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YouTube Videos I want to try to implement!

  • Intro to Agent Builder



    Date: 10/06/2025

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    Okay, so this video about OpenAI’s Agent Builder is seriously cool and timely. It walks you through creating agentic workflows visually, using a drag-and-drop interface. You can connect different tools and then publish these workflows using ChatKit and the Agents SDK. Think of it as a no-code/low-code way to orchestrate AI agents, taking them from theoretical concept to actual, functioning components in your applications.

    For those of us diving into AI coding and LLM-based workflows, this is a game-changer. We’re moving beyond just writing code and more into designing AI-driven processes. Being able to visually map out and connect tools, without getting bogged down in complex code at the initial stage, allows you to experiment and prototype much faster. You could build an agent to handle customer support queries, automate content generation, or even manage parts of your CI/CD pipeline—all without needing to be a hardcore AI specialist.

    What’s really exciting is the practical application. Imagine visually designing a workflow where a customer’s request triggers a search using a specific API, summarizes the findings using an LLM, and then sends a personalized response—all handled by an agent you built with drag-and-drop. This is where development is heading: less manual coding of every step and more orchestration of AI-powered components. I’m personally eager to try this out because it aligns perfectly with building more intelligent, automated systems while minimizing the boilerplate. Plus, visual tools can be amazing for quickly iterating and demonstrating complex workflows to non-technical stakeholders.

  • GitHub Trending Repos Weekly #2: Dayflow, React Old Icons, OpenTrack,tunn,VLA-Adapter, Blogr, GeoSVR



    Date: 10/06/2025

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    Okay, so this video is essentially a curated list of the top 15 trending GitHub repositories from a specific date in the future, September 26, 2025. What makes this immediately interesting is seeing where open-source is heading. A quick glance reveals projects spanning AI-powered music generation (SongPrep), visual mimicry (VisualMimic), and enhanced ComfyUI workflows (Lucy Edit). It’s not just about individual tools; it’s about the convergence of different technologies.

    For someone like me, who’s actively trying to integrate AI coding and no-code tools into the development lifecycle, this is gold. Imagine being able to leverage a tool like VisualMimic to rapidly prototype UI designs or using SongPrep to generate personalized background music for applications. The projects showcased hint at workflows that can dramatically reduce development time. Plus, the VLA-Adapter and OpenTrack repos point towards advanced data handling and real-time tracking capabilities – features that were incredibly complex and time-consuming to implement using traditional methods. Now, it looks like these tools could make them accessible to a wider range of developers.

    The real win here is seeing these advanced capabilities democratized through open-source. Blogr may be a way to integrate blog functionality into your web app with no code. Do I need every single one of these? Absolutely not. But even finding one or two that can significantly speed up a project or open up new possibilities makes exploring this list worthwhile. It gives me a glimpse into the future of development, and frankly, it’s inspiring to see how we can offload repetitive tasks and focus on higher-level problem-solving with these AI-powered tools. I’m definitely going to dive deeper into some of these!

  • 15 Trending AI Projects on GitHub: opcode, FastMCP, Dyad, RustGPT, Paper2Agent, Pepper, AG-UI,shimmy



    Date: 10/06/2025

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    Okay, so this video is basically a rapid-fire rundown of 15 trending AI projects on GitHub right now. We’re talking tools like opcode, FastMCP, RustGPT, and cool stuff like Paper2Agent that generates agents from research papers, all the way to agent UI frameworks like AG-UI. It even covers prompt engineering tools and coding agent templates. A real mixed bag of AI goodies!

    Why is this valuable? Because it’s a curated list of what’s actually catching fire in the AI dev space. As someone diving deep into AI-assisted development, no-code tools, and LLM workflows, I am always trying to find new ways to streamline repetitive tasks and automate the creation of new features. For instance, seeing projects like PromptEnhancer or coding agent templates like the Vercel-labs project immediately sparks ideas about automating the creation of more detailed and robust automated tests or improving the quality of automatically generated documentation. And projects like Qwen3-Omni? That could really boost our ability to integrate powerful multi-modal capabilities into existing Laravel apps.

    Honestly, this video feels like hitting the “refresh” button on my AI toolbox. It’s worth experimenting with because, let’s face it, the AI landscape is moving fast. It’s easy to get stuck in the same patterns. This video is a shortcut to discover projects that could genuinely supercharge your workflows, and finding just one or two things that can speed up development time by 10-20% translates into significant cost savings and faster turnaround times on projects. I’m definitely going to check out a few of these!

