Lydo
Multiplayer AI Teamspace with Context Brain for Agents
What it does
Lydo is a multiplayer AI teamspace with a shared brain your AI agents work from. Your team's context and decisions live in one Space that Claude, GPT, Gemini, and your own agents all read over MCP. Teach it once, every agent gets smarter, and every answer shows its sources. It's also your team's native workspace: chat, boards, docs, and calls. Tag Claude, GPT, or your own agent inline in a doc and it writes next to your teammates, live. Now on Web IOS/Android devices and Ipad/Tablets
Lydo is a multi-agent, multi-provider AI workspace where people and AI teammates work from one shared Space, with context, sources, notes, and approvals built in. Arc 30 is in beta.
Home gives you one place to see what needs to move. Your people and AI teammates work from the same context, then carry the work into a Space when it is time to bring everyone in. Open a piece of work and the right context is already there. Ask a question, add a teammate or agent, review what changed, and keep moving from the same thread. Let’s lock the launch story today. @Researcher validate the positioning, @Designer finish the campaign set, and @Finance hold spend until the final review. The market scan is complete. The strongest message is: one place where people and every kind of AI teammate work together without model lock or seat pricing. I opened a launch review Huddle and pulled…from lydo.chat
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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.
AI · 16d ago · simedw.com