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YouTube Videos I want to try to implement!

  • 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!

  • Scrape EVERY Social Media with n8n (CHEAP & EASY)



    Date: 09/27/2025

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    Okay, so this video is all about leveraging n8n (a no-code workflow automation platform) and Scrape Creators (a unified scraping API) to pull data from social media platforms. It’s essentially showing how to replace a bunch of individual, often clunky, API integrations with a single, cheaper, and more manageable solution. The demo covers scraping competitor ads and mining Reddit for content strategy, which are immediately useful use cases.

    Why is this exciting for a developer like me diving into AI and no-code? Well, it’s about efficiency and shifting focus. Instead of wrestling with different APIs and writing custom scrapers, you can use n8n to orchestrate the entire process with a drag-and-drop interface, and Scrape Creators handles the actual data extraction. This frees up time to focus on what matters: analyzing the data, building AI models on top of it, or automating decisions based on insights. I’m always looking for ways to reduce boilerplate and increase the leverage I get from my code.

    The real-world application is HUGE. Imagine automating market research, social listening, or lead generation with just a few clicks. You could build an AI-powered content recommendation engine using Reddit data, or monitor competitor strategies and trigger alerts when they launch new campaigns. I’m particularly interested in how this could streamline my automation workflows for client projects, saving me time and money while delivering more value. It’s absolutely worth experimenting with because it’s a tangible step towards a more AI-driven, no-code-enhanced development process!

  • Walmart Blasts Past Agent Experimentation



    Date: 09/25/2025

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    Okay, so the AI Daily Brief is talking about Walmart’s shift to “agent orchestration,” moving from individual AI agents to a unified system. They’ve got these four “super agents” – Sparky for customers, Marty for suppliers, and then agents for employees and developers – all coordinating specialized tasks. What’s fascinating is they’re already seeing real results like 40% faster customer support and cutting weeks off production cycles.

    Why is this video a must-watch for devs like us diving into AI? Because it’s a concrete example of scaling agentic systems. We’re not just playing with LLMs in isolation anymore; this shows how to structure them into complex, interconnected workflows. Think about applying this to e-commerce projects. Imagine an agent that handles product recommendations, another that manages inventory based on real-time demand, and a third that coordinates with suppliers for restocking, all working together.

    Walmart’s results highlight the potential for massive efficiency gains. Cutting shift planning from 90 to 30 minutes? That’s the kind of impact we’re chasing with automation. This inspires me to start thinking about how to break down our own project workflows into smaller, more manageable tasks that AI agents can handle, and then orchestrate those agents for end-to-end automation. It’s not just about the individual AI tool, but how they play together. Definitely worth experimenting with!

  • I Just Automated a Website with Cursor AI Agents



    Date: 09/25/2025

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    Okay, this video about building a self-coding website using Zapier and Cursor AI is seriously inspiring, and here’s why. It’s all about bridging that gap between a simple idea and a live, working piece of code completely hands-free. The creator uses Zapier’s new Cursor AI integration to build a workflow where a website automatically codes itself based on user comments. Someone leaves a comment like “a spinning rainbow square,” and boom, Cursor AI writes the HTML/CSS, which is then automatically merged and deployed via GitHub Pages.

    For a developer like me who’s actively exploring AI-driven workflows, this is pure gold. It showcases how you can leverage LLMs to automate the grunt work of coding and deployment. Imagine the possibilities! Think about rapidly prototyping UI elements or automating the creation of landing pages based on marketing copy. We could potentially use similar workflows for automatically generating API endpoints from database schemas or even refactoring legacy code with minimal human intervention.

    What makes this video really worth experimenting with is the tangible proof-of-concept. It’s not just theory; it’s a working example you can actually try out! Seeing that entire loop from idea to live code happening automatically is incredibly powerful. It’s a glimpse into a future where we, as developers, spend less time writing boilerplate code and more time architecting solutions and solving complex problems, guiding the AI rather than being in the weeds.

