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Hushstat – Cookie-free web analytics with heatmaps

Hacker News

Hushstat – Cookie-free web analytics with heatmaps

Hi HN, I built Hushstat to combine traffic analytics with a closer look at what visitors do on a website. It includes traffic sources, funnels, heatmaps, and signals such as rage clicks. The tracker does not use tracking cookies. The stack is Go, Postgres, and React. There is a 20-day free trial with no card required. I would appreciate feedback on the dashboard and installation experience. What would you need before using it on your own website?

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: using · Missing: mac, agents, macos
77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
47%47% predicted probability of success on AppSumo, 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
39%39% 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
15%15% 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
6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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