Minimalytics – a standalone minimal analytics app built on SQLite

Hacker News

Hi everyone! I wanted to share my analytics app with you. This project came from requirements to track certain very frequent events. I found that the cost to do it on a regular analytics product was much more than i was willing to pay. Secondly, I also wanted to use as few resources as possible. So I thought it may be a good idea to create something that may be useful for myself (and hopefully others). I have been able to track a great number of events with this using ~20 MB of storage and memory which is incredible. I have been really impressed by golang as a language and as an ecosystem and would love to work more in this language going forward. Some Highlights: 1. No dependencies 2. CLI based management 3. Web based UI (and the server to serve it) included in the program. 4. 20 MB install size. 5. 20 MB memory use while running. 6. Minimal storage requirements because it aggregates events. This can be a great fit for anyone who wants to have a lightweight minimal analytics for internal events. I am looking forward to your comments and feedback.

Visit product View on Hacker NewsMar 20, 2025

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

78%78% 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

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

Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

39%39% 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

25%25% 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

20%20% 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.

Correct prediction on native model

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