Oaki–job finder and resume maker

Hacker News

Hi! I built Oaki about a year ago as a side project to solve my own frustration with job applications, and it’s now helping thousands of users with their job hunt. I had quit my previous (consulting) company when I decided to step back into the job market, and I HATED applying to jobs with a passion. Finding good jobs, sifting through all the crap, etc.etc. So I built a rough MVP and posted it on Reddit and got more paid users than I ever had with any other company/startup I was in. To top that off, I found a really awesome job (and landed many more interviews) with it, so I know from first-hand experience that it works! Oaki’s 3-step flow: 1. Import or build a modern, eye-catching resume in under 2 minutes with Oaki 2. Set preferences (role, location, salary, and more) 3. Oaki finds best-fit jobs daily, generates a slightly tailored resume for each, designed to amplify each users' uniqueness On that last point, we're really big on safe AI use; that means we never use it for spam or 'spray and pray' applications. On the surface it looks pretty simple, but Oaki is powered by some really cool tech, blending ML with LLMs, orchestration, hybrid search, and much much more from finding jobs to printing high quality dynamic resumes, and even helping you apply to jobs. While the job finder itself is free (and all accounts get a free no-credit card trial), I do have to charge people for the AI-generated resumes/applications part. For anyone who needs it or knows someone, I hope it can help with the job search; it's reeeally bad right now. You can also use code `ICAMEFROMHN20` to get 20% off, or DM/email me at nour@oaki.io (I read everything). Cheers! Nour

Visit product View on Hacker NewsAug 31, 2025

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Actual performance

5points

Did not reach leaderboard

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

87%87% 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: ide, io · Missing: https docs, excited, just released

72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, email, code · Missing: mac, agents, macos

30%30% 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 · Strong signals: paid · Missing: web3, chat, crypto

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

Incorrect prediction on native model

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