Tag: supabase

Date: 06/04/2025

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Okay, this Supabase MCP Server video is seriously cool, and here’s why I think it’s worth your time. It shows how to give your AI agent deep context about your Supabase project, essentially letting it “understand” your backend in the same way it groks file structures and code. Jon Meyers walks through setting up the Supabase MCP server within Cursor IDE and then uses Claude to whip up an Edge Function that intelligently scrapes recipe websites. Forget the ad-ridden, SEO-spam versions – this pulls out just the core recipe data and stores it in a Postgres database, then displays it in a Next.js app.

The real value for us, as developers moving towards AI-assisted workflows, is how it streamlines development and automation. Imagine the possibilities! Instead of manually writing complex scrapers and data cleaning scripts, you can leverage AI to handle that heavy lifting. I’ve spent countless hours wrestling with web scraping in the past (and honestly, who hasn’t?), so seeing this level of automation makes me genuinely excited. This isn’t just about scraping recipes; it’s about connecting AI to your database schema, table relationships, and even your custom functions, allowing it to assist in tasks you hadn’t even imagined.

I’m already brainstorming ways to apply this to our internal tools and client projects. Think automated data migrations, intelligent report generation, or even AI-powered API development. This video gives a practical, hands-on example of how to bridge the gap between LLMs and real-world development tasks. The combination of Supabase’s backend capabilities and AI coding tools like Claude could seriously boost productivity and unlock new levels of automation, it’s definitely worth experimenting with.

Date: 06/02/2025

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Okay, this video on Supabase Edge Functions on Fly.io is gold for any of us transitioning to AI-driven workflows. Jon Meyers walks through deploying a Supabase Edge Function, essentially a serverless function, on Fly.io using Deno and Oak middleware. This means we can ditch some of the heavier backend lifting and focus on orchestrating logic with tools like LLMs.

Why’s it valuable? Because it showcases how to self-host these functions, giving us control and flexibility. Instead of being tied to a specific cloud provider’s serverless platform, we can deploy these lightweight functions anywhere, including environments where we’re integrating AI agents or no-code solutions. Imagine using an LLM to generate the core logic within the Edge Function, then deploying it to a cost-effective and scalable platform like Fly.io, orchestrated entirely by AI. We can have AI write the function, write the tests, and orchestrate the deployment!

The real-world application is huge. Think automated content generation, dynamic API endpoints, or even real-time data transformation triggered by AI models. By experimenting with this, we’re not just learning about Supabase or Fly.io; we’re building a foundation for a whole new level of automation and intelligent applications. It’s definitely worth carving out an hour to play with!

Date: 06/02/2025

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Okay, this video on self-hosting Supabase with S3 storage, custom domains, and Authelia is exactly the kind of thing I’m diving into! It’s a walkthrough of setting up a complete backend infrastructure, and what’s killer is the focus on self-hosting. We’re talking full control, reducing reliance on external services, and potentially big cost savings down the road. It’s not just about slapping together a quick prototype; it’s about building a robust, production-ready environment.

What makes this video inspiring is that it bridges the gap between traditional backend setups and the newer, “serverless” world that Supabase offers. The inclusion of Authelia for authentication shows a real-world security mindset. We, as devs, can leverage the techniques shown here to move away from the complexity of frameworks such as Laravel and use Supabase as BaaS and build an entire scalable app using Vue, React or Svelte (my favourite). The video even acknowledges some initial hiccups (which the author immediately fixed), it adds a layer of authenticity.

I’m already thinking about how I can use this setup for a client project where data sovereignty and control are paramount. Instead of relying on a managed Supabase instance, I can deploy this on a Hetzner or DigitalOcean server, giving the client complete ownership of their data. This video is a must-watch for any developer looking to level up their backend game and explore the power of self-hosted solutions, and the mentioned hiccups only add credibility. I’m going to experiment with this over the weekend.

Date: 05/12/2025

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Okay, so this Supabase video is a game-changer for anyone diving into serverless functions. It basically shows you how to create, test, and even edit with AI your Supabase Edge Functions, all directly from their dashboard. No more complex CLI setups or wrestling with configurations – it’s all visual and streamlined. As someone who’s been trying to blend traditional PHP/Laravel with AI-assisted development, this hits the sweet spot.

Why’s it valuable? Because it drastically lowers the barrier to entry for using Edge Functions. Think about it: you could use this for things like image optimization on upload, real-time data transformations, or even custom authentication logic – all triggered at the edge, closer to the user. The AI editing feature is what really caught my eye. Imagine describing what you want the function to do, and the AI generates the code, then you fine tune from there. It can be like pair programming with an AI assistant.

For me, this is worth experimenting with because it aligns perfectly with automating repetitive tasks and boosting productivity. We can focus more on the business logic and less on the infrastructure plumbing. Plus, the fact that it’s all within the Supabase ecosystem makes it even more appealing. It makes me wonder how many custom PHP scripts I have running that could be streamlined using serverless functions edited by an AI, it would be a significant improvement.

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.

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.

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!

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.

Date: 04/16/2025

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Okay, so this Supabase Local Dev video is seriously inspiring, especially if you’re like me and diving headfirst into AI-assisted workflows. It’s all about streamlining your database development process with migrations, branching, and observability – basically, making your local development environment a carbon copy of your production setup, but without the risk of, you know, accidentally nuking live data.

Why’s it valuable? Because it tackles a huge pain point: database schema and data management. Imagine using AI to generate code for new features. Now, picture having an isolated, up-to-date database branch to test that code without the constant fear of breaking things in production. The video walks through cloning your production database structure and even seeding it with data locally. Think about the possibilities: using LLMs to generate test data and then automatically migrating it across your environments! We are talking about a single click deployment process!

The real win here is database branching. It’s like Git for your database, allowing you to create ephemeral databases for each Git branch. This means you can test, experiment, and iterate with confidence, knowing that your changes are isolated. I’m already envisioning integrating this with my CI/CD pipeline, using AI to analyze database changes and automatically generate migration scripts. Trust me, give this a watch. It’s a game-changer for anyone serious about automating their development workflow and leveraging the power of AI in database management.

Date: 04/16/2025

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Okay, this Supabase workflow tutorial is exactly the kind of thing I’m geeking out about right now. It’s all about streamlining development by using the Supabase CLI for local development, pulling data from production for realistic testing, and then deploying those changes globally. Think about it: no more “works on my machine” nightmares or manual database migrations. This is about bringing a modern, automated workflow to the Supabase ecosystem, letting us focus on building awesome features instead of wrestling with environment inconsistencies.

Why is this valuable for us as we transition into AI-driven development? Well, a solid, automated development workflow is the bedrock for integrating AI-powered code generation and testing. Imagine: you make a change locally, AI-powered tests instantly validate it against production data, and then the whole thing gets deployed with minimal human intervention. That’s the dream, right? This video gives you the foundation to build that dream on.

The practical applications are huge. Think about rapidly prototyping new features, A/B testing with real user data, or quickly rolling back problematic deployments. This is about more than just saving time; it’s about de-risking development and allowing us to be more agile. Honestly, I’m itching to try this out on my next project. The idea of a fully synced, locally testable Supabase setup is too good to pass up – it’s time to level up our dev game!