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I made an app which simplifies your remote job hunt

Hacker News

I made an app which simplifies your remote job hunt

Hi HN, Daily I curate 500+ jobs from top quality remote job boards. You can browse and filter these jobs based on the below criteria's job boards job category whether it worldwide or remote us or any other timezone. tech stack viewed jobs you can refine your search results by choosing right combination of filters. once you find the job you are interested in you can shortlist it. once you applied the for the shortlisted job you track you job application progress using the Kanban board. This is useful for future interviews at the same companies During the application process or during the interview if you receive any feedback or any thing that you want to capture you can do that with notes, tasks. You can upload resume's and share with recruiters and you can even link the resume's that you have used for applying to a job. remotejobhuntbuddy.com For those who are looking for remote jobs it will be quite useful

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Actual performance

5points
10comments
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
75%75% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: tasks, using, notes · Missing: mac, agents, macos
34%34% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
28%28% 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
19%19% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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