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.
Top comments (1)
Hello Glad to see you, I am Kane Lim from Hong Kong. I have over 10 years of development experience. I am writing this because your post was interesting.
The most interesting aspect is not automating applications, but converting an inherently subjective workflow into an observable agent pipeline. The dual agent architecture is particularly valuable because independent context creates a natural adversarial evaluation layer instead of trusting a single generation.
I would push this further with a structured candidate knowledge graph. Store every skill, project, measurable outcome, technology, role requirement, and evidence source as typed entities. Matching can then use semantic retrieval plus deterministic constraints instead of relying entirely on an LLM score.
For application generation, I would add an evaluation loop where the Reviewer produces machine readable defects, the Drafter patches them, and a final verifier checks factual consistency against the source profile. That creates a closed loop with provenance rather than unconstrained rewriting.
The local first architecture is also a major advantage for sensitive career data. This is essentially an agentic CRM for personal career development, and the architecture could become considerably more powerful with longitudinal analytics.
I would enjoy exchanging ideas on the evaluation and orchestration layer.