UserScript to detect GPT generated comments on Hacker News

Hacker News

It puts every comment into the GPT output detector and colors and writes a short comment on the HN comment based on its assessment, like you see in the screenshot. This is based on a threshold set by me. >0.7 is probably AI, >0.9 is definitely AI. Lower than that is most likely human. Most comments on HN still appear to be human. It only becomes reliable after about 50 tokens (one token is around 4 characters) so I mark the comments that are too short with gray and make no assessment on those. I know pretty much nothing about Javascript. This is shitty code largely written by ChatGPT itself, untested beyond Chrome on MacOS, and no plans for maintenance or extends. If you want features or support, you have to ask ChatGPT, not me. Let me know what you think, and please don't comment on code quality.

Visit product View on Hacker NewsDec 8, 2022

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Actual performance

2points

3comments

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: mac, macos, user · Missing: agents, agent, cursor

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

Hacker NewsMay not resonate with HN audience · Strong signals: hacker news · Missing: https docs, excited, just released

50%50% 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: users · Missing: plus, platform, intuitive

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

35%35% 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

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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