Synapsis - Vectorisation as a Service

What it does

Retrieving vector embeddings from petabytes of images, videos or text is challenging to scale, while having full control of its quality and a high precision in search system. Quasara helps retrieve high-quality and high-dimensional vector embeddings.

Quasara | Our vectorisation and semantic search engine helps AI agents to access petabytes of image, video or document data with accurate vector embeddings and leads to supurb outcomes.

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  TL;DR: Vector-based RAG performs poorly for many real-world applications like codebase chats, and you should consider 'language maps'. Part of our mission at Mutable.ai is to make it much easier for developers to build and understand software. One of the natural ways to do this is to create a codebase chat, that answer questions about your repo and help you build features. It might seem simple to plug in your codebase into a state-of-the-art LLM, but LLMs have two limitations that make human-level assistance with code difficult: 1. They currently have context windows that are too small to…

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  I replaced vector databases with Git for AI memory (PoC)2025 · github.com · ▲198

  Hey HN! I built a proof-of-concept for AI memory using Git instead of vector databases. The insight: Git already solved versioned document management. Why are we building complex vector stores when we could just use markdown files with Git's built-in diff/blame/history? How it works: Memories stored as markdown files in a Git repo Each conversation = one commit git diff shows how understanding evolves over time BM25 for search (no embeddings needed) LLMs generate search queries from conversation context Example: Ask "how has my project evolved?" and it uses git diff to show actual…

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