Why benchmarking AI models in a vacuum is a critical security blind spot
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.
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How to build AI agents that can handle thousands of tools
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.
Inside BSF: How Goodfire unlocked a new level of AI interpretability and control
Current interpretability tools fracture continuous concepts into isolated points. Goodfire's new approach preserves the full shape of AI reasoning.
Rethinking AI agent memory through reconstruction
Passive RAG floods LLM context windows with noise. MRAgent’s active memory reconstruction improves reasoning and cuts token costs.
A primer on self-improving agent harnesses
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.
From self-improving agents to self-improving robots
Nvidia's ASPIRE and the new era of self-improving AI frameworks are drastically reducing token costs and deployment friction for real-world robotics applications.
The myth of the AI moat: Why tech giants are bleeding into each other's territory
OpenAI is doing hardware, Nvidia is doing models, xAI is doing data centers, Google is doing everything.
Loop engineering vs loopmaxxing
Use agentic loops to speed up your AI applications. But don't overdo it.
Escaping the chain-of-thought trap: What is next for LLM reasoning
Chain-of-Thought prompting is slow, expensive, and largely an illusion. The future of machine reasoning happens in latent space.
How Codev brings discipline to AI software development
Casual AI prompting breaks down as codebases grow. Codev introduces strict protocols and multi-model reviews to help teams ship maintainable software.
Scaling the harness: The next major bottleneck in agentic AI
Scaling LLMs hits limits when dealing with agentic AI tasks. For that, we need to look at the harness and the system built around the model(s).
What makes Cursor's Composer 2.5 a good coding model (and what are the caveats)
A deep look at the self-distillation techniques that make Composer 2.5 such a great coding model (and the hidden tradeoffs they introduce to AI reasoning).
How prompt injection broke Nvidia's sandboxed OpenClaw agent
Research into Nvidia’s NemoClaw reveals that sandboxes don't stop AI agents like OpenClaw from leaking data. We need to rethink security from first principles.
Unpacking Gemma 4’s multi-token prediction (and why you should care)
How Gemma 4’s multi-token prediction and community-driven DFlash are speeding up local LLM throughput by 3-6x.
How to scale LLMs to 100 million tokens without blowing up memory costs
Memory Sparse Attention (MSA) scales LLM context windows to an unprecedented 100 million tokens while preserving accuracy.
The silent threat of Claude Code (and other AI coding assistants)
A new study reveals how AI coding assistants like Claude Code are quietly hoarding and publishing sensitive API keys to code repositories.
The 'by design' security flaw of Model Context Protocol (MCP)
Security researchers have uncovered a massive architectural flaw in Anthropic's Model Context Protocol, exposing millions of AI applications to remote takeovers.
The hidden trap of LLMs self-distillation
Optimizing LLMs for concise answers can destroy their ability to explore alternative solutions on difficult problems. New study reveals the hidden cost of self-distillation.
The art of AI harness engineering
The recent leak of Anthropic's Claude Code reveals a hard truth: as LLMs become commoditized, the sophisticated engineering harness built around them is becoming the real moat.
How GhostClaw exploits macOS and OpenClaw to steal developer credentials
As developers rush to run local AI agents on Mac Minis, GhostClaw malware exploits macOS binaries to silently harvest credentials.