in an image and providing their origin stories. * **Limitations:** Acknowledges that Vision may miss certain details in complex images. **3. Key Takeaways:** * ChatGPT Vision is readily accessible within the default GPT-4 model without selecting specific modes. * Screenshotting and pasting images into ChatGPT is a fast and efficient workflow. * Specifying a role for ChatGPT before prompting enhances the accuracy and depth of its analysis. * Mobile integration allows for easy image input via photo uploads, new photos, or copying/pasting. * Iterate on your prompts and provide feedback to refine the AI's responses and improve accuracy. **4. Notable Quotes or Examples:** * "…as long as you have default, you can go ahead and get started with vision." * Example of prompt: "Act as an image specialist specifically on comics and tell me the comic book characters fix that on the wall and their origin." * The video demonstrates the ability of ChatGPT Vision to identify characters (Harley Quinn, Batman, Joker) painted on a wall and provide their origin stories, even recognizing the characters despite the artwork being created by the presenter. **5. Target Audience:** * Beginners new to ChatGPT Vision. * Individuals looking for practical tips on using Vision effectively. * Users interested in leveraging AI for image analysis and content generation. **Key Highlights:** - **Accessing Vision in ChatGPT**: Vision is automatically available within the default GPT-4 model; no explicit selection of a specific mode (like Bing or DALL-E) is necessary. - **Screenshot to Clipboard Workflow**: On Mac, use Command+Control+Shift+4 to take a screenshot and automatically copy it to the clipboard, enabling instant pasting into ChatGPT. - **Role-Playing for Better Analysis**: Specifying a role (e.g., "act as an image specialist") before prompting enhances ChatGPT's analysis and accuracy in identifying image content. - **Mobile Vision Input Methods**: On mobile, use the image icon to upload photos, take new ones, or copy images from browsers to paste directly into ChatGPT. --- ### 10 Ways Chat GPT Vision Can Help Developers **Published:** 10/18/2023 **Duration:** 18 minutes **YouTube:** https://youtube.com/watch?v=glZ_LZpdp_8 **Full LLM Text:** /video/cmis35isz001k04kwxvtnistm/llm.txt **Description:** Unlock the power of Chat GPT-4 Vision for software developers! Dive deep into 10 ways ChatGPT Vision can revolutionize how we approach design, code, and user experience. From optimizing SEO via visual webpage analysis to transforming static mockups into interactive prototypes, GPT-4 is redefining the boundaries of software development. 🚀 Have you ever considered how a simple screenshot could help improve your website's accessibility? Or how a visual scan of a YouTube code tutorial could lead to more precise understanding and optimization tips? Welcome to the next-gen tech world, where GPT-4 Vision leads. 📰 FREE snippets & news: https://sendfox.com/nerdingio 👉🏻 Ranked #1 Product of the Day: https://www.producthunt.com/posts/ever-efficient-ai 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 0:00 Introduction 0:13 Logo Feedback 1:29 SEO 2:57 Web Accessibility 5:58 Light and Dark Mode 8:19 YouTube Video Code Explainer 9:31 Debugger 12:29 Interactive Enhancement 14:31 Game Developer 16:36 AR App Guidance 18:29 Conclusion 🔗 Links https://chat.openai.com/ https://openai.com/blog/chatgpt-can-now-see-hear-and-speak ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## Chat GPT Vision for Developers: Video Summary **1. Executive Summary:** This video explores 10 practical applications of Chat GPT-4 Vision for software developers, demonstrating its potential to revolutionize workflows across design, debugging, UI/UX enhancement, and even AR/game development. By analyzing images, Chat GPT Vision can provide valuable insights, suggest improvements, and generate code, streamlining development processes. **2. Main Topics Covered:** * **Logo Feedback:** Using Vision to analyze logo design principles. * **SEO & Accessibility Audits:** Extracting SEO insights and identifying accessibility violations from website screenshots. * **Light and Dark Mode CSS:** Generating CSS code for light and dark mode themes from website images. * **YouTube Video Code Explainer:** Analyzing code snippets from YouTube tutorials to understand their functionality. * **Code Debugger:** Identifying errors and suggesting best practices in code snippets from images. * **Interactive Enhancement:** Suggesting UI/UX improvements for static website mockups. * **AR App Guidance:** Generating ideas for augmented reality application based on real-world images. * **Game Developer:** Provides insights and suggestions to improve video game design from an image of the game. **3. Key Takeaways:** * Chat GPT Vision allows developers to analyze visual data (images) and extract actionable insights relevant to their work. * The tool can perform tasks ranging from design feedback and SEO auditing to code debugging and UI/UX enhancement, all from images. * Chat GPT Vision can be leveraged for creative brainstorming, generating ideas for AR applications and game development. * Using Vision can potentially speed up development cycles