Auric
Stop re-explaining your project to AI
What it does
Developers and AI agents keep losing track of what’s happening and what changed, what was decided, what’s blocked, and what needs to happen next. Auric keeps everyone on the same page. So your next AI agent or teammate can pick up exactly where the last one left off without re-explaining the project from scratch. Your AI agents finally work like a team. We’re opening up the Auric beta soon
Auric gives every project its own persistent intelligence — what it is, what has happened, and where the work stands — across every agent, person and session.
Auric gives your project its own persistent intelligence — what it is, what it's trying to achieve, what changed and why, and what needs to happen next. Every agent that works on it reads that understanding, and adds to it. What you'd decided. What you were halfway through. Why the last approach failed. Every file saved. Every commit in place. Nothing that says why. The context lives in the sessions. The decisions live in the conversations. The reasoning lives in your head. Every new agent starts from none of it. Auric gives your project its own persistent intelligence — what it is, what it's trying to achieve, what changed and why, and what needs to happen next. Every agent that works on…from auric.cx
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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…
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Launched alongside, August 2026
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Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…
Life & fun · Aug 2026 · toneyalexander.github.io


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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