A laid-off geophysicist turned his job hunt into an agentic workflow and open-sourced it. The project, ai-job-search, has blown past 29,000 stars on GitHub — and it's a blueprint for how AI turns a messy, subjective process into an executable pipeline.
▸ From 69 applications to an offer (source: @aigc1024). Mads Lorentzen, a geophysicist who was laid off, used Claude Code to build a job-search framework that automated his entire funnel: 69 applications → 20 first-round interviews → a job offer, and he landed an AI engineering role. The full funnel data is shared publicly on LinkedIn.
▸ The whole job-search supply chain, automated (source: @aigc1024). Where most people use ChatGPT as a copy-paste resume polisher, ai-job-search rebuilds the pipeline end to end: job discovery, match scoring (skills, experience, culture, location, career growth), tailored resume and cover letter drafting, interview prep, and application tracking.
▸ Dual-agent review is the core design (source: @aigc1024). A Drafter agent generates a tailored resume and cover letter from your profile; a Reviewer agent — a second Claude agent started with fresh context — independently researches the target company and critiques the draft from a third-party angle. That directly attacks the quality instability of single-pass generation.
▸ Four commands cover the full loop (source: @aigc1024). /setup
builds your career profile (from a documents folder, a single resume, or a guided interview); /scrape
searches and ranks jobs (built-in Danish portals, plus /add-portal
to extend to any region); /apply
generates materials — LaTeX-compiled to an exactly-2-page resume and 1-page cover letter, with ATS verification on the PDF text layer; /interview
produces staged interview prep with company research, interviewer background, STAR case mapping, and mock interviews.
▸ Local-first, privacy-safe, fork-and-own (source: @aigc1024). All data stays on your machine — no SaaS, no privacy leaks. Fork it and change anything.
▸ Bonus: ColaMD 2.0.0, a markdown editor shipped by a self-taught non-programmer (source: @aigc1024). The author credits AI coding tools — specifically the ox and terra models — for pushing each version forward, including a notoriously harder iOS build. Now at 1,000+ stars and 1,500+ installs, with Mermaid support, multi-window, custom fonts, and auto-save.
The pattern is the point: complex, subjective workflows become repeatable agent pipelines — and the people shipping those pipelines are the ones getting hired.