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A lightweight open-source web analytics for webdevs

Hacker News

A lightweight open-source web analytics for webdevs

I wanted to share something I've been working on recently: Statum, a handy web analytics tool. https://github.com/extractumio/statum The journey began when I found myself frequently needing simple web analytics for my projects. I tried Google Analytics, especially GA4, and realized it was quite complex and, at times, not very accurate, especially when I wanted to view stats for the current day or recent hours. Then I tested a few fancy startup solution but ended up with way too expensive plans the expect me to subscribe (I'm not that rich to pay $99/month for every pet project web analytics). This drove me to think, "Why not create a tool that bridges the gap between these complex/expensive platforms and the real needs of developers like me?" So, I designed Statum to seamlessly parse both Nginx and Apache server logs. It filters all necessary data, including IP, http method, URL, and user agent. What's more, it can categorize these user-agents into mobile users, bots, and others. Besides that...to provide a holistic view, Statum augments local log file statistics with web-based JavaScript analytics, offering even deeper insights, especially into returning visitors. With Statum, you can quickly get a grip on: - The number of visitors and a breakdown between bots and real users. - Identify visitors as unique entities (tied to the browser). - Client-side parameters such as screen resolution, OS, language. - Unique user-agents and much more. I've aimed to make the setup process straightforward (it's just one js line to insert into your website template and update a few paths in .py file to parse logs from the right place). Currently, it does not have UI, I use just a text output from sqlite, the default query to get the number of visitors and return visitors is in gen_stats.sh so you can enhance it depending on what you want to analyze. I'd appreciate any feedback you have on the project. Please keep in mind that this is an initial version and it's not designed for high-traffic websites.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, google · Missing: mac, macos, cursor
96%96% 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: para, including · Missing: supports, reddit linkedin, podcasting
94%94% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: nginx, ide, io · Missing: https docs, excited, just released
66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, google, users · Missing: mobile apps, ios, personal
46%46% 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
9%9% 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.

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