Firebase-style backend for AI-native apps. One API for chat, vectors, data, files & real-time streams. Ship prototypes or scale production-ready LLM features without managing infra. Quipubase lets you focus on building, not backend.

memories.sh — One layer for memories, skills, and rules across any agent

One layer for memories, skills, and rules across any agent

I’m Oscar, and I’m excited to share Quipubase — a Firebase-style backend designed for building AI-native apps. As devs building with LLMs, we kept running into the same pain: • Too many APIs • Too much boilerplate • Slow to prototype • Hard to scale So we built Quipubase — an API that merges LLM chat, embeddings, vectors, collections (like Firestore), files and real-time streams. ⚡️ What you can build with it: • Chatbots (with memory!) • Embedding search • Live document Q&A • Audio transcribers • Anything that talks to OpenAI-like APIs 🎯 Try the playground: https://quipubase.oscarbahamonde... We’d love your feedback. AMA or roast me below. 🧠🔥 — Oscar

This is truely awesome! Merging all those different APIs (LLM chat, embeddings, etc.) into one is such a smart move. Seriously cuts down on the boilerplate, right? Makes prototyping so much faster. How's the pricing structured?

memories.sh — One layer for memories, skills, and rules across any agent

One layer for memories, skills, and rules across any agent

Quipubase

Let your data talk. Quipubase handles the rest.

Let your data talk. Quipubase handles the rest.