Launch HN: Laminar (YC S24) – RL-trained model that finds errors in agent traces
Recently, we released flow-1, a model trained with RL to find errors in agent traces.
Proof1 community thread
Key points
- We’ve been building open-source observability for AI agents since 2024.
- Agent observability is crowded, so instead of a bullet list of how Laminar differs from X, we’ll explain how we’re solving agent observability from first principles.
- Simply looking at the final agent output is not enough.
- We finetuned a small OSS model and then post trained it with RL on thousands of synthetic investigations of realistic agent workflows in software, legal, customer support, etc. We got a model that is both good at agent trace analysis and very cheap to run.
Sources (1)
- [1]Launch HN: Laminar (YC S24) – RL-trained model that finds errors in agent tracesHacker News: LLM threads (40+ points) · Oct 19, 04:00 PM
Recently, we released flow-1, a model trained with RL to find errors in agent traces.
We’ve been building open-source observability for AI agents since 2024.
Extractive summary: sentences quoted from the sources.
Hype check
Independently evaluated5 independent looks since
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Before this
- Oct 19, 2026The Model Swap Workshop: GPT-5.5 vs Kimi vs DeepSeek vs Mistral
- Oct 19, 2026MiniMax M3.1 Flash shows China's AI price race has no floor in sight
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- Oct 19, 2026AdaExploreBench: Benchmarking LLM Agents for Proactive Exploration and Test-time Adaptation in Evolving Environments
- Oct 18, 2026Google Open-Sources Harness2, a Recursive Agent Harnessing Framework
- Oct 18, 2026Elevatormusic/DeepSeek-V4.1-Flash-1017-EXL3-TensorFold-2x-DGX-Spark: DeepSeek-V4.1-Flash-1017 EXL3 2.9bpw on 2x NVIDIA DGX Spark (GB10) with TensorFold: 2.1-2.3x the vLLM kit's decode speed.