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YouTube Videos I want to try to implement!

  • I gave AI full control over my database (postgres.new)



    Date: 05/03/2025

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    Okay, this database.build (formerly postgres.new) video is seriously inspiring for anyone diving into AI-assisted development. It’s essentially a fully functional Postgres sandbox right in your browser, complete with AI smarts to help you generate SQL, build database diagrams, and even import CSVs to create tables on the fly. Think about it: no more local setup headaches, just instant database prototyping!

    Why is this a big deal for us? Well, imagine quickly mocking up a data model for a new Laravel feature without firing up Docker or dealing with migrations manually. The AI assistance could be a huge time-saver for generating boilerplate SQL or even suggesting schema optimizations. Plus, the built-in charting and reporting features could be invaluable for rapidly visualizing data and presenting insights to clients before even writing a single line of PHP. This kind of rapid prototyping and iteration is exactly where I see the biggest wins with AI and no-code tools.

    Frankly, the idea of spinning up a database, generating a data model, and visualizing some key metrics all within a browser in a matter of minutes is incredibly powerful. It’s like having a supercharged scratchpad for database design. I’m definitely experimenting with using this to brainstorm new application features and generate initial database schemas way faster than I could before. Definitely worth a look!

  • We improved Supabase AI … A lot!



    Date: 05/02/2025

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    Okay, so this video with the Supabase AI assistant, where “John” builds a Slack clone using only AI prompts, is seriously inspiring. It’s a clear demonstration of how far AI-assisted development has come. We’re talking about things like schema generation, SQL debugging, bulk updates, even charting – all driven by natural language. For someone like me who’s been wrestling with SQL and database design for ages, the idea of offloading that work to an AI while I focus on the higher-level logic is a game-changer.

    What really stands out is seeing these AI tools applied to a practical scenario. Instead of just theoretical possibilities, you’re watching someone build something real – a Slack clone. Think about the implications: instead of spending hours crafting complex SQL queries for data migrations, you could describe the desired transformation in plain English and let the AI handle the syntax. Or imagine generating different chart types to visualize database performance with a single prompt! This isn’t just about saving time; it’s about unlocking a level of agility and experimentation that was previously out of reach.

    Honestly, seeing this makes me want to dive in and experiment with Supabase’s AI assistant ASAP. I can envision using it to rapidly prototype new features, explore different data models, and even automate tedious database administration tasks. Plus, debugging SQL is one of those tasks that every developer loves to hate. I really recommend giving it a try, because you’ll start to notice other tasks you could offload. It feels like we’re finally getting to a point where AI isn’t just a buzzword, but a genuine force multiplier for developers.

  • 3 new things you can do with SupaCharged Edge Functions



    Date: 05/02/2025

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    Okay, this Supabase Functions v3 video is seriously inspiring for anyone diving into AI-powered development, especially with LLMs. It’s not just about “new features,” it’s about unlocking practical workflows. The demo shows how to proxy WebSocket connections through a Supabase Edge Function to OpenAI’s Realtime API (key protection!), and how to handle large file uploads using temporary storage with background processing. Imagine zipping up a bunch of vector embeddings and sending them off for processing.

    Why is this gold for us? Well, think about securing API keys when integrating with LLMs – the WebSocket proxy is a game-changer. It’s all about building secure, scalable AI-driven features without exposing sensitive credentials directly in the client-side code. Plus, offloading heavy tasks like processing large files (mentioned in the video) to background tasks is crucial for maintaining a responsive user experience. This helps when dealing with massive datasets for training or fine-tuning models. It’s literally the type of thing that helps to scale.

    The potential here is huge. Imagine building a real-time translation app powered by OpenAI, or an automated document processing pipeline that extracts key information and stores it in your database, triggered by a file upload. Supabase is leveling up its functions to compete with the big players. It’s time to get our hands dirty experimenting with these features – the combination of secure API access, background tasks, and temporary storage feels like a major step forward in building robust AI applications that are both secure and scalable. I am now adding “rebuild my OpenAI Slack bot using Supabase Functions v3” to my project list.

  • Manage secrets and query third-party APIs from Postgres



    Date: 05/02/2025

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    This Supabase video about Foreign Data Wrappers (FDW) is a game-changer for any developer looking to streamline their data workflows. In essence, it shows you how to directly query live Stripe data from your Supabase Postgres database using FDWs and securely manage your Stripe API keys using Supabase Vault. Why is this so cool? Imagine being able to run SQL aggregates directly on your Stripe data without having to build and maintain separate ETL pipelines!

