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YouTube Videos I want to try to implement!

  • The KEY to Infinitely Scale Your n8n RAG Agents



    Date: 09/22/2025

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    Okay, this n8n RAG scaling video is straight fire for anyone diving into AI-powered workflows! It tackles a real-world problem I’ve definitely hit: building a killer RAG system is easy with a few files, but what happens when you need to ingest thousands? This video shows you how to use n8n, Supabase, and some clever orchestrator patterns to reliably process massive amounts of data. We’re talking scaling to thousands of files per hour.

    What makes this valuable is its focus on practical solutions to common AI integration bottlenecks. API rate limits, memory overloads, system instability – these are the dragons you face when moving from a PoC to a production-ready system. The video breaks down how to build an orchestrator workflow that handles parallel executions, tracks parent/child processes using Supabase, and even handles errors with automated retries. Plus, the deep dive into using webhooks instead of sub-workflows is a game-changer for performance and tracking – something I’ve been experimenting with myself lately and seen HUGE improvements from.

    Imagine building a document processing pipeline for a legal firm or a content ingestion system for a large e-commerce site. This video provides a blueprint for automating those processes at scale. Honestly, the techniques for handling errors and preventing system overloads are worth the price of admission alone. I’m definitely going to be experimenting with these orchestration patterns in my next project. The layered error handling and Supabase configuration tips are gold!

  • Windows 11 Users Need This File Explorer Replacement



    Date: 09/22/2025

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    Alright, this video is about switching from the clunky default Windows File Explorer to a modern alternative called “Files.” This tool promises easier file management, a better UI, and more customization. Why does this matter for us, the AI-driven developers? Think about it: we’re automating everything else, but are we still dealing with a dated file system?

    This is important because, as we bring in AI coding and no-code tools, efficient file management becomes crucial. We’re handling more files, more code snippets, more AI-generated assets, and we need a system that can keep up. Picture this: you use an LLM to generate hundreds of image variations for a marketing campaign. “Files” could help you organize, tag, and quickly access those assets in a way the default explorer just can’t. Plus, if the UI is genuinely more intuitive, that means less time searching and more time coding.

    Honestly, it’s worth trying out because it’s a small change that could significantly impact your daily workflow. We’re already embracing automation in our code; why not extend that to how we manage the very foundation of our projects—the files themselves? It makes sense to see if “Files” can boost our productivity and make our lives a bit easier, one file at a time.

  • This AI Changes Film, Games, and 3D Forever (and you can use it today for Free)



    Date: 09/19/2025

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    Okay, this video on World Labs’ Marble model is seriously inspiring, especially for us devs exploring the AI frontier! It’s all about creating interactive 3D environments from single images, letting you “walk around” inside them. Think of it: instead of painstakingly modeling everything from scratch, you’re using AI to build a world.

    What makes this valuable is how it bridges the gap between traditional content creation and AI-powered workflows. The video walks through creating a short film entirely within World Labs, using tools like Reve for AI clean-up, VEO 3 for animation, and even integrating it into Premiere Pro for post-production. This shows that you don’t need to abandon your existing skills; you augment them with AI.

    Imagine automating environment design for games or creating immersive VR experiences with minimal modeling. This isn’t just theoretical; the video shows it in action. For me, the idea of rapidly prototyping interactive environments and then refining them with familiar tools is a game-changer. It’s definitely worth experimenting with because it provides a glimpse into a future where creativity is amplified, not replaced, by AI. The friction is gasoline for creativity, as the author puts it.

  • New Embed Field & Enhanced Tag Features in Softr

    News: 2025-09-18



    Date: 09/19/2025

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    Softr just rolled out some thoughtful updates to its embed and tagging features, making it faster to build more dynamic apps. The embed field now accepts direct URLs, letting you drop in content from services like Spotify or YouTube without having to hunt for the specific embed code. They’ve also overhauled tags, adding custom sorting and conditional color-coding that can pull directly from your data source. This is a great example of a no-code platform removing friction and giving builders more creative control over data presentation. These seemingly small updates significantly improve the end-user experience, letting you create more professional internal tools or client portals with less effort.

