MLSentinel – monitor ML models and catch failures early

Hacker News

I built MLSentinel to make it easier to monitor ML models after they are deployed. One problem I kept running into was that a model could continue running normally while something underneath was starting to go wrong. By the time you notice it, you may already have spent hours trying to figure out what changed. MLSentinel monitors model health and surfaces issues through monitoring, reports, and alerts. The software is free to use. The platform is not open source, but the SDK is publicly available here: https://github.com/Narasimha440/mlsentinel You can try the platform here: https://mlsentinel.dev I'd especially like feedback from people who have deployed ML models: what do you currently monitor in production, and what tends to get missed?

Visit product View on Hacker NewsAug 27, 2026

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nativeThis product was originally launched on this platform.

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60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

32%32% 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

15%15% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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