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YouTube Videos I want to try to implement!

  • Google Just Released an AI App Builder (No Code)



    Date: 07/25/2025

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    Okay, so this video is packed with exactly the kind of stuff I’ve been geeking out over: rapid AI app development, smarter AI agents, and AI integration within creative workflows. Basically, it’s a rundown of how Google’s Opal lets you whip up mini AI apps using natural language – like, describing an AI thumbnail maker and bam, it exists! Plus, the video dives into how ChatGPT Agents can actually find practical solutions, like scoring cheaper flights (seriously, $1100 savings!). And Adobe Firefly becoming this AI-powered creative hub? Yes, please!

    Why is this gold for a developer transitioning to AI? Because it showcases tangible examples of how we can drastically cut down development time and leverage AI for problem-solving. Imagine automating routine tasks or creating internal tools without writing mountains of code. The idea of building a YouTube-to-blog post converter with Opal in minutes? That’s the kind of automation that could free up serious time for more complex challenges. It’s not about replacing code, it’s about augmenting it.

    What really makes this worth a shot is the sheer speed and accessibility demonstrated. The old way of doing things involved weeks of coding, testing, and debugging. Now, we’re talking about creating functional apps in the time it takes to grab a coffee. This is about rapid prototyping, fast iteration, and empowering anyone to build AI-driven solutions. It’s inspiring and something I will be exploring myself.

  • We made Supabase Auth way faster!



    Date: 07/25/2025

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    Okay, this video on Supabase JWT signing keys is definitely worth checking out, especially if you’re like me and trying to level up your development game with AI and automation. In a nutshell, it shows how to switch your Supabase project to use asymmetric JWTs with signing keys, letting you validate user JWTs client-side instead of hitting the Supabase Auth server every time. The demo uses a Next.js app as an example, refactoring the code to use getClaims instead of getUser and walking through enabling the feature and migrating API keys. It also touches on key rotation and revocation.

    Why is this so relevant for us? Well, imagine you’re building an AI-powered app that relies heavily on user authentication. Validating JWTs server-side becomes a bottleneck, impacting performance. This video provides a clear path to eliminating that bottleneck. We can use this approach not only for web apps but also adapt it for serverless functions or even integrate it into our AI agents to verify user identity and permissions locally. It will help improve performance and reduce dependence on external services, and in turn that will speed up our entire development/deployment cycles.

    What I find particularly exciting is the potential for automation. The video mentions a single command to bootstrap a Next.js app with JWT signing keys. Think about integrating this into your CI/CD pipeline or using an LLM to generate the necessary code snippets for other frameworks. Faster authentication means faster feedback loops for users, and less dependency on external validation. It’s a small change that can yield huge performance and efficiency gains, and that makes it absolutely worth experimenting with.

  • ChatGPT Agent Just Went Public—Here’s My Honest Reaction



    Date: 07/25/2025

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    Okay, this ChatGPT Agent video is a must-watch if you’re trying to figure out how to integrate AI into your development workflow. The presenter puts the new Agent through a real-world gauntlet of tasks—from researching projectors to planning trips and even curating a movie newsletter. It’s a fantastic overview of what’s possible (and what isn’t) with this new tool.

    What makes this so valuable is seeing the ChatGPT Agent tackle problems that many of us face daily. Think about automating research for project requirements, generating initial drafts of documentation, or even scripting out basic user flows. Watching the Agent struggle with some tasks while excelling at others gives you a realistic expectation of what it can do. We could potentially use this for automating API research or generating boilerplate code based on specific requirements.

    What really excites me is the potential for no-code/low-code integrations using the Agent. Imagine feeding it user stories and having it generate a basic prototype in a tool like Bubble or Webflow. The possibilities are endless, but it’s crucial to understand its limitations, which this video clearly highlights. I’m definitely going to experiment with this—if nothing else, to save myself a few hours of tedious research each week!

  • This One Fix Made Our RAG Agents 10x Better (n8n)



    Date: 07/23/2025

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    Okay, so this video is all about turbocharging your RAG (Retrieval Augmented Generation) agents in n8n using a deceptively simple trick: proper markdown chunking. Instead of just splitting text willy-nilly by characters, it guides you on structuring your data by markdown headings before you vectorize it. Turns out, the default settings in n8n can be misleading and cause your chunks to be garbage. It also covers converting various formats like Google Docs, PDFs, and HTML into markdown so that you can process them.

    For someone like me, neck-deep in the AI coding revolution, this is gold. I’ve been wrestling with getting my LLM-powered workflows to produce actually relevant and coherent results. The video highlights how crucial it is to feed your LLMs well-structured information. The markdown chunking approach ensures that the context stays intact, which directly translates to better answers from my AI agents. I can immediately see this applying to things like document summarization, chatbot knowledge bases, and even code generation tasks where preserving the logical structure is paramount. Imagine using this for auto-generating API documentation from a codebase!