  • Ai Home Datacenter Build (part 1)



    Date: 09/16/2025

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    This video showcases a homelab datacenter rebuild, focusing on upgrading to new racks (APC AR3150) and incorporating servers (Dell R730xd, R930) and JBODs (NetApp DS4246/DE6600) for optimized storage performance. It’s all about building a robust, high-performance home datacenter, which is super relevant for us as we explore AI-driven workflows.

    Why’s this valuable? Because as we integrate AI coding and LLMs into our development lifecycle, we’re increasingly dealing with data-intensive tasks: training models, managing large datasets, automating testing. This video highlights the importance of a solid infrastructure to support those workloads. Thinking about how to scale and optimize our local development environments – maybe even building a homelab like this – lets us prototype and test AI-powered features more effectively. Plus, understanding hardware limitations helps us write more efficient code and design better solutions when deploying to the cloud.

    Imagine using no-code tools to automate the monitoring and management of this homelab, or even leveraging LLMs to predict storage needs and optimize data placement. It’s all about taking that deep understanding of infrastructure and automating it! Seeing someone build this from the ground up is inspiring. It’s a reminder that understanding the foundations empowers us to build better, more scalable AI-driven applications, and it’s got me thinking about finally upgrading my own dev environment. Definitely worth a watch!

  • QWEN3 NEXT 80B A3B the Next BIG Local Ai Model!



    Date: 09/14/2025

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    This video is all about Qwen3 Next, a new LLM architecture emphasizing speed and efficiency for local AI inference. It leverages “super sparse activations,” a technique that dramatically reduces the computational load. While there are currently some quirks with running it locally with vllm and RAM offloading, the video highlights upcoming support for llama.cpp, unsloth, lmstudio, and ollama, making it much more accessible.

    Why is this exciting for us as we transition to AI-enhanced development? Well, the promise of faster local AI inference is HUGE. Think about the possibilities: real-time code completion suggestions, rapid prototyping of AI-driven features without relying on cloud APIs, and the ability to run complex LLM-based workflows directly on our machines. We’re talking about a potential paradigm shift where the latency of interacting with AI goes way down, opening up new avenues for creative coding and automation.

    The potential applications are endless. Imagine integrating Qwen3 Next into a local development environment to automatically generate documentation, refactor code, or even create entire microservices from natural language prompts. The fact that it’s designed for local inference means more privacy and control, which is crucial for sensitive projects. I’m particularly keen to experiment with using it for automated testing and bug fixing – imagine an AI that can understand your codebase and proactively identify potential issues! This is worth experimenting with, not just to stay ahead of the curve, but to fundamentally change how we build software, making the development process more intuitive, efficient, and dare I say, fun!

  • Making n8n AI Agents Reliable (Human-in-the-Loop Demo)



    Date: 09/13/2025

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    Okay, so this video is all about bringing human oversight into your AI and automation workflows, specifically within n8n. Till Simon from gotoHuman chats with Max from theflowgrammer, and they demo how gotoHuman lets you inject human review steps right into your n8n flows. Think of it as a “pause” button that sends data to a real person for a sanity check or approval before it gets processed further by your automation.

    This is gold for us as we’re leveling up our AI game. We’re building increasingly complex LLM-powered workflows, and the thought of letting those run completely unsupervised can be terrifying. Imagine an LLM generating content for a client’s website – without human review, you could end up with some serious brand damage. This video shows a practical way to mitigate that risk. It’s about responsibly integrating AI, acknowledging that sometimes a human eye is still crucial, especially when dealing with sensitive data or critical decisions. Plus, the fact that Till built the n8n node himself highlights how accessible building integrations and tools is becoming!

    The real power here is the ability to create guardrails for our automations. We could use gotoHuman to review AI-generated code before deployment, approve financial transactions based on AI predictions, or even just QA content before it goes live. It’s a game-changer for building truly reliable and trustworthy AI-driven systems. Honestly, seeing how easily it integrates with n8n makes me want to spin up a demo flow right now and start experimenting. It feels like a crucial piece of the puzzle for anyone trying to bridge the gap between cutting-edge AI and real-world business needs.

