skilledcoder.ai
Interview people who can actually do the job.
What it does
skilledcoder.ai is AI-native technical hiring. Candidates do real work on a real codebase with AI, and you get a ranked shortlist of the final four or five worth interviewing, each with a detailed report and proof the work is genuinely their own.
skilledcoder.ai has candidates do real work on a real codebase with AI , then shows you exactly how they think and confirms the work is genuinely their own. Every name on your shortlist comes with proof. AI changed how engineers build. It also broke the tests you use to hire them. A candidate hands the task to AI and submits clean code. You learn nothing about how they think. Puzzles far from the real job, solved by the same AI you are screening against. Every resume is AI polished. Finding real signal costs hours per candidate. Each candidate works with AI on your actual codebase, solving a task that mirrors the day to day. Not puzzles anyone can rehearse. Every prompt and every change is…from skilledcoder.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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