Lytics – open-source Web Analytics with Heatmaps

Hacker News

Hey HN, I built Lytics because other offerings don't let you compare dimensions (all functionality is crammed into a SPA) and I wanted segment-able click tracking with accurate heatmaps. Lytics uses the Quarkus web framework, clickhouse for the backend, and it has a query transpiler for the reports and heatmaps. Heatmaps use a custom renderer I built, and for the best possible experience they overlay directly on your website, no iframes are used. It's open source so you can dive into the code or host it yourself. Live demo here: https://app.lytics.cloud/account/view/41832119/dashboard I would love to get feedback. Thank you!

Visit product View on Hacker NewsJan 29, 2026

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Actual performance

5points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: code, open · Missing: mac, agents, macos

93%93% 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

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, clickhouse, io · Missing: https docs, excited, just released

75%75% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: host · Missing: plus, platform, intuitive

53%53% 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

42%42% 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

16%16% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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