  • OpenLovable: NEW Opensource Agent Mode Can Build ANYTHING! Create Full-Stack Apps With No CODE!



    Date: 09/13/2025

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    Okay, so this video is about OpenLovable, which is positioning itself as an open-source alternative to Lovable. Basically, it’s an AI-powered full-stack developer that lets you build apps and websites without writing code. But here’s the kicker: it’s all local, open-source, and leverages Firecrawl’s web scraping and AI magic. Think of it as having a personal AI coder who doesn’t lock you into a platform or charge you subscription fees.

    Why is this video inspiring and valuable? As someone diving deeper into AI-enhanced workflows, the idea of a local, open-source AI tool that can clone websites into React/Tailwind apps and let me edit them with natural language is a huge deal. We’re talking potentially automating tedious front-end tasks and rapidly prototyping ideas. Imagine cloning a competitor’s site to quickly build a proof-of-concept for a client – that’s a serious time-saver compared to building from scratch. Plus, the “agent mode” described hints at deeper automation possibilities.

    For me, the key takeaway is the control and flexibility. Vendor lock-in has always been a pain point with no-code platforms. OpenLovable promises the benefits of AI-assisted development without sacrificing ownership of the code. I’m definitely going to experiment with this. The idea of using AI to generate boilerplate code and then fine-tuning it myself feels like the perfect balance between automation and customization. It aligns perfectly with my goal of leveraging AI to augment my development process, not replace it entirely, and I think a lot of other devs will feel the same way.

  • THIS is the REAL DEAL 🤯 for local LLMs



    Date: 09/12/2025

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    Okay, this video looks like a goldmine for anyone, like me, diving headfirst into local LLMs. Essentially, it’s about achieving blazing-fast inference speeds – over 4000 tokens per second – using a specific hardware setup and Docker Model Runner. It’s inspiring because it moves beyond just using LLMs and gets into optimizing their performance locally, which is crucial as we integrate them deeper into our workflows. Why is this valuable? Well, as we move away from purely traditional development, understanding how to squeeze every last drop of performance from local LLMs becomes critical. Imagine integrating a real-time code completion feature into your IDE powered by a local model. This video shows how to get the speed needed to make that a reality. The specific hardware isn’t the only key, but the focus on optimization techniques and the use of Docker for easy deployment makes it immediately applicable to real-world development scenarios like setting up local AI-powered testing environments or automating complex code refactoring tasks. Personally, I’m excited to experiment with this because it addresses a key challenge: making local LLMs fast enough to be truly useful in everyday development. The fact that it leverages Docker simplifies the setup and makes it easier to reproduce, which is a huge win. Plus, the resources shared on quantization and related videos provide a solid foundation for understanding the underlying concepts. This isn’t just about speed; it’s about unlocking new possibilities for AI-assisted development, and that’s something I’m definitely keen to explore.

  • Stop Wasting Time – 10 Docker Projects You’ll Actually Want to Keep Running



    Date: 09/10/2025

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    Okay, this video is exactly what I’m talking about when it comes to leveling up with AI-assisted development. It’s a walkthrough of 10 Docker projects – things like Gitea, Home Assistant, Nginx Proxy Manager, and even OpenWebUI with Ollama – that you can spin up quickly and actually use in a homelab. Forget theoretical fluff; we’re talking practical, real-world applications.

    Why is this gold for us as developers shifting towards AI? Because it provides tangible use cases. Imagine using n8n, the no-code automation tool highlighted, to trigger actions in Home Assistant based on data from your self-hosted Netdata monitoring. Or using OpenWebUI with Ollama to experiment with local LLMs, feeding them data from your Gitea repos. These aren’t just isolated projects; they’re building blocks for complex, automated workflows, the kind that AI can dramatically enhance.

    For me, the most inspiring aspect is the focus on practicality. It’s about taking control of your services, experimenting with new tech, and learning by doing. I’m already thinking about how I can integrate some of these containers into my development pipeline, maybe using Watchtower to automate updates or Dozzle to streamline log management across my projects. This is the kind of hands-on experimentation that unlocks the real potential of AI and no-code tools. Definitely worth a weekend dive!