by providing quick, visually-driven feedback and suggestions. * Although helpful, the tool isn't perfect. While the debugging feature is amazing, it can be incorrect at times. **4. Notable Quotes or Examples:** * **Logo Feedback:** "The imagery of the glasses goes with the nerd stereotype... Simplicity... clean and able to be used in in different ways." * **SEO & Accessibility Audits:** "Talking about the file names right like making sure that they're descriptive instead of just image one." & "It's talking about the contrast right out of the gate. This could be super helpful." * **Code Debugger:** "This one was kind of interesting that like on line 22 you are using prompt format and it's not clear where prompt is coming from so this is kind of a mistake but kind of an interesting one at the same time because prompt is actually it commented out and so that's why prompt template isn't getting called." & "It recognized the best practice that even though I'm doing a fetch call it doesn't have a TR catch block..." * **Interactive Enhancement:** "...talking about keyboard accessibility this is the third time that we've seen it bring up wayag..." * **AR Augmentation Ideas:** "... holographic street signs above the road pointing out directions super imposed floating ads or billb Billboards in the sky...virtual pets...a virtual magazine that you could flip through." * **Game Designer/Developer (Super Mario Bros. image):** The platformers should automatically drop when mario stands on them, instead of having him manually jump off. Also, checkpoints should be added to help mitigate gamer fatigue. **5. Target Audience:** This video is primarily targeted at software developers, UI/UX designers, game developers, AR/VR developers, web developers, and anyone interested in leveraging AI and computer vision to improve their workflows. **Key Highlights:** - **Logo Feedback & Design Analysis**: Chat GPT Vision analyzes a logo (Nerding IO) for color theory, symmetry, typography, and design principles. Demonstrates potential for design feedback and brand analysis from images. - **SEO & Accessibility Audits from Images**: Vision identifies SEO issues (keywords, alt text) and accessibility violations (contrast, keyboard navigation) from a website screenshot. Offers a quick, visual way to audit websites. - **Code Debugging & Best Practices**: The tool can identify errors in code snippets from an image, even catching undefined variables, best practice violations (missing try-catch), and pointing out unused imports, showcasing its debugging capabilities. - **UI/UX Enhancement Suggestions**: Given an image of a website, Chat GPT Vision suggests improvements to UI/UX, including interactivity, feedback mechanisms, and responsive design, acting like a visual consultant. - **AR Augmentation Ideas from Images**: The tool can analyze real-world images (city, park, home) and suggest virtual augmentations for AR applications. It envisions holographic signs, virtual pets, and interactive elements, demonstrating creative possibilities. --- ### How To Use Next Js 13 With Langchain And Langsmith **Published:** 10/14/2023 **Duration:** 9 minutes **YouTube:** https://youtube.com/watch?v=_B-3XwPBzTc **Full LLM Text:** /video/cmis35yho001l04kwwbaa0ei8/llm.txt **Description:** In this video, we will dive into Langchain and Langsmith in a Next js 13 application. We cover everything from understanding the security concerns of using OpenAI in the browser to the backend setup! Learn about session storage, lane chain in the front end, traditional API protection, and more. Get insights into setting up environment variables, creating prompt templates, and connecting with Langsmith. We also touch upon related topics like the Python example and upcoming videos on the web langchain. Take advantage of these valuable insights! Like, subscribe, and join our community of nerds. #OpenAI #Nextjs #API #LangChain #Langsmith #WebDevelopment 📰 FREE snippets & news: https://sendfox.com/nerdingio 👉🏻 Ranked #1 Product of the Day: https://www.producthunt.com/posts/ever-efficient-ai 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 0:00 Introduction 1:09 Getting Started 1:37 LangChain 2:31 NextJS 3:25 Prompt Template 4:03 Testing in the Browser 4:55 API Fetch 5:48 API Route 6:13 Chat Model 7:33 Result 8:04 Langsmith 9:22 Conclusion 🔗 Links https://nextjs.org/docs/getting-started/installation https://js.langchain.com/docs/get_started/installation https://js.langchain.com/docs/get_started/quickstart https://smith.langchain.com/ ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## Video Summary: How To Use Next Js 13 With Langchain And Langsmith **1. Executive Summary:** This video provides a practical guide to integrating Langchain and Langsmith within a Next.js 13 application. It covers both front-end and back-end implementations, emphasizing security best practices and demonstrating how to monitor Langchain processes using Langsmith for tracing and debugging. **2. Main Topics Covered:** * **Introduction to Next.js and Langchain:** Briefly explains the increasing usage of JavaScript in AI models and applications. * **Setting up a Next.js Application:** Includes creating a new Next.js project using `create-next-app` with specific configurations (no TypeScript, using the App Router). * **Frontend Langchain Implementation:** Demonstrates using Langchain directly in a Next.js component including Dynamic Prompt Generation. * **Backend API Route for Security:** Discusses creating a secure Next.js API route to handle Langchain logic and protect the OpenAI API key. * **Langsmith Integration:** Showcases how to integrate Langsmith for monitoring, tracing, and debugging Langchain applications, visualizing run sequences, and analyzing latency. * **Working with Prompt Templates:** Creating and using prompt templates within both the front-end and back-end for dynamic text generation. **3. Key Takeaways:** * Langchain can be implemented both on the front-end and back-end within a Next.js application. * Exposing OpenAI API keys directly in the front-end is a security risk. A secure back-end API route is the recommended approach. * Langsmith provides valuable tools for monitoring, tracing, and debugging Langchain applications. * Prompt templates allow for dynamic and reusable text generation. * The presenter mentioned JavaScript's increasing role in AI applications, like flow wise AI, embed chain, and webang chain. **4. Notable Quotes or Examples:** * **Prompt Template Example (Front-end):**`"What is a good name for a company that makes {topic}"`. * **API Route creation:** `app/api/llm/route.js` demonstrates the Next.js file structure for API endpoints. * **Model setup:** The model can default to the openAI API key and can be added in your environment variables. * **JavaScript Joke:** "Why do JavaScript developers prefer wearing glasses? Because they don't miss any exceptions." - Example of generated content. **5. Target Audience:** * Web developers interested in integrating AI functionalities into their Next.js applications. * Developers looking for a practical guide on using Langchain with Next.js. * Individuals interested in learning about secure API key management when working with OpenAI. * Developers who want to utilize Langsmith for monitoring and debugging their Langchain applications. **Key Highlights:** - **Next.js & Langchain Integration**: Demonstrates using Next.js with Langchain, showcasing JavaScript's increasing role in AI applications, including front-end implementations and server-side API routes. - **Frontend Langchain Implementation**: Explores using Langchain directly in the Next.js frontend, highlighting prompt templates and dynamic prompt generation, but cautioning about security implications of exposing API keys. - **Backend API Route for Security**: Presents a more secure approach by creating a Next.js API route to handle Langchain logic, protecting the OpenAI API key and demonstrating data flow from frontend to backend. - **Langsmith Monitoring Integration**: Shows how to integrate Langsmith for tracing, monitoring, and debugging Langchain applications within Next.js, including visualizing run sequences and latency. --- ### How Transformer.js Can Help You Create Smarter AI In Your Browser #webml #ai **Published:** 9/29/2023 **Duration:** 13 minutes **YouTube:** https://youtube.com/watch?v=MNJHu9zjpqg **Full LLM Text:** /video/cmis36aqz001m04kwm0yqgxr8/llm.txt **Description:** In this video, I'll introduce you to WebML and Transformer JS, which is all about using AI in the browser. We'll explore a library called transformers.js that allows us to use the hugging faces transformers Python library in JavaScript. I'll show you how to perform tasks like natural language processing, computer vision, and even multimodal tasks directly in your browser without needing a server. We'll also dive into examples of using WebML in vanilla.js, React, and Next. So let's get started! 📰 FREE eBooks & News: https://sendfox.com/nerdingio 👉🏻 Course: https://www.udemy.com/course/ai-in-the-browser-with-js-chrome-extensions-huggingface/?couponCode=55A07DD0215025B3E590 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction to WebML 01:02 Chrome Extension Example 04:18 Behind the Scenes of WebML 06:44 Defining Pre-trained Models 07:20 Convert Pre-trained Models 08:22 Demos 09:00 React Translator 10:54 Blind Chat 11:41 Code Gen 13:00 Review 🔗 Links https://huggingface.co/docs/transformers.js/index https://github.com/nerding-io/questionable/tree/main https://huggingface.co/collections/Xenova/transformersjs-demos-64f9c4f49c099d93dbc611df https://huggingface.co/spaces/Xenova/react-translator https://huggingface.co/spaces/mithril-security/blind_chat ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** Here's a structured summary of the video, designed for quick understanding: **Video Title:** How Transformer.js Can Help You Create Smarter AI In Your Browser #webml #ai **1. Executive Summary:** This video introduces Transformer.js, a library enabling browser-based AI processing using Hugging Face's Transformers in JavaScript. It demonstrates how to perform tasks like NLP, computer vision, and code generation directly in the browser without relying on a backend server, highlighting the potential for faster, offline-capable AI applications. **2. Main Topics Covered:** * **Introduction to WebML and Transformers.js:** Defining WebML as AI in the browser