HiredToday.app – AI resume tailoring, cover letters and interview prep

Hacker News

Hey HN, I'm Oskar, a long time lurker on here. I've finally decided to build and launch my first product publicly. I've built side projects in the past but never followed through on releasing them. I built HiredToday because I was frustrated with how tedious it was to tailor my CV and cover letter for every company I wanted to apply to. This led to me not bothering to tailor it and just submit a generic one. Of course that lowers your chances substantially because you're not lifting the specific qualities that matter for the position you're applying for. HiredToday helps you do that efficiently, using AI to tailor your resume, cover letter, etc based on the job description. It even creates pre-written emails for follow-ups and salary negotiation. Of course, the basics such as ATS Score analysis and optimization is built right in. I sincerely hope that this tool will be helpful in your or someone else's job search journey. I'm super open to early feedback from you guys on what you think and if you have any things you would like to see added, I'm more than happy to add them. There's currently a promo going for the launch: Everyone gets one free analysis and after that it's $10 for 30 application packages or $29 per year depending on how much usage you want. The $29 per year gets you 500 application packages. Check it out here: https://www.hiredtoday.app Let me know what you think!

Visit product View on Hacker NewsMar 24, 2026

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Actual performance

3points

Did not reach leaderboard

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

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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

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

23%23% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

18%18% 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.

Incorrect prediction on native model

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