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ClickNest a tiny self-hosted, AI native web analytics in Go

Hacker News

ClickNest a tiny self-hosted, AI native web analytics in Go

I've been building a bunch of random side projects, as I'm sure we all are. Most analytics tools were either too heavy or required juggling multiple free tiers across different subdomains. After looking what's out there I couldn't find a lightweight analytics platform that worked on the free tier of fly.io. It's a single Go binary using DuckDB for events and SQLite for metadata, so there are no external dependencies to manage. Drop a script tag on your site and it auto-captures pageviews and clicks. The part I'm most excited about: it uses an LLM to automatically name raw click events into something human-readable. Instead of seeing button.btn-primary#checkout in your dashboard, you see "Clicked checkout button". No manual event instrumentation needed. One fly launch and you have your own analytics instance for free. GitHub: https://github.com/danielthedm/clicknest

Visit productView on Hacker NewsFeb 26, 2026
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: single, tiny, using · Missing: mac, agents, macos
82%82% 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
58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, ide, io · Missing: https docs, just released, exist
47%47% 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
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews
28%28% 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
17%17% 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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