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I built a cheap privacy-friendly web analitics tool

Hacker News

I built a cheap privacy-friendly web analitics tool

Hey HN, I run a lot of small/medium websites, and I wanted to have basic analytics for all of them without having to use google analytics. I've recently released a tool I created, after being annoyed with constantly seeing the $10+ per month subscriptions, mostly powered by DynamoDB. I'm thinking of releasing it open source at some point (when the code is cleaner, curse of developer overthinking). Let me know what you think. Cheers

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Actual performance

2points
2comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, code, open · Missing: mac, agents, macos
83%83% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting
80%80% 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, io · Missing: https docs, excited, just released
69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: month, google · Missing: mobile apps, ios, personal
56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: friendly · Missing: plus, platform, intuitive
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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