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Kudory – Tailor your CV for each job application

Hacker News

Kudory – Tailor your CV for each job application

I've never liked maintaining multiple versions of my CV. Every application seems to want the same experience presented slightly differently, so I kept rewriting the same bullet points and ending up with files like `cv-final.pdf`, `cv-final-final.pdf`, and `cv-final-final-v2.pdf`. I built Kudory to make that part less tedious. You upload your CV, paste a job description, and it tailors your CV to the role by rewriting and reordering your existing content. One constraint I cared about from the beginning is that it doesn't invent experience, job titles, or achievements—it only works with what's already there. It also generates a cover letter draft, estimates how well your CV matches the role, and lets you export the result using one of several templates, but the core idea is simply making it easier to tailor a CV for each application. It's still a work in progress, and I'd really appreciate any feedback—whether it's about the product, the UX, or things that simply don't work. I'll be around to answer questions.

Visit productView on Hacker NewsJul 29, 2026
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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
67%67% 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.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · 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: exist, existing, ide · Missing: https docs, excited, just released
39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: using · Missing: mac, agents, macos
33%33% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% 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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