I

I made a streak tracker for ProductHunt

Hacker News

I made a streak tracker for ProductHunt

One day I woke up and realized, oh f*ck dude, I forgot yesterday to check in at ProductHunt and boom, 400-day streak is gone. I'm a human, that is addicted to streaks, look at my 600-day Duolingo streak or my Snapchat streaks with my friends. So I built a tool that helps you to never ever forget your ProductHunt streak. And well, I reached place 5 of the day, and this makes me really happy. Some guys gave me feedback that I should build this tool not so general; instead, I should build it for every streak and not only for ProductHunt. I think I will do this in the future, but technically you can already use it as any streak tracker you want. Check it out now (Btw, it's completely free and will stay free FOREVER)

Visit product View on Hacker NewsMar 18, 2025

Actual performance

3points

2comments

Did not reach leaderboard

Launch Intel predictions

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

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Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released

45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

40%40% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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