AgentR 3.0
Hiring evaluation built for the AI cheating era
What it does
AgentR is an AI agent that runs the full hiring evaluation, judgment, real work scenarios, and verification, so every shortlist is built on evidence, not a resume and a gut feeling. With Phase 3, AgentR now conducts the entire first interview itself, structured, adaptive, and built to hold up against AI-assisted cheating, so you walk into every decision with the confidence to make the call.
AgentR reads every application, checks what candidates actually claim, and interviews the ones who clear your bar. You make every call.
You spend the week on four people, not two hundred. Nobody is rejected by a machine. Your criteria against every single application. Not keyword bingo. Actual reading, with the evidence kept. Every claim is checked against the public record and the candidate's own history. What holds up, what looks overstated, with sources. A structured interview built from the role, taken on their own schedule. Every answer is scored against a rubric written before anybody applied, with the quote attached. Top Picks, Contenders, Prospects. Every name on it has receipts you can show the hiring manager. Two layers watch an interview. One sees the room, the other sees the machine. Neither of them decides…from agentr.global
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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.
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