
Ix
Map your codebase. Give your AI persistent memory.
What it does
Every AI session starts from scratch. Not just losing chat history, losing the entire architecture of your system. Ix fixes that. One command maps your entire codebase into a persistent architectural graph. Your LLM stops reconstructing context and starts navigating a real map. 30% fewer tokens on average. Sometimes 80%+. Mapped Kubernetes, 20,000 files, in under 90 seconds. Open source. Free to download.
Does a similar job
all alternatives →- HWHow we leapfrogged traditional vector based RAG with a 'language map'2024 · twitter.com · ▲162
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…
Second Brain for AIMay 2026 · github.com · ▲286Persistent memory for Claude, ChatGPT & Cursor. Free.
- COCore – open source memory graph for LLMs – shareable, user owned2025 · github.com · ▲112
I keep running in the same problem of each AI app “remembers” me in its own silo. ChatGPT knows my project details, Cursor forgets them, Claude starts from zero… so I end up re-explaining myself dozens of times a day across these apps. The deeper problem 1. Not portable – context is vendor-locked; nothing travels across tools. 2. Not relational – most memory systems store only the latest fact (“sticky notes”) with no history or provenance. 3. Not yours – your AI memory is sensitive first-party data, yet you have no control over where it lives or how it’s queried. Demo video:…



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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com


Launched alongside, March 2026
the whole month →

Switch from ChatGPT to Claude with import memory feature
AI · Mar 2026 · claude.com


