Contexter
One context for every AI — and everyone you work with
What it does
Collect thoughts and materials from Telegram, group chats, ChatGPT, and Claude into a single context. Share it, build on it together, and work with it using your preferred AI. Architecture notes, backlogs, work logs, raw ideas, tickets, or a travel itinerary—the format doesn’t matter; the accumulated meaning does. Ask an LLM, and it turns that context into a summary, a decision history, or next steps. Now early access.
Save what you worked out with one AI into a shared context, continue it in another, and share it with people in Telegram — they keep working on it in their own AI.
Save what you worked out with one AI, continue it in another — and share the context with people in Telegram, who keep working on it in their own AI. If the link does not open, find the bot in Telegram: @ai_contexter_bot — or open it in the app . A context belongs to you, not to the assistant. ChatGPT, Claude and Telegram are doors into the same one — no copy per tool, no re-explaining the project. Same records, same people, whichever door you came through. Not a copy per door. Day 1 the Tre Cime di Lavaredo, Day 2 the Alpe di Siusi. Hut-to-hut, the van leaves at 06:30… Alex — the tent, Maria — the first-aid kit. Dates Sep 14–15, transfer at 06:30 from the square… Two builders price by the…from contexterai.com
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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
- SA
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