15:I[981,["658","static/chunks/658-a698693f56d0116a.js","183","static/chunks/183-6beeeb801c274aaa.js","474","static/chunks/app/%5Btype%5D/layout-631c4730b83c8223.js"],"Posts"] 16:T50b,Most resumés are rejected by AI before a human ever sees them. Applicant tracking systems screen for keywords, structure, and relevance long before a recruiter looks at anything.

I built a small app that uses AI to operate on the other side of that filter.

You upload your experience once. Then, for any job posting, the agent parses the role, analyzes requirements, and generates a fresh, ATS friendly resumé tailored specifically to that job. Bullet points are rewritten to match the posting’s language, relevant experience is prioritized, and the output is a clean PDF in about a minute.

Under the hood, this is powered by Subconscious, which handles the agent reasoning, job parsing, and structured resumé generation. The agent also does light company research and keeps your experience stored as structured data so it improves over time instead of creating duplicate documents.

The goal was to reduce the manual, repetitive work of rewriting resumés while acknowledging the reality that software is doing most of the screening.

Demo: https://resume-tailoring-agent.subconscious.dev/

Happy to answer questions about the approach, tradeoffs, or the agent design.17:T70d,Hey HN,

We explored how decision-making happens under severe information asymmetry and used career exploration as a test subject. In practice, people converge on a small set of highly legible default paths, with little visibility into credible alternatives.

That’s why we’re building Fig, a tool for reasoning about career paths when the next step isn’t obvious, given the dependence on fleeting personal preferences.

Most existing tools respond by collapsing uncertainty into a single recommendation or by modeling careers as linear trajectories. That

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