    For someone like me who’s been diving deep into AI-enhanced workflows, this video is pure gold. It bridges the gap between complex data silos and gives you the power to access and manipulate that data right within your existing database environment. Think about the possibilities for building automated reporting dashboards, triggering custom logic based on real-time Stripe events, or even training machine learning models with up-to-date financial data. Plus, the integration with Supabase Vault ensures that your API keys are securely managed, which is paramount in any data-driven application.

    This approach could revolutionize how we handle real-world development and automation tasks. Instead of writing custom code to fetch and process data from external APIs, you can simply use SQL. And, let’s be honest, who doesn’t love writing SQL? I’m definitely going to experiment with this. The time saved by not having to build separate data integration pipelines and increased agility from having direct access to Stripe data within Postgres are huge wins!

  • Suna: FULLY FREE Manus Alternative with UI! Generalist AI Agent! (Opensource)



    Date: 05/01/2025

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    Okay, so this video introduces Suna AI, which is pitched as an open-source, fully local AI agent. It’s positioned as a direct competitor to commercial offerings like Manus and GenSpark AI, but with the significant advantages of being free and having a clean, ready-to-use UI. The video walks through setting it up with Docker, Supabase (for the backend), and integrating LLM APIs like Anthropic Claude via LiteLLM. It even covers how to use Daytona for easier environment provisioning, which is super helpful.

    Why is this interesting for us as developers moving into AI-enhanced workflows? Well, the promise of a powerful, fully local AI agent is huge. I’ve been increasingly focused on bringing AI capabilities closer to the metal for better control, privacy, and cost efficiency. Suna AI seems to tick all those boxes. Imagine having an AI assistant that you can tweak, customize, and integrate deeply into your existing systems without relying on external APIs or worrying about data privacy. Plus, the video highlights real-world use cases like data analysis and research, which are exactly the kind of tasks I’m looking to automate and improve.

    For me, the biggest draw is the control and flexibility. I’m tired of being locked into proprietary platforms with limited customization options. The idea of having a fully local, open-source AI agent that I can mold to my specific needs is incredibly appealing. Experimenting with Suna could lead to creating custom tools for code generation, automated testing, or even client communication. It’s definitely worth checking out and seeing how it can fit into my AI-enhanced development workflow.

  • NEW! OpenAI’s GPT Image API Just Replaced Your Design Team (n8n)



    Date: 04/30/2025

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    Okay, this video is seriously inspiring for anyone diving into AI-powered development! It’s all about automating the creation of social media infographics using OpenAI’s new image model, news scraping, and n8n. The workflow they build takes real-time news, generates engaging posts and visuals, and even includes a human-in-the-loop approval process via Slack before publishing to Twitter and LinkedIn. I think this is really cool.

    Why is this valuable? Well, we’re talking about automating content creation end-to-end! As someone who’s been spending time figuring out how to use LLMs to streamline my workflows, this hits all the right notes. Imagine automatically turning blog posts into visual assets, crafting unique images for each article, and keeping your social media feeds constantly updated with zero manual effort – that’s the time savings we need and that translates into direct business value.

    The cool part is the integration with tools like Slack for approval, plus the ability to embed these AI-generated infographics into blog posts. This moves beyond basic automation and shows how to orchestrate complex, AI-driven content pipelines. I think it’s worth experimenting with because it showcases a tangible, real-world application of AI. It also presents a solid framework for building similar automations tailored to different content types or platforms. I can envision using this approach to generate marketing materials or even internal documentation for my projects, further decreasing time spent on manual tasks.

  • Two NEW n8n RAG Strategies (Anthropic’s Contextual Retrieval & Late Chunking)



    Date: 04/29/2025

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    Okay, this video is gold for anyone, like me, diving deep into AI-powered workflows! Basically, it tackles a huge pain point in RAG (Retrieval-Augmented Generation) systems: the “Lost Context Problem.” We’ve all been there, right? You ask your LLM a question, it pulls up relevant-ish chunks, but the answer is still inaccurate or just plain hallucinated. This video explains why that happens and, more importantly, offers two killer strategies to fix it: Late Chunking and Contextual Retrieval.

