AI providers tried to hide the internal reasoning of their frontier models. A cryptographic oversight turned that protection into a massive enterprise data leak.
Developers often treat agent harnesses as neutral wiring, but new red-teaming research shows that your choice of harness can make or break your AI security.
Giving an LLM thousands of tools leads to noisy decisions. Learn how to optimize AI agent planning and tool routing without overwhelming the context window.
Current interpretability tools fracture continuous concepts into isolated points. Goodfire's new approach preserves the full shape of AI reasoning.
Passive RAG floods LLM context windows with noise. MRAgent’s active memory reconstruction improves reasoning and cuts token costs.
With harness engineering becoming a main focus of AI engineering, new frameworks allow AI agents to write their own execution logic and optimize their performance.
ASPIRE and the new era of self-improving AI frameworks are drastically reducing token costs and deployment friction for real-world robotics applications.
A breakdown of how OpenAI, Nvidia, Google, and Amazon are shifting their development strategies to capture value across every layer of the tech stack.
Chain-of-Thought prompting is slow, expensive, and largely an illusion. The future of machine reasoning happens in latent space.
Casual AI prompting breaks down as codebases grow. Codev introduces strict protocols and multi-model reviews to help teams ship maintainable software.