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YouTube Videos I want to try to implement!

  • 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!

  • Your ULTIMATE n8n RAG AI Agent Template just got a Massive Upgrade



    Date: 09/09/2025

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    Okay, so this video is all about leveling up your RAG (Retrieval Augmented Generation) game using n8n. It tackles the common frustrations we’ve all experienced: RAG falling short because it misses context, fails to connect related ideas across documents, and lacks the smarts to really understand what you’re asking. It’s not just another “how-to” – it’s a “how-to make RAG actually useful.”

    This video is gold for anyone transitioning to AI-enhanced workflows because it introduces three powerful strategies that address the core problems with traditional RAG. Agentic Chunking ensures context isn’t lost when documents are split. Agentic RAG gives the agent the ability to intelligently explore your knowledge base. And finally, Reranking refines the search results for precision. Imagine using this to build a support bot that doesn’t just regurgitate snippets but actually understands the user’s problem and provides comprehensive, connected solutions.

    What I find really exciting is the “agentic” approach. It’s like giving your RAG setup a brain, allowing it to reason and make decisions about how to best extract information. I’m keen to experiment with the n8n template to automate tasks like onboarding new employees with personalized knowledge delivery, or even building a custom AI assistant for complex data analysis. The promise of a RAG system that truly understands the data is a huge leap forward, and definitely worth diving into.

  • The SMARTER Way to Build RAG Agents (n8n + DeepEval)



    Date: 09/08/2025

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    Okay, so this video on integrating DeepEval with n8n is seriously inspiring, especially if you’re, like me, diving deep into AI-powered automation. It shows you how to move beyond “vibe testing” your AI agents in n8n and start using a proper, metric-driven evaluation framework. We’re talking about setting up a real testing system with datasets, metrics, and automated runs, all powered by DeepEval, a leading open-source AI evaluation tool.

    What makes this valuable is that it addresses a huge pain point: how do you know if the tweaks you’re making to your AI models are actually improving things? The video demonstrates how to deploy DeepEval (even on a free tier!), connect it to n8n via API, and then run tests using a bunch of built-in metrics like faithfulness and relevancy. You can even define custom metrics for specific domains and generate synthetic test cases. Imagine being able to automatically log all of this in Airtable!

    For me, the real kicker is the shift from gut feeling to hard data. I’ve spent way too long tweaking prompts and hoping for the best. The idea of using DeepEval within n8n to objectively measure performance – generating test cases from documents and tracking things with metrics like faithfulness and contextual relevancy – is revolutionary. I’m excited to experiment with the DeepEval wrapper and see how much more robust I can make my LLM-powered workflows. No more whack-a-mole, just solid improvements!

  • 10 Pro Secrets of AI Filmmaking!



    Date: 09/04/2025

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    Okay, this AI filmmaking video is gold for us devs diving into AI and no-code. It’s not just about how to use the tools, but the process – that’s the real takeaway. He’s covering everything from organizing your project (a “murder board” for creatives? Genius!) to upscaling images and videos, fixing inconsistent AI audio with stem splitting and tools like ElevenLabs, and even storytelling tips to make your AI films stand out.

    Why’s it valuable? Well, we often focus on the tech itself – the LLMs, the APIs – but forget that a solid workflow is crucial for efficient and impactful results. The tips on audio consistency (using stem splitters) and video upscaling are directly applicable. I could see using these concepts to enhance the output of internal automation tools I’ve built. For example, maybe I have a script that uses AI to generate marketing videos, but the audio is always a little off. This video provides concrete ways to improve that.

    Plus, the storytelling aspects – breaking compositional rules, using outpainting – translate to designing more engaging user interfaces, even in traditional web apps. The emphasis on being platform-agnostic and focusing on problem-solving really resonates, because as AI evolves, we need to adapt our skills constantly. The “think short, finish fast” mentality is perfect for rapid prototyping in the AI space. Honestly, I’m already itching to try the “outpainting” technique to see how it can be used for creative visual effects in my next side project. It’s this kind of practical, creative advice that makes the video worth the watch!

  • EASIEST Way to Fine-Tune a LLM and Use It With Ollama



    Date: 09/03/2025

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    Okay, this video on fine-tuning LLMs with Python for Ollama is exactly the kind of thing that gets me excited these days. It breaks down a complex topic – customizing large language models – into manageable steps. It’s not just theory; it provides practical code examples and a Google Colab notebook, making it super easy to jump in and experiment. What really grabbed my attention is the focus on using the fine-tuned model with Ollama, a tool for running LLMs locally. This empowers me to build truly customized AI solutions without relying solely on cloud-based APIs.

    From my experience, the biggest hurdle in moving towards AI-driven development is understanding how to tailor these massive models to specific needs. This video directly addresses that. Think about automating code generation for specific Laravel components or creating an AI assistant that understands your company’s specific coding standards and documentation. Fine-tuning is the key. Plus, using Ollama means I can experiment and deploy these solutions on my own hardware, giving me more control over data privacy and costs.

    Honestly, what makes this video worth experimenting with is the democratization of AI. Not long ago, fine-tuning LLMs felt like a task reserved for specialized AI researchers. This video makes it accessible to any developer with some Python knowledge. The potential for automation and customization in my Laravel projects is huge, and I’m eager to see how a locally-run, fine-tuned LLM can streamline my workflows and bring even more innovation to my client projects. This is the kind of knowledge that helps transition from traditional development to an AI-enhanced approach.