Troopr AI Scrum Master
Writes your standup from real work. Remembers your team.
What it does
Troopr joins your standup, writes each person's update from real Jira, GitHub, and Slack activity, and flags what doesn't add up. It builds a memory of how your team works, so every standup gets more accurate.
Troopr Check-ins runs standups, retros, and planning poker in Slack with live Jira context and AI summaries of blockers. Free for 10 users.
Troopr drafts each person's update from their real Jira, GitHub, and Slack activity, sits in your live standup, posts the team report, and turns what was discussed into Jira updates. The daily ritual mostly disappears. Retros, planning poker, and team mood run the same way. Built from their real activity. No reply in time and Troopr posts it for them, so the standup never stalls on one person. Summary up top, each person's detail in the thread. Blockers and the critical path called out. Built from their real activity. No reply in time and Troopr posts it for them, so the standup never stalls on one person. Summary up top, each person's detail in the thread. Blockers and the critical path…from troopr.ai
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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.
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