Resource library
Explore our complete library of resources including blogs, benchmarks, research papers, and more.
Data 2.0 and the research era of AI data
Announcing Snorkel’s $350M fundraise to build the frontier lab for AI data.
Terminal-Bench 4.0: Why Continuous Benchmarks Require Continuous QA
Grok 4.5 Testing Results: How SpaceXAI’s New Model Performs on Real Professional Work
Why coding agents need better data, evals, and environments
Closing the Evaluation Gap in Agentic AI
Announcing a $3M commitment to launch Open Benchmarks Grants
Why Frontier Agents Fail Real Engineering Work: Two Terminal-Bench 3.0 Task Deep Dives
Building FinQA: An Open RL Environment for Financial Reasoning Agents
The science of rubric design
AI alignment ensures that AI systems align with human values, ethics, and policies. Here’s a primer on how developers can build safer AI.
The Snorkel Flow label model plays an instrumental role in driving the enterprise value we create. Here’s a peek at how it works.
Learn how Alfred enables users to encode their subject matter expertise via natural language prompts for language and vision-language models.
Vision language models demonstrate impressive image classification capabilities, but LLMs can help improve their performance. Learn how.
Learn how to fine-tune LLMs like Meta’s Llama 3 for specialized tasks and see a live demo of curating high-quality training data with Snorkel Flow
Snorkel researchers devised a new way to evaluate long context models and address their “lost-in-the-middle” challenges with mediod voting.
See a walkthrough of how Snorkel Flow users build applications with production-grade RAG retrieval components.
Learn how to optimize RAG components to increase accuracy with programmatic data development in this free webinar.
