Snorkel AI Raises $100 Million To Build Better Evaluators For AI Models

Coding agents have moved from tab-complete to teammate. They autonomously inspect repositories, edit files, run commands, diagnose failures, and work through multi-step engineering tasks. That creates a harder reliability problem. A model that only suggests code is easy for a human to evaluate. A coding agent refactoring your repository and testing its own changes is much harder to supervise –…

At our latest Snorkel AI Reading Group, Mayee Chen (Stanford, Hazy Research) stopped by our San Francisco office to walk us through Olmix: A Framework for Data Mixing Throughout LM Development — work she contributed to during her internship at Ai2 on OLMo 3. Olmix tackles one of the messiest, least-documented levers in LLM pre-training: how to set the ratios…

Since launching the Open Benchmarks Grants, we’ve received more than 100 applications from academic groups and industry labs spanning a wide range of domains and capabilities. As the best benchmarks drive how the field allocates research effort, the bar for benchmarks has risen as well. Here, we share what’s now table stakes for useful benchmarks, and what separates the ones…