Date: 05/02/2025
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.