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Phinxer, open source Web Analytics

Hacker News

Phinxer, open source Web Analytics

I built Phinxer for my own use a year or so ago because existing alternatives felt bad to use (Google Analytics) becasue of excessive tracking or were really expensive. I was running an app that had ~350k pageviews at the time but was only making $80 from ads so paying $50 a month for analytics sounded silly. The actual cost of running this is only a fraction of that, so I'm now releasing the tool I wish I had found back then. It's significantly cheaper than all web analytics alternatives at $10 per 1M events that don't expire. So if you use less than 1M events you can use the events for up to a year (alternatives cost $60-$140/mo for the same volume). Phinxer is also open source! It's built with an Express backend, React app, Postgres for general data and ClickHouse for events. I used [Vike]( https://vike.dev/ ) which is based in Vite for this project and was very pleasantly surprised, it's what I always wished Next.js was. Code here: https://github.com/gnardini/analytics

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Actual performance

4points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: google, code, open · Missing: mac, agents, macos
88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, existing · Missing: https docs, excited, just released
83%83% 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, way · Missing: mobile apps, ios, personal
54%54% 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
26%26% 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
22%22% 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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