OnJob.io
Your AI-powered career, from profile to offer
What it does
OnJob.io is an AI-powered career intelligence platform that helps job seekers stop applying blindly and start getting hired. Build an ATS-optimized profile, discover jobs that match your skills, get AI-powered match scores, track recruiter interest, understand rejection reasons, prepare for interviews, and access salary insights—all in one place. Apply smarter, improve faster, and take control of your job search.
See who viewed your profile, learn why recruiters pass, fix it with AI, and apply to 20,000+ AI-matched jobs in one click.
Applying blind is what makes a job search feel endless: you send the same resume everywhere and never learn why it failed. OnJob.io shows you who opened your profile, what recruiters screen for and where your resume falls short, then matches you to 20,000+ live roles and lets you apply in one click. Free to start, no card. OnJob.io helps candidates get hired faster by removing the guesswork from the job search. It AI-matches you to 20,000+ live jobs so you only apply where you fit, shows you in real time which recruiters viewed your profile and whether they liked or skipped you, and uses AI to diagnose exactly why you were passed over — then fixes it. Candidates report 3.1× more interview…from onjob.io
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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.
AI · 16d ago · simedw.com