and introducing Transformers.js as a way to use Hugging Face's Transformers in JavaScript. * **Benefits of Browser-Based AI:** Eliminating the need for a server for AI processing, enabling faster and potentially offline AI applications. * **ONNX Runtime and Model Conversion:** Using ONNX runtime to load and run models in the browser and converting PyTorch, TensorFlow, or JAX models into ONNX format. * **Implementation Examples:** Demonstrations of WebML using vanilla JavaScript, React, and Next.js, including a Chrome extension and live translation. * **Caching and Model Management:** Caching models in the browser for faster subsequent use and defining pre-trained models with local paths. * **Code Generation Demonstration**: Showcasing a practical example of using Transformer.js for code completion directly in the browser. **3. Key Takeaways:** * **Browser-Based AI is Possible:** Transformers.js brings the power of Hugging Face's Transformers library directly to the browser. * **No Server Required:** Process NLP, computer vision, audio and multimodal tasks without a backend server. * **Performance and Efficiency:** ONNX runtime enables efficient model execution, and caching significantly improves subsequent use. * **Customization and Flexibility:** Models can be loaded from Hugging Face Hub or local files, offering flexibility in model selection. * **Practical Applications:** The video showcases real-world examples such as Chrome extensions, live translation, and code completion. **4. Notable Quotes or Examples:** * "What's meant by that is you can actually use different machine learning practices and run them in the web like in the browser without any backend." - Defining WebML. * Example of Sentiment Analysis in real time: Demonstrating positivity/sentiment analysis on every keystroke within an input field. * "Even though we're opening and closing this Chrome extension, that Chrome extension has only had to load the model a single time." - Highlighting the benefits of caching in Transformer.js. * "Now that this is uh loaded the model and it's running it through ONNX, it's pretty fast like faster than actually hitting an API and returning it." - Comparing performance with traditional server-side AI. * Code Generation Example: A demonstration of getting code completion within the browser, utilizing a specific code generation model. **5. Target Audience:** * Web developers interested in integrating AI capabilities into their web applications. * JavaScript developers looking for a serverless AI solution. * AI/ML engineers exploring browser-based AI deployment options. * Anyone interested in using Hugging Face's Transformers library in a JavaScript environment. **Key Highlights:** - **Browser-Based AI with Transformers.js**: Run ML models directly in the browser using Transformers.js, enabling NLP, computer vision, audio processing, and more without a backend server. - **No-Server Transformer Power**: Transformers.js allows the use of Hugging Face's Transformers library in JavaScript, eliminating the need for a server and providing browser-based AI processing. - **ONNX Runtime Integration for Performance**: Leverage the ONNX runtime within Transformers.js to load and run models efficiently in the browser, with tools to convert PyTorch, TensorFlow, and JAX models. - **Caching Models for Speed**: Transformers.js caches loaded models in the browser, resulting in significantly faster subsequent use, bypassing the need for repeated loading during interactions. - **Customize Models and Tasks**: You can define pre-trained models and configure local paths for them. Transformers.js can also access models on Hugging Face, allowing flexibility in model selection and usage. --- ### 📊 How to Protect Your Data from AI Crawlers **Published:** 9/22/2023 **Duration:** 9 minutes **YouTube:** https://youtube.com/watch?v=TsUYGs_PCYY **Full LLM Text:** /video/cmis36lww001n04kw0i54yte6/llm.txt **Description:** In this video, I discuss the importance of protecting your data from AI crawlers and provide insights on how to do it effectively. I explain the concept of AI crawlers and their role in indexing web pages. I also introduce the robots.txt file and its significance in allowing or disallowing access to your website. Additionally, I explore the proposed AI.txt standard and its potential impact on data protection. Watch this video to learn practical strategies for safeguarding your data from AI crawlers. 