Obsess Jobs – Apply to jobs in your sleep

Hacker News

Hello! I got tired of filling out applications so I built a bot to do it for me. I decided it would probably be useful to everyone else looking for a job in this hellish market, so I built a site that applies to jobs for you, completely automatically. It took me about three months to build and I'm pretty proud of how smoothly it works. You can: Click the "Auto Apply" button next to postings in the search results and keep scrolling. It'll get added to a queue and processed within 2-5 minutes Click the Zero-Click toggle in the top right of a search and it will automatically apply day and night to jobs that fit that criteria. I also added some other cool stuff like real time updates and the ability to see any submitted application and the exact answers that were provided so that you can monitor and self-validate. All applications are completed on the company's actual job board so your application has priority over 3rd party job aggregator sites. It doesn't go straight to the trash like LinkedIn's Easy Apply feature. Lastly, you can still apply manually if you just want to use it as a job search tool! Happy New Year!

Visit product View on Hacker NewsDec 31, 2024

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

85%85% 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 HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos

62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

62%62% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

45%45% 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

22%22% 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.

Incorrect prediction on native model

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