    Why is this video so relevant for us right now? Because it moves beyond basic RAG implementations. It directly addresses the limitations of naive chunking methods. The video introduces using long-context embedding models (Jina AI) and LLMs (Gemini 1.5 Flash) to maintain and enrich context before and during retrieval. Imagine being able to feed your LLM more comprehensive and relevant information, drastically reducing inaccuracies and hallucinations. The presenter implements both techniques step-by-step in N8N, which is fantastic because it gives you a practical, no-code (or low-code!) way to experiment.

    Think about the possibilities: better chatbot accuracy, more reliable document summarization, improved knowledge base retrieval… all by implementing these context-aware RAG techniques. I’m especially excited about the Contextual Retrieval approach, leveraging LLMs to add descriptive context before embedding. It’s a clever way to use AI to enhance AI. I’m planning to try it out in one of my client’s projects to make our support bot more robust. Definitely worth the time to experiment with these workflows.

  • Introducing the GitHub MCP Server: AI interaction protocol | GitHub Checkout



    Date: 04/28/2025

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    Okay, so this GitHub Checkout video about the MCP (Machine Communication Protocol) Server is exactly the kind of thing that gets me excited about the future of coding. Basically, it’s about creating a standard way for AI assistants to deeply understand and interact with your GitHub projects – code, issues, even your development workflow. Think about it: instead of clunky integrations, you’d have AI tools that natively speak “GitHub,” leading to smarter code suggestions, automated issue triage, and maybe even AI-driven pull request reviews.

    For someone like me who’s actively shifting towards AI-enhanced development, this is huge. Right now, integrating AI tools can feel like hacking solutions together, often requiring a lot of custom scripting and API wrangling. A unified protocol like MCP promises to streamline that process, allowing us to focus on the actual problem-solving instead of the plumbing. Imagine automating tedious tasks like code documentation or security vulnerability checks directly within your GitHub workflow, or having an AI intelligently guide new team members through a complex project.

    Honestly, this feels like a foundational piece for the next generation of AI-powered development. I’m planning to dive into the MCP Server, experiment with building some custom integrations, and see how it can be applied to automate parts of our CI/CD pipeline. It’s open source, which is awesome, and the potential for truly intelligent AI-assisted coding is just too compelling to ignore.

  • Did Docker’s Model Runner Just DESTROY Ollama?



    Date: 04/28/2025

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    Okay, this video is seriously worth a look if you’re like me and trying to weave AI deeper into your development workflow. It basically pits Docker against Ollama for running local LLMs, and the results are pretty interesting. They demo a Node app hitting a local LLM (Smollm2, specifically) running inside a Docker container and show off Docker’s new AI features like the Gordon AI agent.

    What’s super relevant is the Gordon AI agent’s MCP (Multi-Container Placement) support. Think about it: deploying and managing complex AI services that need multiple containers (like microservices, but for AI) can be a real headache. This video shows how Docker Compose makes it relatively painless to spin up MCP servers, something that could simplify a lot of the AI-powered features we’re trying to bake into our applications.

    Honestly, I’m digging the idea of using Docker to manage my local AI models. Containerizing everything just makes sense for consistency and portability. It’s a compelling alternative to Ollama, especially if you’re already heavily invested in the Docker ecosystem. I’m definitely going to play around with the Docker Model Runner and Gordon to see if it streamlines my local LLM experiments and how well it plays with my existing Laravel projects. The ability to version control and easily share these AI-powered environments with the team is a HUGE win.

  • How Supabase Simplifies Your Database Management with Declarative Schema



    Date: 04/28/2025

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    Okay, this Supabase video on declarative schema is seriously interesting, especially for how we’re trying to integrate AI into our workflows. It tackles a common pain point: managing database schemas. Instead of scattered migration files, you get a single source of truth, a declarative schema file. Supabase then automatically updates your migration files based on this schema. Think of it as Infrastructure as Code, but for your database – makes versioning, understanding, and, crucially, feeding schemas into LLMs way easier.

    Why is this valuable? Well, imagine using an LLM to generate complex queries or even suggest schema optimizations. Having a single, well-defined schema file makes that process infinitely smoother. Plus, the video shows how it handles views, functions, and Row Level Security (RLS) – all essential for real-world applications. We could potentially automate a lot of schema-related tasks, like generating documentation or even suggesting security policies based on the schema definition.

    For me, the “single source of truth” aspect is the biggest draw. We’re moving towards using AI to assist with database management, and having a clean, declarative schema is the foundation for that. I’m definitely going to experiment with this, especially on projects where we’re leveraging LLMs for data analysis or AI-powered features. It’s worth it just to streamline schema management, but the potential for AI integration is what makes it truly exciting.