HTML Commenter

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HTML Commenter

Hi! I vibe coded a small tool that I think is relatively unique — at least, I haven't found anything like it — and I've found personally extremely useful. I don't know if anyone else will, but I'm sharing it just in case someone does find it useful. Basic description: HTML Commenter is a zero-dependency, self-contained document annotation tool for any static HTML page. This system allows users to annotate the text on a web page, auto-saves their comments to local storage, and lets them share their comments with others via compressed links or back them up with JSON files, all without requiring a server or external libraries. The goal of this project is to provide a lightweight collaborative document annotation experience for people who: - don't want to use SaaS and/or heavy PWAs - don't want to have to install and maintain software on their own server - don't want to ask users to install software (like LibreOffice) don't want to have to send - multiple versions of files around like it's 1990 and we're dealing with Word Documents - prefer writing in plain markup languages (like Markdown, Org, or HTML), exporting to HTML, and posting their writing to their domain (either self hosted or on things like NeoCities) Github: https://github.com/alexispurslane/html-commenter/ URL for trying it out: https://alexispurslane.github.io/html-commenter/try.html

Visit product View on Hacker NewsAug 28, 2025

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Actual performance

3points

2comments

Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

52%52% 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, users · Missing: plus, platform, intuitive

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: personal, users · Missing: mobile apps, ios, entrepreneurs

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue

13%13% 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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