📰 FREE eBooks & News: https://sendfox.com/nerdingio 👉🏻 Ranked #1 Product of the Day: https://www.producthunt.com/posts/ever-efficient-ai 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 02:09 Understanding AI Crawlers 02:41 The Role of Robots.txt 04:44 Protecting Against OpenAI User 06:43 The Proposed AI.txt Standard 🔗 Links https://netfuture.ch/2023/07/blocking-ai-crawlers-robots-txt-chatgpt/ https://platform.openai.com/docs/gptbot https://platform.openai.com/docs/plugins/bot https://site.spawning.ai/spawning-ai-txt ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## Video Summary: How to Protect Your Data from AI Crawlers **1. Executive Summary:** This video explains the importance of protecting your data from AI crawlers, similar to how SEO manages indexing for search engines. It covers using `robots.txt` to control crawler access and introduces the proposed `AI.txt` standard for more granular content-type control, enabling users to prevent sensitive data from being indexed by AI models. **2. Main Topics Covered:** * **Understanding AI Crawlers:** Explanation of AI crawlers and their role in indexing web content for training AI models. Specific crawlers like GPTBot and OpenAI User are mentioned. * **The Role of `robots.txt`:** Detailed explanation of how `robots.txt` functions to allow or disallow access to specific parts of a website, using user-agent directives and disallow rules. * **Protecting Against OpenAI User (Plugins):** Focus on differentiating between the regular GPTBot and the OpenAI User agent used specifically for plugins, and how to block them both. * **Dynamic `robots.txt` Generation:** Demonstration using Node.js and Express to create a dynamic `robots.txt` file, allowing for conditional rules based on real-time data. * **The Proposed `AI.txt` Standard:** Introduction of the `AI.txt` proposal as a future standard for specifying allowed content types for AI crawlers, offering more precise control over data indexing. * **Protecting Specific File Types:** The importance of preventing specific file types (e.g., `.txt`, `.png`, `.pdf`) from being indexed using `robots.txt` or the proposed `AI.txt`. **3. Key Takeaways:** * Use `robots.txt` to manage AI crawler access by specifying user agents (e.g., `GPTBot`, `OpenAI-User`) and disallowing specific directories or files. * Dynamic `robots.txt` generation allows for more flexible control based on real-time data or application logic. * The proposed `AI.txt` standard offers more granular control over which content types AI crawlers can access. * Protect sensitive data by disallowing specific file types that might contain valuable information from being indexed by AI models. * Keep an eye on the adoption of `AI.txt` as a potential future standard. **4. Notable Quotes or Examples:** * "You can think of it as how SEO Works basically you have a file on your server which is called a robot txt and that allows things like the googlebot for search to actually go out and index your pages." * Example: `User-agent: GPTBot`, `Disallow: /private/` (Blocking GPTBot from the /private/ directory). * Example: `Disallow: *.txt` or `Disallow: *.png` (Blocking all text and image files, which shows use of wild cards to specify file types.) **5. Target Audience:** * Website owners and developers concerned about data privacy and the use of their content by AI models. * Individuals interested in learning about controlling AI crawler access to their websites. * Professionals responsible for managing SEO and data protection for organizations. **Key Highlights:** - **Robots.txt for AI Crawlers**: The video explains how to use robots.txt to control AI crawler access, similar to SEO, by specifying user agents like 'GPTBot' and disallowing specific directories or files. - **Dynamic Robots.txt Generation**: Demonstrates creating a dynamic robots.txt file using Node.js and Express, enabling conditional disallowing based on real-time data or application logic. - **AI.txt Proposed Standard**: Introduces the AI.txt proposal as a future standard (not yet implemented) for specifying allowed content types for AI crawlers, offering more granular control. - **Protecting Specific File Types**: Highlights the importance of disallowing specific file types (e.g., .txt, .png, .pdf) in robots.txt or AI.txt to prevent AI models from indexing sensitive data. --- ### Use ANY language in LangSmith with REST **Published:** 9/6/2023 **Duration:** 21 minutes **YouTube:** https://youtube.com/watch?v=7BL0GEdMmgY **Full LLM Text:** /video/cmis36yfd001o04kwv5mpm6z4/llm.txt **Description:** Hey everyone, welcome to Nerding IO. In this video we will be diving into Lang Chain and exploring the Lang Smith cookbook. We'll be looking at different examples, including Next.js and a REST-based example. I'll guide you through the process of setting up a chain, running a REST API example, and exploring different runs. We'll also cover logging LLM runs and testing for errors. Join me on this exciting journey into Lang Chain! 📰 FREE eBooks & News: https://sendfox.com/nerdingio 👉🏻 Ranked #1 Product of the Day: https://www.producthunt.com/posts/ever-efficient-ai 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 03:21 Setting up LangSmith and running a REST API example 08:50 Logging LLM runs 12:30 Exploring different chains 14:24 Understanding the run logger class 16:15 Running a Fibonacci code example 17:45 Reviewing the logs and traces 19:54 Testing for errors 🔗 Links https://github.com/langchain-ai/langsmith-cookbook/tree/main https://github.com/langchain-ai/langsmith-cookbook/blob/main/tracing-examples/rest/rest.ipynb https://smith.langchain.com/ ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## LangSmith REST API: Unleashing Language Model Monitoring Across All Languages **1. Executive Summary:** This video from Nerding IO explores the LangSmith cookbook, focusing on the recently added REST API. It showcases how to leverage LangSmith's tracing, logging, and error-tracking capabilities from