Rafter
One shared brain for your team's AI tools
What it does
Rafter is a hosted MCP server that gives your team shared memory, skills and agents. Claude, Cursor and ChatGPT start every session knowing how your team works — capture a workflow once, everyone runs it anywhere. OAuth 2.1, connects in one line.
Rafter captures how your team actually solves tasks and turns it into skills and agents anyone can run, inside the tools you already use.
Rafter learns how you work. Then lets every team member use this knowledge to accelerate their work. Illustrations, not screenshots. Interfaces shown belong to their respective owners; Rafter is independent and unaffiliated. Rafter connects over MCP, so it shows up inside the tool you already use. Two steps, and neither of them is a migration. Prefer the long form? Step-by-step guides for every tool . https://app.heyrafter.xyz/api/mcp A sign-in window opens as you add it. Works in the web and desktop apps. Turn on Developer mode under Settings → Security. ChatGPT's own Settings → Security screen. Then create a connector with this URL and OAuth authentication.…from heyrafter.xyz
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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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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