Nomik
Your AI agent's memory. A knowledge graph of your codebase
What it does
Stop feeding raw files to your AI agent. Nomik parses your entire codebase into a structured knowledge graph: functions, call chains, routes, DB tables, APIs, events, all connected with real relationships. It plugs into Cursor, Windsurf, and Claude via MCP so your agent queries precise context instead of guessing from file dumps. Ask "what breaks if I change this?" and get a real answer. Supports TypeScript, Python, Rust, SQL, and config files. Free, open-source, 100% local. No cloud, no lock-in
Does the same job
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- CTCodeRLM – Tree-sitter-backed code indexing for LLM agentsFeb 2026 · github.com · ▲81
I've been building a tool that changes how LLM coding agents explore codebases, and I wanted to share it along with some early observations. Typically claude code globs directories, greps for patterns, and reads files with minimal guidance. It works in kind of the same way you'd learn to navigate a city by walking every street. You'll eventually build a mental map, but claude never does - at least not any that persists across different contexts. The Recursive Language Models paper from Zhang, Kraska, and Khattab at MIT CSAIL introduced a cleaner framing. Instead of cramming everything into…
Sense, code intelligence for AI agentsJun 2026 · luuuc.github.io · ▲14Your AI has your code's text, never its map. Fix that.
- TITilth – I spent tokens so my agents would stop wasting them (~4k Rust)Feb 2026 · github.com · ▲10
I'm an "ideas person" who messes around with AI on a low budget. I got tired of watching my tokens vanish and context windows filling up while agents fumbled around trying to find the right thing. Agents don't flail like they used to with shell tools, but there are still weak/blind spots and back-and-forth episodes — especially when using tools in combination/sequence. So I built "tilth" today. Or rather, AI built it — every line is Opus 4.6. I spent a lot of my precious tokens getting it to "not shit" (at least several of the different vendors' AI overlords assure me it's not…
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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
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Switch from ChatGPT to Claude with import memory feature
AI · Mar 2026 · claude.com
