GenOps AI – OSS (OpenTelemetry) runtime governance for AI workloads

Hacker News

We just open-sourced GenOps AI, a framework for standardizing runtime governance, cost tracing, and policy enforcement across AI workloads. It builds alongside OpenLLMetry, extending the OpenTelemetry spec for LLM usage, FinOps alignment, and compliance reporting. Repo: https://github.com/KoshiHQ/GenOps-AI We’d love feedback from folks working on AI infrastructure, observability, and FinOps tooling, as well as contributors interested in evolving the open spec.

Visit product View on Hacker NewsOct 29, 2025

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

70%70% 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.

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

35%35% 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

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

28%28% predicted probability of success on Indie Hackers, 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.

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

17%17% 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

11%11% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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