    Honestly, the fact that a 10-second fix can dramatically improve RAG performance is incredibly inspiring. It’s a reminder that even in the age of complex AI models, the fundamentals – like data preparation – still reign supreme. I’m definitely diving in and experimenting with this; even if it saves me from one instance of debugging nonsensical LLM output, it’ll be worth it!

  • Qwen 3 2507: NEW Opensource LLM KING! NEW CODER! Beats Opus 4, Kimi K2, and GPT-4.1 (Fully Tested)



    Date: 07/22/2025

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    Alright, so this video is all about Alibaba’s new open-source LLM, Qwen 3-235B-A22B-2507. It’s a massive model with 235 billion parameters, and the video pits it against some heavy hitters like GPT-4.1, Claude Opus, and Kimi K2, focusing on its agentic capabilities and long-context handling. Think of it as a deep dive into the current state of the art in open-source LLMs.

    For someone like me, who’s knee-deep in exploring AI-powered workflows, this video is gold. It’s not just about the hype; it’s about seeing how these models perform in practical scenarios like tool use, reasoning, and planning—all crucial for building truly automated systems. Plus, the video touches on the removal of “hybrid thinking mode,” which is fascinating because it highlights the trade-offs and challenges in designing these complex AI systems. Knowing Qwen handles a 256K token context is a game changer when thinking about the possibilities around document processing and advanced AI workflows.

    What makes it worth experimenting with? Well, the fact that you can try it out on Hugging Face or even run it locally is huge. This isn’t just theoretical; we can get our hands dirty and see how it performs in our own projects, maybe integrate it into a Laravel application or use it to automate some of those tedious tasks we’ve been putting off. For example, could it write better tests that I keep putting off or, even better, is it capable of self-debugging and auto-fixing things? I’m definitely going to be diving into this one.

  • Google Veo 3 For AI Filmmaking – Consistent Characters, Environments And Dialogue



    Date: 07/21/2025

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    Okay, this VEO 3 video looks incredibly inspiring for anyone diving into AI-powered development, especially if you’re like me and exploring the convergence of AI coding, no-code tools, and LLM-based workflows. It basically unlocks the ability to create short films with custom characters that speaks custom dialogue, leveraging Google’s new image-to-video tech to bring still images to life with lip-synced audio and sound effects. Talk about a game changer!

    The video is valuable because it’s not just a dry tutorial; it demonstrates a whole AI filmmaking process. It goes deep on how to use VEO 3’s new features, but also showcases how to pair it with other AI tools like Runway References for visual consistency, Elevenlabs for voice control (I have been struggling to find a good tool), Heygen for translation, Suno for soundtracks, and even Kling for VFX. The presenter also gives great prompting tips and also some cost savings ideas (a big deal!). This multi-tool approach is exactly where I see the future of development and automation going. It is about combining the best of breed tools to create new workflows and save time and money.

    For example, imagine using VEO 3 and Elevenlabs to quickly prototype interactive training modules with personalized character dialogues. Or, think about automating marketing video creation by generating visuals with VEO 3, sound effects with Elevenlabs and translating them into multiple languages. What I found to be very interesting is how it can be used to create story boarding content quickly. The possibilities are endless! I’m genuinely excited to experiment with this workflow because it bridges the gap between traditional filmmaking and AI-driven content creation. I am especially interested to see how the presenter created the short film, Hotrod. I want to see if I can create something similar.

  • Rork tutorial: build an AI app for the App Store (million dollar app idea)



    Date: 07/21/2025

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    Okay, so this video is all about building a niche AI-powered calorie counting app for vegans using Rork, a no-code AI app builder. Think of it as a “Cal AI for Vegans.” What’s immediately cool about it is the speed – going from idea to a working MVP in one session. As someone neck-deep in exploring how AI can streamline development, that claim alone is worth investigating. The video dives into using image inputs for calorie counting (hello GPT-4 Vision!), real-time debugging, and even touches on Gen Z-friendly design. For me, the potential to rapidly prototype and validate app ideas like this is incredibly appealing, especially when you’re used to spending weeks, if not months, on similar projects.

    What makes this video particularly valuable for those transitioning to AI-enhanced workflows is its practical approach. It’s not just theory; it shows you how to connect OpenAI’s GPT-4 Vision, how to debug in real-time, and how to optimize for a specific audience. We can apply the same principles to other automation projects. For example, imagine building an internal tool for analyzing customer support tickets using similar AI vision and language models, customized for specific industries or products. The key is taking these no-code/low-code tools and blending them into custom workflows.

