Op

Open-source resume builder and parser

Hacker News

Open-source resume builder and parser

Hi HN, I recently created and published an open-source resume builder as a weekend project. The idea came to me while I was mentoring students and noticing common mistakes they made in their resumes that I had also made in the past. I thought to build a tool to help people easily create a modern professional resume with built-in best practices to avoid those mistakes. Top highlights of the resume builder are: 1. Real time UI update as you type 2. ATS friendly to top ATS platforms, e.g. Greenhouse, Lever 3. Privacy focus - no sign up is required and data is stored locally in browser that only users have access 4. Support import from existing resume PDF The tool also includes a resume parser to help people test their existing resumes’ ATS readability if they might not be interested in using the builder. I also explained the parser algorithm in an article with interactive tables that might be an interesting read to see the steps and logics it uses ( https://www.open-resume.com/resume-parser ). I hope others might find this tool useful and I look forward to hearing any feedback the community has. Thanks all. Home Page: https://open-resume.com Github Repo: https://github.com/xitanggg/open-resume Product Hunt: https://producthunt.com/posts/openresume

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Actual performance

656points
198comments
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Indie HackersFits the IH revenue-focused audience · Strong signals: created, mistakes · Missing: supports, reddit linkedin, podcasting
79%79% 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.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
65%65% 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 HuntOn track for Day 1 leaderboard · Strong signals: user, using, open · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, friendly, builder · Missing: plus, intuitive, reviews
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real time · 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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