  • Don’t Miss AGUI : The Next Standard After MCP & A2A for Agents UI



    Date: 09/25/2025

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    Okay, so this video is all about AGUI, a new open protocol aiming to connect AI agents directly to any user interface. Think of it as a universal adapter that lets your AI bots interact with websites and applications as if they were human users. It’s being positioned alongside MCP and A2A as the next big standard in the agent world.

    Why is this valuable for us, developers diving into AI? Because it bridges the gap between LLMs and the real world. We’re always looking for ways to make our AI-powered apps more interactive and user-friendly. AGUI promises to simplify the process of building “agent-ready” interfaces, potentially cutting down the time it takes to integrate AI agents into existing systems. Instead of wrestling with complex APIs and custom integrations, AGUI offers a standardized way for agents to “see” and interact with the UI. This concept could be a game-changer for automating tasks like data entry, testing web applications, or even creating personalized user experiences.

    Honestly, what makes this worth experimenting with is the potential for faster development and wider AI adoption. Imagine building a Laravel app and being able to plug in an AI agent to handle customer support queries or automate form submissions. This isn’t just about cool tech; it’s about boosting efficiency and unlocking new possibilities for how users interact with our applications. The fact that it’s an open protocol is another win, fostering community-driven innovation and interoperability. Worth checking out, for sure.

  • Knowledge Graphs in n8n are FINALLY Here!



    Date: 09/25/2025

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    Okay, this video on integrating knowledge graphs into n8n workflows using Graphiti MCP is seriously exciting! It’s all about augmenting Retrieval-Augmented Generation (RAG) systems – the core of many AI agents – with knowledge graphs. Essentially, instead of just relying on vector databases (which can sometimes miss contextual relationships), we’re adding a layer that lets the agent understand and reason about the relationships within the data. Think of it as giving your agent a brain that can connect the dots, not just recall information.

    Why is this a game-changer for us transitioning into AI-enhanced development? Because RAG is becoming the backbone of many AI applications. We’re constantly looking for ways to make these RAG systems more robust and intelligent. This video directly addresses a limitation of traditional RAG by adding knowledge graphs on top. The ability to build AI agents that understand relationships within data is powerful. Imagine an agent that can not only find information but also reason about its implications. I can see immediately applying this to customer service bots, dynamic product recommendations, and even advanced data analysis workflows I’m building, like automating client research and identifying market opportunities. The steps provided are practical, and you can copy and paste most of it to get going right away!

    Honestly, what makes this worth experimenting with is the potential to create truly intelligent automation. We’re not just scripting anymore; we’re architecting systems that understand and reason. The video provides a solid foundation and a clear path to integrate this into our existing n8n-based workflows. The n8n template that is provided for free in the video is a fantastic starting point and I plan to use it for my agentic research project.

  • Build an Open AG-UI Canvas with CopilotKit + Mastra



    Date: 09/23/2025

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    Okay, this video on integrating Mastra with CopilotKit and AG-UI is seriously inspiring! It walks through building a real-time interactive UI powered by LLM agents. Basically, Mastra handles the heavy lifting – reasoning, managing multiple LLMs, workflows, and RAG – while CopilotKit and AG-UI take that agent output and turn it into a dynamic interface.

    Why’s it valuable? Because it showcases a practical way to orchestrate complex LLM interactions and present them in a user-friendly way. We’re talking about moving beyond simple chatbots and into building full-fledged AI-powered applications. Think about automating complex workflows with a visual interface, allowing users to guide and refine the process in real-time. It gets us closer to building real AI assistants that truly augment user capabilities.

    This video’s a must-watch because it’s not just theory. It’s a tangible example of how we can leverage LLMs, no-code UI components, and AI orchestration tools to build genuinely useful applications. I’m excited to experiment with this stack and see how it can streamline my development process and unlock new possibilities for client projects. Anything that makes this process easier is gold!