Take the monotony of job applications away from you

Hacker News

I built this app out of frustration with the scattered, often clunky tools available for managing a job search. The goal is simple: to help you stand out in your job applications while automating the tedious but necessary parts of the process. The app takes care of everything you need for your job search and is packed with powerful AI features. When you start using the app: Firstly, it analyzes your resume and provides actionable suggestions for improvement. It also checks your keyword usage and helps optimize it to pass Applicant Tracking Systems (ATS). Writing compelling resume content can be challenging, so the app includes an input enhancer—just type what you want to refine, and it will rewrite it in a polished, professional way. You can also track your job applications while leveraging AI-driven insights. With just one click, you can research potential employers instead of wasting time searching the web. The app also offers a job fit analysis based on your profile and the job description, generates personalized resumes and cover letters, and even prepares interview materials and tips to help you stand out. It’s like having a personal career coach in your pocket. it’s completely free! Try it out at https://app.eloovor.com .

Visit product View on Hacker NewsFeb 13, 2025

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

92%92% 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.

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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: using · Missing: mac, agents, macos

42%42% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

38%38% 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

26%26% 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

10%10% 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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