CodeFoundry
Ship faster. Build smarter.
What it does
Launching CodeFoundry AI — an agentic RAG platform that turns production-ready code snippets into a searchable, copy-paste developer vault. Instantly retrieve existing snippets or generate custom ones using AI, powered by hybrid semantic search and a self-reflection loop to reduce hallucinations. Ship faster. Write less boilerplate. Build smarter.
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Huzzah – a novel approach to coding with AI17d ago · danielvaughn.dev · ▲384Hello everyone. I've been working on this experimental editor called Huzzah. I've been working almost exclusively with coding agents since January of this year, and over the past few months I began to feel utterly exhausted by them. They're great, but I'm finding it more and more tedious to write full sentences for every change I want. Not only that, but it seems there's a complexity limit for codebases - beyond a certain point the agent begins confusing itself. I'd like to go back to writing code, but I don't want to go all the way back to fully manual coding. So I've come up with this…
- SCSemble – Code search for agents that uses 98% fewer tokens than grepMay 2026 · github.com · ▲445
Hey HN! We (Stephan and Thomas) recently open-sourced Semble. We kept running into the same problem while using Claude Code on large codebases: when the agent can't find something directly, it falls back to grep, reading full files or launching subagents. This uses a lot of tokens, and often still misses the relevant code. There are existing tools for this, but they were either too slow to index on demand, needed API keys, or had poor retrieval quality. Semble is our solution for this. It combines static Model2Vec embeddings (using our latest static model: potion-code-16M) with BM25, fused…
- WAWindsurf – Agentic IDE2024 · codeium.com · ▲101
At Codeium, we've been building AI-powered extensions for a while (we first launched our VSCode extension with autocomplete a little over 2 years ago!), but we've always thought there would come a day where we would hit the limits of what could be achieved within existing IDEs, so we decided to build our own: Windsurf (yes, it's yet another VSCode fork :) We've stuffed a lot of cool features into Windsurf—a super fast autocomplete model, an inline diff generation experience that feels truly native, but we're most proud of Cascade, which is an evolution of the sidebar chat experience that…
- CAChunkHound, a local-first tool for understanding large codebasesJan 2026 · github.com · ▲114
ChunkHound’s goal is simple: local-first codebase intelligence that helps you pull deep, core-dev-level insights on demand, generate always-up-to-date docs, and scale from small repos to enterprise monorepos — while staying free + open source and provider-agnostic (VoyageAI / OpenAI / Qwen3, Anthropic / OpenAI / Gemini / Grok, and more). I’d love your feedback — and if you have, thank you for being part of the journey!
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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
