Talentz
The signal layer for hiring.
What it does
Zee is the agentic recruiting platform behind Talentz.ai. Three connected agents cover your full pre-hire loop. Zee Scout finds candidates by proof of work, searching where talent actually lives. Zee Engage runs two-way conversations across every channel on one thread. Zee Lens conducts structured AI video interviews and delivers decision-ready findings. Your hiring manager makes the call, backed by evidence instead of a resume.
Transform your hiring process with AI-powered talent acquisition. Zee agents find, engage, and interview every candidate so you hire on evidence, not surface.
Zee agents find, engage, and interview every candidate. You make the call on evidence. Transform your hiring process with AI-powered talent acquisition. Zee agents find, engage, and interview every candidate so you hire on evidence, not surface. AI-driven hiring platform where Zee agents find, engage, and interview every candidate before you decide. Talentz.ai is the signal layer for hiring. Zee Scout surfaces candidates by proof of work, Zee Engage runs multi-channel conversations, and Zee Lens conducts structured interviews. Zee agents find, engage, and evaluate every candidate. You make the call on evidence. It was built for a lower-volume world. AI changed the market faster than hiring…from talentz.ai
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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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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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