Launch Intel
I

I created an easy way to find new job postings

Hacker News

I created an easy way to find new job postings

Hi everyone! During my layoff last year, I had a hard time finding jobs through LinkedIn, indeed, and other services. The listings were old and usually had hundreds of applicants. After a few months, I landed on a super simple process that I created in Notion that got me more interviews. Then, I converted it into a simple website. I hope it's a tool you can use every day to find your next opportunity. Please share this post if it's helpful! Pro tip: Use commas in the job title to search for multiple jobs like: qa engineer, engineering manager, data scientist

Visit productView on Hacker NewsMar 27, 2024
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Actual performance

3points
6comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
76%76% 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 · Missing: plus, platform, intuitive
59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: month, way · Missing: mobile apps, ios, personal
58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos
49%49% 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: io · Missing: https docs, excited, just released
46%46% 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
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.

Correct prediction on native model

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