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http://mindovermatter.it/ Just a quick and dirty hack I did while learning Go and how to set up an EC2 server. A proof of concept for a feature I think it would be useful on HN. It often happens that I read the whole thread of comments for a discussion, and get back to it at a later time to see the updates. It's hard to see at a glance which comments are new and which ones I already read. The app uses cookies to record the last time you accessed a discussion thread, and modify the html in order to highlight the new comments (the ones added since your last GET of the page). Just access a discussion thread through the proxy site, and come back when new comments are added to see the effect. (Obviously I don't have access to HN backend, so you can't do anything more than reading a thread).

Visit product View on Hacker NewsSep 3, 2013

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

75%75% 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.

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

65%65% 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

44%44% 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

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

29%29% predicted probability of success on Indie Hackers, 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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