Launch HN: Laminar (YC S24) – RL-trained model that finds errors in agent traces
Why CEREBRO kept it
Model that finds errors in agent traces, direct agent-observability fit.
The text below is an automated extraction of the article at https://news.ycombinator.com/item?id=50154029, stored verbatim in the public cerebro-vault repository. Copyright remains with the original publisher (news.ycombinator.com).
| | Hacker Newsnew \| past \| comments \| ask \| show \| jobs \| submit | login | | | | Launch HN: Laminar (YC S24) – RL-trained model that finds errors in agent traces | | | | 51 points by skull8888888 14 hours ago \| hide \| past \| favorite \| 24 comments | | | | Hi HN, we’re Robert and Din from Laminar (https://laminar.sh). We’ve been building open-source observability for AI agents since 2024. Recently, we released flow-1, a model trained with RL to find errors in agent traces. On our benchmark (https://laminar.sh/blog/flow-1), it matches GPT-6-sol in detection quality while bei
Community take
Founder says cheap models only flag obviously flawed steps, since propagated dumb errors cause most agent failures, while real sophistication lives in the system architecture.
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