  • Sculptor: The missing UI for Claude Code



    Date: 10/05/2025

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    Okay, “Sculptor – The Missing UI for Claude Code”… This is exactly the kind of thing that gets me jazzed about the current state of development. Essentially, it’s a UI that lets you visually interact with and manage multiple Claude Code agents running in containers, showing you changes live.

    Why is this valuable? Because it bridges the gap between the “black box” of LLM-powered code generation and actual, usable code. As someone neck-deep in integrating AI into our Laravel workflows, the idea of seeing multiple AI coding agents working in parallel, with a visual preview of their output before committing to the codebase? Game-changer! We’re talking about faster experimentation, easier debugging, and ultimately, more confidence in the code these agents are producing.

    Imagine using this to prototype different features, compare different approaches generated by Claude, and then cherry-pick the best parts. Forget tedious manual code reviews of AI output; with Sculptor, you’re almost live-coding with the AI. Plus, the roadmap includes features like forking agents (think of it like branching in Git, but for AI code!) and custom Dockerfiles? Now that’s powerful. Seriously, I’m putting this on my to-try list for next week – it could completely revamp how we approach feature development and automation, and potentially cut our development time by a substantial amount by allowing us to build in parallel with AI.

  • Meta Ray Ban Display 24 Hours Later! Lets Talk…



    Date: 10/02/2025

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    Okay, so this video is a hands-on review of the new Ray-Ban Meta smart glasses after a full day of real-world use. The reviewer dives into the good, the bad, and the buggy, covering everything from the missing features to ordering snafus. Basically, it’s a no-holds-barred look at the current state of wearable AI.

    Why is this relevant to us as developers moving towards AI-enhanced workflows? Because it highlights the actual user experience of AI integration in a tangible product. We’re not just talking theory here; we’re seeing how AI translates into a consumer device. The insights on missing promised features directly translate to the importance of scoping, testing, and iterative development when working with LLMs and AI tools in our own projects. If Meta (with all their resources) can miss the mark on launch features, imagine the pitfalls we face when building custom AI-driven applications.

    Think about it: We could use the video’s insights on user expectations to inform our prompt engineering or feature prioritization in a Laravel app that leverages an LLM for content generation. Understanding the gap between promise and reality is critical. For instance, consider integrating a no-code tool like Drakkio (also mentioned in the video) for project management. Then, compare its ease of use and integration with the glasses’ actual capabilities. To me, the takeaway is simple: dive into these real-world examples, even with their flaws. It’s a crash course in user-centric AI development.

  • Your RAG Agent Needs a Hybrid Search Engine (n8n)



    Date: 10/02/2025

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    Okay, this video on building a Hybrid RAG Search Engine in n8n is exactly the kind of thing that gets me fired up about the future of development. We’re talking about moving beyond simple vector embeddings for Retrieval Augmented Generation (RAG) and into a more robust, real-world applicable search solution. It walks you through combining dense embeddings (semantic search), sparse retrieval (BM25, lexical search), and even pattern matching within an n8n workflow using Supabase and Pinecone. The coolest part? It dynamically weights these methods based on the query type. Forget AI hallucinations!

    Why is this valuable for us transitioning to AI-enhanced development? Because it addresses a very real problem: vector search alone often fails for exact matches and specific details. As someone who’s struggled with searching through piles of documentation and code using just vector databases, I can attest to that! This approach of hybrid search, especially the dynamic weighting, aligns perfectly with the kind of automation and intelligent workflows I’m aiming to build. Think about applying this to customer support bots that need to accurately find product information, or internal knowledge bases that require precise code snippet retrieval.

    Seriously, the idea of programmatically shifting search strategies based on the question being asked is a game-changer. I see this as a concrete step towards building truly intelligent and adaptable AI agents. Reciprocal Rank Fusion (RRF) isn’t something I’ve used extensively, but I can already think of 10 different applications for my clients to build a better search. I’m definitely going to be experimenting with this setup – n8n, Supabase, and Pinecone are all tools I’m familiar with, so the barrier to entry is pretty low and the potential payoff is huge. It’s time to stop relying on “good enough” vector search and start building something truly intelligent!

  • Turn ANY File into LLM Knowledge in SECONDS



    Date: 10/02/2025

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    Alright, this video on Docling is seriously inspiring for anyone, like myself, diving headfirst into AI-enhanced workflows. It tackles a huge pain point: getting your data, regardless of format, into a shape that LLMs can actually use effectively. RAG (Retrieval-Augmented Generation) is a powerful concept, but only if you can feed the LLM relevant and properly structured data. Docling streamlines the whole “curation” process by offering an open-source pipeline that can extract and chunk text from almost any file type. Seeing it in action, parsing PDFs, audio files, and other formats, really highlights its versatility.

