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An independent directory of AI misalignment reports

Hacker News

An independent directory of AI misalignment reports

Hey HN, In light of the concerns regarding AI safety, I created a website to track all known cases of misalignment. I will keep this up to date as more cases emerge. Anyone is encouraged to submit a report (anonymous or otherwise). Frontier labs should make all misalignment data public. Citizens have a right to transparency, especially when public policy decisions are being made. Let's all get aligned on the misalignment so we can make data-driven decisions in a democratic fashion. - Alex Wormuth

Visit product View on Hacker NewsSep 12, 2026

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · 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.

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

39%39% 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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

30%30% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

22%22% 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

9%9% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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