iine – privacy-first like buttons for the small/indie web

Hacker News

I wanted to add likes/kudos to my blog without tracking or account creation. I also wanted an excuse to build my first project with a backend. I focused on: * privacy: no user tracking; no account required * accessibility: kb navigation, screen reader friendly * works without js: the button is a `<form>` submission when JS is disabled * lightweight: ~3KB of JS * self-hostable and free: AGPL licensed; can run on your own PostgreSQL setup The backend stores URL + counter. No timestamp, IP, user-agent, cookies… Since 500MB of free Supabase storage can support over 100,000 sites, I decided it to make it free and public. --- Code (AGPL) & self-hosting guide: https://github.com/welpo/iine The full story (my blog post): https://osc.garden/blog/iine-small-web-like-buttons/ Feedback is more than welcome!

Visit product View on Hacker NewsJul 30, 2025

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Launch Intel predictions

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

76%76% 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

62%62% 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, 000, io · Missing: https docs, excited, just released

53%53% 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: host, friendly · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

38%38% 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

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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