    Why is this video a must-watch? Because it bridges the gap between theory and practice. We’re not just talking about RAG; we’re seeing how to practically implement it with a tool designed for the job. The demo of the Docling RAG AI agent is particularly valuable. It’s a template we can actually use, dissect, and adapt to our own projects. Imagine building a chatbot that can instantly access and understand all your company’s documentation, even if it’s scattered across PDFs, audio recordings, and other random formats. The video highlights how to make that happen.

    Honestly, I’m excited to start experimenting with Docling. The promise of simplifying data ingestion and chunking for LLMs is a game-changer, especially in our fast-paced world. The ability to train an AI agent on internal knowledge with minimal hassle? Sign me up! This video gives us not just the “what” but also the “how,” making it a practical stepping stone toward building more intelligent and automated systems.

  • AI Agents for Softr Databases: Build Smarter Tables with AI



    Date: 10/02/2025

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    Okay, this Softr video about AI Agents for databases is seriously inspiring, especially if you’re like me and trying to ditch the drudgery of repetitive coding tasks. Basically, it shows how you can use AI agents directly within your Softr databases to automate things like lead qualification, data enrichment, and even customer support. Forget about manually updating records or writing custom scripts for every little thing – these agents jump in on record creation or updates and take care of it.

    What’s killer is the level of control. You’re not just throwing data into a black box; you get to define the prompts, pick the AI model (GPT-4o, Claude, etc.), and set conditions for when the agent runs. Imagine automatically enriching new leads with company size, industry info, and a personalized follow-up email – all triggered when the “Lead Quality” score hits a certain threshold! Or automatically categorizing support tickets using your product documentation and drafting consistent responses? That’s huge for freeing up developer time.

    The beauty of this is its real-world applicability. Think CRMs, internal tools, client portals – anywhere you’re dealing with data that needs to be kept current and where your team is wasting time on manual updates. For example, on a recent project to build a lightweight internal tool, instead of writing custom functions to update and tag records, I could have used these agents and saved at least 2 days. It’s worth experimenting with because it’s a tangible way to see how AI and no-code can streamline development and let us focus on the more challenging, creative aspects of our work.

  • n8n Community Livestream



    Date: 10/02/2025

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    Okay, so this n8n livestream sounds pretty awesome, especially if you’re like me and diving headfirst into AI-powered automation. Essentially, it’s a deep dive into n8n, a no-code workflow automation platform, with a focus on new features, community involvement, and real-world AI integrations. They’re even showcasing how Fireflies.AI, a meeting assistant, can be woven into n8n workflows.

    Why is this valuable for us? Well, we’re trying to bridge the gap between traditional coding and these new AI/no-code tools. This livestream gives us a chance to see firsthand how to build complex automations without writing a ton of code. The Fireflies.AI integration is particularly interesting; imagine automating meeting summaries, action item extraction, and follow-ups – all within a visual workflow. It’s a fantastic example of how AI can augment our existing processes. Plus, hearing from n8n ambassadors gives insights into how others are using the platform to solve real-world problems.

    I’m personally excited to see the live demos. It’s one thing to read about features, but it’s another to see them in action, especially when combined with AI tools like Fireflies.AI. I’m already brainstorming how I can adapt some of those workflows for my own projects – maybe automating client onboarding or streamlining our internal reporting. Getting a chance to ask questions and network with the n8n community makes this definitely worth blocking out time for. Seeing how others are using the platform and how these new features can be applied to real projects will provide fresh ideas and maybe even shave off hours of development time.

  • Building Full Stack AI Agent Apps with CopilotKit + CrewAI



    Date: 09/29/2025

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    Okay, this video about integrating UI components with an AI assistant using CopilotKit’s Crew AI integration is exactly the kind of stuff that’s getting me excited these days! It’s basically showing how to build a full-stack application where your UI directly interacts with an AI agent “crew” to accomplish tasks, think recipe creation or workout planning.

    Why is this valuable? Well, for starters, it bridges the gap between no-code/low-code front-ends and the power of LLMs on the back-end. We’re talking real-time updates, streaming responses – the kind of slick UX that clients are starting to expect. Imagine building a project management tool where AI agents automate task assignments and progress tracking directly within the UI. Or an e-commerce platform where an AI helps customers find the perfect product based on complex needs, all powered by background agent workflows. This video is a hands-on demo of those possibilities.

    Honestly, what makes it worth experimenting with is how it moves beyond basic chatbot interactions. It’s about orchestrating AI-driven workflows, and presenting the results in a clean, user-friendly way. Plus, the Crew AI integration aspect is huge, as it opens up complex, multi-agent solutions that were previously a nightmare to build from scratch. I’m definitely adding this to my “must-try” list for next week!