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Huntr – GPT resume builder to semantically match your resume to a job

Hacker News

Huntr – GPT resume builder to semantically match your resume to a job

This week we launched an AI powered resume builder for Huntr. For the past 5 years we’ve been focused on building a really powerful job application tracker, which over 300k job seekers have used since our initial launch. For the past 6 months we’ve been heads down working on an AI powered resume builder. We wanted to leverage our existing job tracker to help job seekers quickly tailor their resumes to individual jobs. Unlike most resume builders in the market, we look at NOT ONLY keyword coverage, but at actual responsibilities and qualifications for the job. We then calculate how well those match your resume and offer AI powered suggestions for improved ‘coverage’. With typical resume builders, you can get a really high job match score by just stuffing a bunch of keywords into the resume. While this might get the resume through the initial ATS screen, it will not get you past the first set of human screenings. Our semantic matching, in addition to our keyword matching, increase the probability that a resume with a high match score will get through both ATS and human screen and eventually to first round interviews. We also launched an application autofill tool into our Chrome Extension and an AI powered cover letter writer. This has allowed us to create a streamlined experience that speeds up every step of the application process: from initial job discovery, to building resume/cover-letters and finally filling our the application form itself. Our goal is take all of the drudgery of the job search off-of job seekers’ plates (customizing resumes, filling application forms with the same info over and over again, tracking applications, etc), so they can focus on the truly important parts like defining career values/goals, networking, skill development and interview prep.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
90%90% 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.
TrustMRRFits verified-revenue profile · Strong signals: month · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: builder · Missing: plus, platform, intuitive
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, io · Missing: https docs, excited, just released
31%31% 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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