A framework to observe epistemic drift in AI outputs

Hacker News

I’m an independent researcher building GuardianAI, a structural observer for AI systems. This demo runs a strict deterministic contract test where the model must output exact literals. GuardianAI doesn’t judge correctness or inspect content — it observes trajectory behavior and surfaces failure signals when outputs breach constraints, emitting control states such as CONTINUE or PAUSE. The interface shown is the visualization layer; the observer runs independently and can be tested via endpoint.

Visit product View on Hacker NewsFeb 26, 2026

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2points

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Launch Intel predictions

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

78%78% 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.

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

46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

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

29%29% predicted probability of success on BetaList, based on ML models trained on real launch data.

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

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

nativeThis product was originally launched on this platform.

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.

Correct prediction on native model

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