OneCLI
Give every employee a secured, sandboxed pro assistant agent
What it does
The best way to get work done, instead of chatting about it and doing it yourself. Open-source, secure agents, in Slack and on the web. Self-hosted agent harness for teams: sandboxed, policy-controlled, and never touching a real credential.
The best way to get work done, instead of chatting about it and doing it yourself. Open-source, secure agents, in Slack and on the web.
The secured sandboxed agent for every employee. It never holds a password. It's also open source . It works before you ask, with your access and nobody else's. It runs on its own, and waits for your approval when your rules say so. It opens the tools, does the work , and stops on its own when a step needs a human. Each employee's agent reaches their tools through the OneCLI gateway. Scoped credentials injected per request. Agents never hold a real secret. Only enforcement outside the model guarantees that. Every OneCLI agent has it built in. Fact Meta's head of AI safety and alignment gave an autonomous agent free rein over her inbox. It started deleting emails, and kept going after being…from onecli.sh
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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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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…
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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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