Spot Hallucinations in ChatGPT

Hacker News

Hi everyone, I have been bothered by hallucinations in ChatGPT. So I built an extension flagging potential hallucinations in ChatGPT. It uses heuristics ran locally as a first test. There are optional checks by references to fact-checking databases and a further interesting approach of asking ChatGPT multiples times to spot changes in the answer - there was a research paper called SelfCheckGPT using this. It is not invasive if you want to keep the flow intact but if you work on sensitive work you can toggle on the flags in line which wit warn you more visually. All logic stays client-side except the optional API calls, so the add-on is fast, private, and easy to audit. Let me know your thoughts

Visit product View on Hacker NewsMay 28, 2025

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

84%84% 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.

AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive

48%48% predicted probability of success on AppSumo, 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.

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

33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

31%31% 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

17%17% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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