An AI agent that tailors your resumé to beat ATS filters

Hacker News

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.

Visit product View on Hacker NewsJan 30, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent · Missing: mac, agents, macos

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: friendly · Missing: plus, platform, intuitive

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

23%23% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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