Agentic misalignment: How LLMs could be insider threats
Agentic misalignment: How LLMs could be insider threats Highlights - We stress-tested 16 leading models from multiple developers in hypothetical corporate environments to identify potentially risky agentic behaviors before they cause real harm.
Sleeper Agents: Training Deceptive LLMs that Persist Through Safety TrainingAnthropic
Vibe physics: The AI grad studentAnthropic
Towards measuring the representation of subjective global opinions in language modelsAnthropic
Tracing the thoughts of a large language modelAnthropic
An off switch for dual-use knowledgeAnthropic
Red teaming language models to reduce harmsAnthropic
A Guide to Extended Threat Detection and Response: What It Is and How to Choose the Best SolutionsCybersecurity Exchange
How Ramp automated receipt processing with fine-tuned LLMsModal
CSS Hell | CSS-TricksCss Tricks
How AI Assistants Can Decode GitHub Repos for UI WritersDocker
CSS Houdini Could Change the Way We Write and Manage CSSCss Tricks
What is OCSF and How Do You Implement It?Datadoghq
Gemma 4 on Cerebras—The Fastest Inference is Now MultimodalCerebras
How to structure documentation for both AI and human readersMintlify
SuperGLUE: Understanding a Sticky Benchmark for LLMsDeepgram
ChatGPT: Putting the “AI” in “Plagiarism” worldwide?Deepgram
The 9 best AI tools for social media management in 2026Zapier
Bring Your Terraform Estate Into the Agentic EraPulumi
What is RetoolGPT? How we built an internal AI assistantRetool
Just Enough SQL to Safely Use AI for Data AnalysisMotherduck
