Yet another whoishiring post aggregator

Hacker News

This website indexes the whoishiring posts and allows the user to sort posts according to their criteria. Use predefined keywords or write your own. Posts are sorted and not filtered out. All three categories are indexed so you can search for a job, search for help or check out the competition! I started learning web recently (because apparently ML/AI jobs are rare these days). Reading tutorials is nice, but building your own product, even better! The project is “live”, that is, it constantly scans the news.ycombinator.com for new posts. Other projects that does roughly the same: https://grepwhoishiring.com/ https://hnhired.fly.dev/ https://kennytilton.github.io/whoishiring/ https://hnjobs.emilburzo.com/ https://hnhiring.com/ Happy job/candidate hunting!

Visit product View on Hacker NewsDec 27, 2022

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · 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 · Strong signals: user, new · Missing: mac, agents, macos

38%38% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

13%13% 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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