any programming language through REST calls, opening up broader use cases beyond Python and JavaScript. **2. Main Topics Covered:** * **Introduction to LangSmith REST API:** Overview of the new API and its benefits for logging runs and data from diverse environments. * **Setting up LangSmith and REST API Example:** Walkthrough of the necessary prerequisites (API Key, Project Name, App ID) and launching the REST API notebook. * **Logging LLM Runs via API:** Demonstrating logging inputs, outputs, errors, metadata, tags, and events using POST and PATCH requests to the LangSmith API, including: * Simple data logging * Adding tags and metadata (runtime, platform) * Logging LLM specific data (prompts, model parameters, function calls) * **Exploring Nested Runs and Execution Tracking:** Illustrating the creation of nested runs using parent run IDs and execution order, with a Fibonacci sequence example to track intricate workflows. * **Error Logging and Tracing:** Demonstrating how to log errors, capture error messages, and provide a traceback of the execution flow for effective debugging. **3. Key Takeaways:** * **Language Agnostic Logging:** The LangSmith REST API allows logging runs and data from any language or environment, expanding its utility beyond Python and JavaScript. * **Comprehensive Data Logging:** The API supports detailed data logging, including inputs, outputs, errors, metadata (runtime, platform), tags, and events (retry, new token). * **Detailed LLM Tracking:** You can log LLM runs and capture all relevant data, including prompts, parameters, and function call responses, making debugging much easier. * **Nested Runs for Complex Workflows:** Parent run IDs and execution order enable detailed tracing of complex workflows with nested runs. * **Robust Error Handling:** Error logging includes the error message and a traceback, facilitating efficient debugging. **4. Notable Quotes or Examples:** * *"Although uh you know llms are specifically written in Python mostly and and also JavaScript there's other use cases that you can use to actually log the runs and take advantage of linksmith so if you're using it in different languages or different types of like traces in open telemetry so this is really awesome."* (Illustrates the motivation for the REST API). * **Fibonacci Sequence Example:** Used to demonstrate nested runs and logging success/failure paths, showcasing the API's tracing and error handling capabilities. * **Logging LLM Calls:** Example showcases logging a function call and the subsequent parameters. **5. Target Audience:** * AI/ML engineers and developers using LangChain. * Developers who need to monitor LLM applications built using languages other than Python or JavaScript. * Teams looking to implement robust tracing and debugging strategies for their AI workflows. * Developers interested in using a centralized tool to trace/debug AI code written in many different programming languages. **Key Highlights:** - **LangSmith REST API Unveiled**: LangSmith now offers a REST API, enabling logging runs and leveraging its features from any language or environment, not just Python or JavaScript. This opens doors for wider adoption. - **Flexible Data Logging with API**: The REST API allows for comprehensive data logging, including inputs, outputs, errors, metadata (runtime, platform), tags, and events (retry, new token) providing rich context for debugging and analysis. - **Logging LLM Runs via API**: The API supports logging LLM runs, capturing details like prompts, model parameters, and function call responses (names and arguments). It mimics OpenAI's API structure for ease of integration. - **Nested Runs & Execution Tracking**: Demonstrates creating nested runs using a parent run ID and execution order, allowing for detailed tracing of complex workflows, as shown with the Fibonacci sequence example. - **Error Logging and Tracing**: Illustrates logging errors, capturing the error message, and providing a traceback of the execution flow leading to the error, which aids in debugging and issue resolution. --- ### How to Re-evaluate Dynamic Datasets in LangSmith **Published:** 9/1/2023 **Duration:** 12 minutes **YouTube:** https://youtube.com/watch?v=WyjjZy4pdjY **Full LLM Text:** /video/cmis379sc001p04kwids7c3fm/llm.txt **Description:** In this video, I will guide you through the Lang Smith dashboard and monitoring features. We will be looking at the Langsmith cookbook and a testing example that involves dynamic data. I will walk you through the steps to clone or download the necessary files and set up the prerequisites. Then, we will start building our data set and define our Q&A system. We will use the OpenAI GPT API and execute our chain to get answers to specific questions. Finally, we will explore the chain of events and evaluate the correctness of our results. No action is required from you, but feel free to follow along and ask any questions you may have. 