BeBlob – A Gitlab-Powered Comment Section for Static Websites

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Hi everyone! I'm happy to introduce BeBlob, an open-source widget that adds a comment section to any static or dynamic website—without needing a dedicated backend—by leveraging GitLab issues. Inspired by my personal journey hosting my blog (belev.me) on GitLab Pages and noticing existing GitHub-backed solutions like utterances ( https://github.com/utterance/utterances ) and giscus ( https://laymonage.com/posts/giscus ), I wanted to create a similar tool for GitLab users. BeBlob automatically maps each webpage to a GitLab issue and uses GitLab OAuth for user authentication. It supports emoji reactions, markdown comments with live preview, code-highlighting and a set of customizable themes—four in total—that match the Hexo Cactus ( https://probberechts.github.io/hexo-theme-cactus/ ) theme I use on my blog. The solution is far from polished, but I wanted to get it out there and receive feedback from real users as early as possible. I’ve already integrated it into my personal blog, so I’m the first real user! Source code: https://gitlab.com/antonbelev/beblob Demo page: https://antonbelev.gitlab.io/beblob-demo/ Looking forward to your feedback!

Visit product View on Hacker NewsFeb 19, 2025

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

69%69% 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: supports · Missing: reddit linkedin, podcasting, created

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: personal, users · Missing: mobile apps, ios, entrepreneurs

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: host, users · Missing: plus, platform, intuitive

51%51% 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

11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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