Au

Automatically hide flamebait/shallow/political comments on HN

Hacker News

Automatically hide flamebait/shallow/political comments on HN

I love HN, but lately I have been sick of reading the same dismissive criticisms over and over again. Along with political arguments that have been litigated to death, people's issues with smooth scrolling on blogs, etc. Many of these comments do not get flagged for whatever reason. So I made a service to automatically classify whether comments violate (a modified form of) the HN guidelines automatically. In addition there's a Chrome extension to collapse these comments (if they violate your score thresholds) so you don't have to read them too. You can also just watch guideline violating comments come in as they are posted on the website. Here's more info on how it works: https://classify.stylometry.net/how-it-works

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Actual performance

2points
4comments
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
56%56% 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 NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
49%49% 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 · Missing: mobile apps, ios, personal
41%41% 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
34%34% 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
18%18% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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