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QuickCV – Free ATS Resume Checker

Hacker News

QuickCV – Free ATS Resume Checker

Hi HN, I built a free tool to check how "ATS-friendly" a resume is. I noticed that most resume checkers force you to create an account before showing you the results, or they just grep for keywords. I wanted to build something that actually parses the PDF structure similar to how a real Applicant Tracking System does, but without the "signup wall" friction. The stack is Next.js + `unpdf` for parsing + an LLM layer to analyze the extracted entities (skills, experience, education) against standard job descripti patterns. How it works: 1. It validates the PDF (magic bytes check). 2. Uses `unpdf` to extract raw text and layout data. 3. Analyzes content for common ATS "trip-ups" (tables, complex columns, missing standard headers). 4. Gives a score based on readability and keyword density. 5. It's completely free and requires no email/signup. I'm currently tweaking the scoring algorithm, so I'd love to hear if the score feels "fair" for your resume. Link: https://quickcv.io/resume-checker Thanks!

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3points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntUnlikely to reach the leaderboard · Strong signals: email · Missing: mac, agents, macos
49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: friendly · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: education · Missing: mobile apps, ios, personal
26%26% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
25%25% 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
11%11% 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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