Predictability and surprise in large generative models
Aggregate performance of large models is predictable via scaling laws, yet specific capabilities emerge unpredictably.
Towards measuring the representation of subjective global opinions in language modelsAnthropic
Tracing the thoughts of a large language modelAnthropic
Red teaming language models to reduce harmsAnthropic
Reasoning models don't always say what they thinkAnthropic
Auditing language models for hidden objectivesAnthropic
Decomposing language models into componentsAnthropic
10 best AI observability tools for monitoring and evaluating agents in 2026Mintlify
New Spanish and Turkish Language Models and Updated General Models - Deepgram Blog ⚡️Deepgram
AI frameworks: Definition, types, and how to chooseZapier
Discovered Stacks: One Place for All Your InfrastructurePulumi
How Hunch supercharged AI workflows with Modal SandboxesModal
Anthropic integration with Modal brings scalable compute to Claude ScienceModal
Revolutionizing Life Science and Healthcare with Generative AI - CerebrasCerebras
Cerebras Systems Announces Filing of Registration Statement for Proposed Initial Public OfferingCerebras
Introducing DocChat: GPT-4 Level Conversational QA Trained In a Few Hours - CerebrasCerebras
Cerebras Systems Enables GPU-Impossible™ Long Sequence Lengths Improving Accuracy in NaturalCerebras
Cerebras Announces New Board Members and Chief Financial Officer - CerebrasCerebras
How to Run Hugging Face Models Programmatically Using Ollama and TestcontainersDocker
API docs with Git integration: best platforms and workflows in 2026Mintlify
Lies, damn lies, and benchmarksDeepgram