📰 FREE eBooks & News: https://sendfox.com/nerdingio 👉🏻 Ranked #1 Product of the Day: https://www.producthunt.com/posts/ever-efficient-ai 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction 01:00 Setting Up Prerequisites 02:48 Building the Data Set 03:13 Defining the Q&A System 03:46 Executing the Chain 04:18 Analyzing the Results 07:20 Run Evaluation 09:22 Re-evaluate Dataset 🔗 Links https://github.com/langchain-ai/langsmith-cookbook/tree/main https://github.com/langchain-ai/langsmith-cookbook/blob/main/testing-examples/dynamic-data/testing_dynamic_data.ipynb https://smith.langchain.com/ ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## LangSmith Dynamic Data Re-evaluation: Video Summary **1. Executive Summary:** This video provides a step-by-step walkthrough of using LangSmith to test and re-evaluate dynamic datasets, focusing on a practical example with the Titanic dataset. It demonstrates how to trace agent execution, evaluate results using custom criteria, and simulate dataset updates for continuous monitoring and testing. **2. Main Topics Covered:** * **Dynamic Data Testing with LangSmith:** Utilizing LangSmith to test a Q&A system based on the Titanic dataset. * **Setting Up Prerequisites:** Cloning the LangSmith cookbook, installing required packages (pandas, OpenAI), and configuring API keys. * **Building the Data Set:** Loading questions and corresponding Python code snippets (DataFrame functions) for answering them. * **Defining the Q&A System:** Configuring the OpenAI GPT-4 API with a temperature of zero for accuracy and integrating a Python REPL tool. * **Executing the Chain:** Running the chain with a specific question and observing the generated answer. * **Analyzing Results and Tracing Agent Execution:** Examining the LangSmith dashboard to trace the execution flow, including token usage, latency, observations, and interactions with the OpenAI model and Python REPL. * **Running Evaluation:** Utilizing custom evaluation criteria to assess the correctness of the agent's predictions using GPT-4. * **Re-evaluating Data Sets:** Simulating new data additions to the Titanic dataset and re-running the evaluation to assess performance changes. **3. Key Takeaways:** * LangSmith offers powerful tools for testing and evaluating AI models working with dynamic data. * Tracing agent execution provides detailed insights into the decision-making process, helping to identify areas for improvement. * Custom evaluation criteria allow for targeted assessment of model performance based on specific requirements. * Re-evaluation capabilities enable continuous monitoring and testing as datasets evolve, ensuring ongoing accuracy and reliability. * LangSmith allows for testing of code snippets within a data set. **4. Notable Quotes or Examples:** * Example of using DataFrame functions with Python code snippets to answer questions about the Titanic dataset. * "We're actually using data frame functions to to figure out our answer and that's really cool because not only are we using Snippets from python but we can actually use things where it says even storing API requests and search arguments which is pretty awesome." * Description of tracing agent execution: "Not only does is it telling us things like our tokens and our latency but it'll actually go through how it's doing the observations." * Explanation of custom evaluation criteria: "We're looking for the custom criteria evaluation chain and looking at the prediction and the reference." **5. Target Audience:** * AI/ML Engineers * Data Scientists * Developers working with LangChain and large language models * Individuals interested in using LangSmith for monitoring, evaluation, and debugging AI applications, especially those involving dynamic data. **Key Highlights:** - **Dynamic Data Testing with LangSmith**: The video focuses on using LangSmith to test dynamic data, showcasing a workflow for using the Titanic dataset to answer questions using Python code snippets and DataFrame functions. - **Tracing Agent Execution**: LangSmith allows tracing the execution of the agent, detailing the steps taken, including token usage, latency, observations, and interactions with the OpenAI model and Python REPL tool. - **Re-evaluating Data Sets**: The video demonstrates how to re-evaluate datasets in LangSmith, even simulating new data being added. This allows for continuous monitoring and testing as the data evolves. - **Custom Evaluation Criteria**: The video showcases running evaluations based on custom criteria to assess the correctness of the agent's predictions, utilizing GPT-4 to score the accuracy of results derived from the Titanic dataset. --- ### How to TEST a Q&A Correctiveness System with LangSmith **Published:** 8/18/2023 **Duration:** 16 minutes **YouTube:** https://youtube.com/watch?v=357zfdc0rUQ **Full LLM Text:** /video/cmis37oty001q04kwnscvdxzt/llm.txt **Description:** In this video we continue exploring the Lang Smith cookbook and specifically focus on building a Q&A Correctiveness System. I also talk about Ever Efficient AI, our AI Development Productize subscription model reach out to us for their AI development needs. Throughout the video, I explain the steps involved in building the system, such as installing dependencies like ChromaDB and document splitting loading packages, defining the dataset, and creating a prompt template. I also demonstrate how to evaluate the system's performance and iterate on it for better results. 