SycoFact 4B: Open model detecting sycophancy and delusion confirmation

Hacker News

I published a model you can use now to help detect sycophantic AI responses before they harm users. It rejects 100% of the sycophantic delusion affirming responses from psychosis-bench. It also does well on the AISI Harmful Advice, PKU-SafeRLHF, and safety subsets of RewardBench. It's small enough it can run on a gaming GPU locally. It's got a GGUF checkpoint on hugging face and is available on ollama. You can pull it and run scenarios against it in minutes: https://ollama.com/izzie/sycofact The synthetic training data is also public, you can train other models over the data or reproduce my results. The labels were all generated by Gemma 3 27B with activation steering based on generated contrastive data. A write-up is planned at a later date, feel free to get in touch if curious.

Visit product View on Hacker NewsMar 30, 2026

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

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Hacker NewsMay not resonate with HN audience · Strong signals: llama, 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.

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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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