Find Your Next Job Quickly Using This Chrome Extension – HN Hiring Pro

Hacker News

Hi Everyone, I created this extension to solve my own problem of applying to jobs via Hacker News. Searching through hundreds of comments manually and using browser's built-in search is time-consuming and inefficient. This extension helps streamline the job search process on HN's "Who's Hiring?" threads by providing custom filtering, highlighting, and tracking capabilities. Key problems it solves: - Efficiently filter job posts by keywords, location, and requirements - Highlight and track interesting opportunities - Manage your application process with built-in status tracking - Save time by hiding posts that don't match your criteria Here is a demo, https://www.youtube.com/watch?v=nwz88GyAAXY Get the Chrome Extension Here : https://chromewebstore.google.com/detail/hn-hiring-pro/mpfal...

Visit product View on Hacker NewsNov 11, 2024

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Did not reach leaderboard

Launch Intel predictions

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

77%77% 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.

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

55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

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

41%41% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

29%29% 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

16%16% 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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