📰 FREE eBooks & News: https://sendfox.com/nerdingio 👉🏻 Ranked #1 Product of the Day: https://www.producthunt.com/posts/ever-efficient-ai 📞 Book a Call: https://calendar.app.google/M1iU6X2x18metzDeA 🎥 Chapters 00:00 Introduction and Ever Efficient AI 00:32 Q&A Correctiveness System Overview 01:13 Dependencies and Dataset Creation 02:41 Building the Q&A System 04:19 Running the System and Vector Storage 06:09 Defining the Prompt Template 06:57 Creating a Runnable Map 09:50 Evaluating and Predicting Q&A Correctiveness 🔗 Links https://github.com/langchain-ai/langsmith-cookbook/tree/main https://github.com/langchain-ai/langsmith-cookbook/blob/main/testing-examples/qa-correctness/qa-correctness.ipynb https://www.trychroma.com/ https://smith.langchain.com/ ⤵️ Let's Connect https://everefficient.ai https://nerding.io https://twitter.com/nerding_io https://www.linkedin.com/in/jdfiscus/ https://www.linkedin.com/company/ever-efficient-ai/ **Summary:** ## Video Summary: How to TEST a Q&A Correctiveness System with LangSmith **1. Executive Summary:** This video provides a hands-on demonstration of how to build and test a Q&A system using LangSmith, a tool for debugging, testing and improving LLM powered apps. It covers creating a dataset, defining a Q&A system with ChromaDB and LangChain, running evaluations, and iterating on the system to improve its correctness. **2. Main Topics Covered:** * **Introduction to Q&A Correctiveness Systems:** Explanation of the concept and its importance in AI development. * **Setting up the Environment:** Installing necessary dependencies like ChromaDB, LangChain, and document loaders. * **Dataset Creation:** Building a dataset of question-answer pairs using LangSmith (focus on static examples). * **Q&A System Architecture:** Defining the system using a Vector Store Retriever (ChromaDB), OpenAI embeddings model, and GPT 3.5 turbo. * **Prompt Engineering:** Creating a prompt template to guide the LLM's responses. * **Chain Assembly:** Creating a runnable map to efficiently process questions and retrieve relevant information. * **Evaluation and Prediction:** Running evaluations within LangSmith and analyzing the results to identify areas for improvement. * **Iterative Improvement:** Modifying the prompt template and re-running evaluations to observe the impact of changes on system performance. * **LangSmith Playground:** Utilizing the LangSmith playground for testing chains, input prompts, and analyzing generated outputs. **3. Key Takeaways:** * LangSmith provides a powerful platform for testing and evaluating Q&A systems. * ChromaDB offers an open-source solution for local vector storage, facilitating efficient data retrieval. * Iterative improvement, through prompt engineering and re-evaluation, is crucial for optimizing Q&A system performance. * LangSmith's playground allows for rapid experimentation and debugging of AI chains. * The video showcases a practical example from the LangSmith cookbook, providing a solid foundation for building and testing Q&A systems. **4. Notable Quotes or Examples:** * **Dataset Example:** The video highlights creating a dataset as an array of dictionaries with "question" and "answer" pairs for evaluation. * **Prompt Template:** "Defining the fact that we want it to be a helpful q a assistant we're specifically looking at questions from the linksmith documentation and we're going to answer based on on that information." * **Iterative Improvement:** Shows the addition of a "If you don't have an answer just respond with you don't have an answer" to the prompt. **5. Target Audience:** * AI developers working with LangChain and large language models (LLMs). * Individuals interested in learning how to test and evaluate Q&A systems. * Professionals seeking to improve the accuracy and reliability of their AI applications. * Anyone curious about using LangSmith for debugging, tracing, and iterating on LLM workflows. **Key Highlights:** - **Testing Q&A Systems with LangSmith**: The video demonstrates how to use LangSmith to test a Q&A system, focusing on creating a dataset, defining the system, running evaluations, and iterating to improve results. - **Creating Datasets for Evaluation**: LangSmith allows you to create datasets with question-answer pairs to evaluate the performance of your Q&A system. It handles static examples, and dynamic data will be covered later. - **Leveraging ChromaDB for Vector Storage**: ChromaDB is used as an open-source, local vector store for embeddings, facilitating data retrieval for the Q&A system. Its Python and JavaScript clients integrate well with LangChain. - **Iterative Improvement Using Feedback Loops**: The video shows how to iterate on a Q&A system by modifying the prompt template and re-running evaluations to assess the impact of changes, using LangSmith's feedback mechanisms. - **LangSmith's Playground for Testing**: LangSmith provides a playground environment where you can test chains, input prompts, and analyze the generated outputs, allowing for rapid experimentation and debugging. --- ## About This content is from the nerding.io YouTube channel, focused on AI-powered software engineering. For the full transcript and detailed analysis of any video, visit `/video/:videoId/llm.txt`