Dropstone
The AI runtime that remembers, learns, and acts everywhere
What it does
Dropstone is a runtime for intelligence, one system that can evolve from an AI assistant into an always-on digital workforce.
Dropstone is an AI agent you run on your own infrastructure. It ships real code and your code never leaves your walls. The same agent generalizes to Real-World AI that runs your home and makes the call.
I remember what you taught me, and I learn the skills I don't have yet. I place the calls, watch the inbox around the clock, keep an eye on the house, and ship the code. That's just what I've picked up so far. Running on models we host ourselves, on infrastructure you can run inside your own walls. Built by an AI research lab advancing every domain of science. I started in your terminal as a coding agent. Then I learned the phone, the inbox, the house, and the rest. One memory, every surface. Teach me once and it holds, from the CLI to chat to the SDK to the phone call I place on your behalf. The difference isn't how clever it is. It's who moves first. The coding agents write excellent…from dropstone.io
Pricing, as stated on its site
Free tier, paid plans from $15/mo — Dropstone offers a freemium model with a free tier providing limited monthly usage, and paid tiers starting at $15/month (Pro) with 6x more usage, up to Max plans at $75+/month with 5-10x more usage t
checked September 2026 · prices change
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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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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 · 16d ago · simedw.com