Search HN Jobs bookmarklet

Hacker News

When you run it on a "who's hiring?" post, it creates a little search box in the bottom right of the screen. You can type any regex into the box (it's searched globally with the ignorecase option) and hit enter. It will then filter the elements on the page and tell you how many it matched. There's also a reset to get you back to where you were. Let me know what you think! You can make a bookmarklet by pasting the code from this link into the address section: https://gist.githubusercontent.com/dackerman/158e141dd2cc7cf643951b848c402ca0/raw/e4fa1c435c4dc4ab65e107faf59e469fe5e96d48/hn-job-search-bookmark.js The unminified code is in a gist here: https://gist.github.com/dackerman/158e141dd2cc7cf643951b848c402ca0

Visit product View on Hacker NewsJul 1, 2016

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Actual performance

1points

1comments

Did not reach leaderboard

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

53%53% 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

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

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

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

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

45%45% 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

36%36% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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