Octomind Cloud and Hub
One login, zero API keys — cloud agents + 27 models
What it does
Run AI agents in the cloud — pick a machine, tell it what to do, close your laptop. Sessions resume from any device. Per-second billing, 21 models built in, no API keys. Solved 24/25 benchmark tasks — ahead of Claude Code and Codex.
Pick an AI agent — blog writer, researcher, SEO auditor, developer — and it works on its own Linux machine in the cloud. Free tier, no card.
A researcher, a blog writer, an SEO auditor, a data analyst — each one gets a real Linux machine in the cloud. It browses the web, writes and runs code, installs whatever it needs and keeps your files. Ask in plain language, close the laptop, come back to finished work. No card, no invite. Log in with an email code and your machine is running in under a minute — the hub and the CLI come with the same account. Every one runs on its own machine, with the web, a shell and your files. Not sure which? Assistant is the default and routes for you. Three surfaces, on a machine that is yours. Everything else on this page follows from them. It opens the live web and reads it — today's prices, today's…from octomind.run
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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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Life & fun · 10d ago · louisabraham.github.io
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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.
AI · 17d ago · simedw.com