    Ultimately, the appeal lies in its accessibility and speed. It’s a great example of how you can leverage AI and no-code tools to rapidly iterate and build specialized applications without being bogged down by traditional coding complexities. Plus, the tips on app store publishing, design prompts, and debugging could save a ton of time and headaches. I’m definitely keen to experiment with Rork after watching this. It’s not about replacing code entirely, but about strategically using AI to accelerate the development lifecycle.

  • Uncensored Open Source AI Video & Images with NO GPU!



    Date: 07/18/2025

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    Okay, this video on using Runpod to access powerful GPUs for AI image and video generation with tools like Flux and Wan is seriously inspiring! It tackles a huge barrier for many developers like myself who are diving into the world of AI-enhanced workflows: the prohibitive cost of high-end GPUs. The presenter walks through setting up accounts on Runpod, Hugging Face, and Civitai, renting a GPU, and then deploying pre-made ComfyUI templates for image and video creation. Think of it as a “GPU-as-a-service” model, where you only pay for the compute you use.

    This is valuable for a few reasons. First, it democratizes access to AI tools, allowing developers to experiment and innovate without a massive upfront investment. Second, it demonstrates how we can leverage open-source tools and pre-built workflows to quickly build amazing AI applications. I can immediately see this applying to content creation for marketing materials, generating assets for game development, or even automating visual aspects of web applications. Imagine feeding your existing product photos into one of these models and generating fresh marketing images tailored for specific demographics.

    What makes this video particularly worth experimenting with is the focus on ease of use. The presenter emphasizes that you don’t need to be an expert in ComfyUI to get started, which removes a huge hurdle. Plus, the promise of sidestepping content restrictions on other platforms is enticing. I’m definitely going to try this out. Renting a GPU for a few hours to prototype an AI-powered feature is far more appealing than purchasing dedicated hardware, especially given how quickly this space is evolving!

  • Upgrade Your AI Agents with Fine-Tuning (n8n)



    Date: 07/18/2025

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    Okay, so this video is all about leveling up your AI agents through fine-tuning, and automating the whole process using Airtable and n8n. For someone like me, knee-deep in the transition from traditional PHP/Laravel to AI-powered development, it’s gold. I’ve been experimenting with no-code tools to accelerate development and LLMs to automate complex tasks, and fine-tuning is the obvious next step. We’re not just talking about generic AI responses anymore, but tailoring them to specific domains, tones, and formats, which is what clients actually want.

    Why is this valuable? Well, it bridges the gap between the promise of AI and practical application. Imagine fine-tuning a model for a specific client’s tone of voice, then automating content creation using that fine-tuned model. The video shows a scalable pipeline for prompt/response training, format conversion, and API integration – crucial for efficiently managing these fine-tuned models. And, it explores the different approaches for fine-tuning on different model providers which saves a lot of research time. It’s about moving beyond simple prompts to creating truly bespoke AI solutions, and that’s where the real competitive advantage lies.

    I see myself applying this to streamline my content generation workflows, enhance chatbot responses, and even fine-tune models for code generation tasks. The Airtable and n8n combo makes it particularly appealing because it abstracts away much of the complexity, allowing me to focus on the quality of the training data and the desired outcome. Building a scalable fine-tuning pipeline isn’t just a cool experiment; it’s a step towards fully integrated AI-driven workflows that can redefine how we approach development. Definitely worth the time to dive in and experiment.

  • I Went Deep on Claude Code—These Are My Top 13 Tricks



    Date: 07/17/2025

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    Alright, so this video is all about turbocharging your Claude Code experience. It’s not just about the basics; it’s diving into 11 tips and tricks that make Claude Code feel like a natural extension of your workflow, especially if you’re using tools like Cursor or VS Code. The presenter covers everything from quick setup and context management to custom slash commands and clever ways to integrate screenshots and files. They even touch on using Claude as a utility function and leveraging hooks.

    Why is this gold for us developers making the leap into AI coding and no-code? Because it’s about making AI work for you, not the other way around. We’re not just talking theory here. Imagine being able to initialize a project with a single command, or using Claude as a sub-agent to handle multi-tasking? That’s the kind of automation that frees up serious brainpower for more complex problem-solving. The custom commands and coloring cursor command looked very cool.

    I’m particularly excited about the idea of using Claude as a utility function and the implementation of hooks. Think about automating repetitive tasks or generating code snippets with a simple command. The video shows how to do just that! Plus, the inclusion of a Raycast script to trigger Claude from anywhere? That’s next-level efficiency. For anyone experimenting with LLM-powered workflows, these are the kinds of practical tips that can seriously bridge the gap between concept and tangible productivity gains. I’m already thinking about how to adapt some of these to my Laravel projects, especially for API integrations and automated testing. Worth a look for sure!