Growfile.app
Build your profile once. Get tailored CVs for every job.
What it does
Most resume tools make you start from scratch every time. Growfile works differently. You build your professional profile once: experience, skills, education. Then, for each job you apply to, Growfile analyzes the job description and generates a tailored, ATS-optimized CV and cover letter that surfaces what actually matters for that specific role. No more copy-pasting old files. No more generic cover letters. Currently in early access. We'd love your feedback.
Generate ATS-optimized resumes and cover letters with AI. Import your CV, build your professional profile, and land more interviews. Professional career tool.
Centralize your professional info, get AI resume optimization, and check every application with our ATS resume checker. Growfile is the career copilot you need to stand out in today's job market. ATS systems automatically filter out three out of four resumes before a human ever sees them. Our CV checker analyzes yours and tells you exactly what to fix to beat the bots. AI-powered professional tools — including an ATS resume checker — that do the heavy lifting while you focus on what matters. All your professional info in one place. Import your CV, manage your profile, and keep everything synced effortlessly. Our AI analyzes job descriptions and tailors your CV with the exact keywords ATS…from growfile.app
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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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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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