Reddit Unread Comments – track new comments on Reddit easier

Hacker News

Hello everyone. I made a Firefox/Chrome extension for easier tracking of new comments in Reddit threads. Have you ever felt frustrated having to scroll and reread 100 comments thread just to find a few new scattered comments? If yes, then this extension is for you. Hopefully it will save you nerves and time, give it a try and let me know your impressions. The extension is completely free and open source using MIT license. It is privacy aware, runs completely client side and no data ever leaves your browser and no user activity or data is tracked. It is completely unobtrusive to the existing Reddit's website behavior, you wont notice any changes in the website behavior and performance. Also it runs only in `reddit.com` (no `old.reddit.com` and `new.reddit.com`) pages and any other domain is intact and doesn't load the extension code. You can find the code, links to Firefox and Chrome stores, install binaries and documentation here: https://github.com/nemanjam/reddit-unread-comments/ You can install the extension either from the store, manually import the binaries from the Release page, or build it yourself from source. Demo video is available here: https://www.youtube.com/watch?v=dHw0pM3ZzqY If you are an user you can find the features overview note here: https://github.com/nemanjam/reddit-unread-comments/blob/main... If you are a developer and want to contribute or review code you can find the implementation details explained in this note: https://github.com/nemanjam/reddit-unread-comments/blob/main... If you use Reddit on regular basis give it a try and share your impressions. I will appreciate all constructive feedback, if you like the project and you are developer yourself you can contribute with a pull request or a code review. If you are an user you can contribute by sharing the link with your friends or leaving a feedback.

Visit product View on Hacker NewsDec 21, 2023

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

92%92% 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

81%81% 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, open source, existing · Missing: https docs, excited, just released

67%67% 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 · Missing: plus, platform, intuitive

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

30%30% 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

14%14% 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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