Day Marks

Day Marks

Indie Hackers

Track your performance on personally given parameters🏆

I was looking through my notebook where I typically write marks for productivity, my project efforts, and other params. I decided to explore my overall performance, and the best way to do so were – to build an app.

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Actual performance

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal, way, para · Missing: mobile apps, ios, entrepreneurs
58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released
55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
48%48% 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
17%17% 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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