Signals

Production-oriented research signals and interpretation for AI systems builders.

Deep Dives and Frameworks

Implementation playbooks, operator patterns, and durable analysis.

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Agents That Rewrite Themselves: The Self-Modifying Stack Is Here

Three independent papers demonstrate agents rewriting their own training code, generating their own knowledge structures, and refining their reasoning at test time. Self-improvement has moved from theory to working engineering.

7 min read
The Prompt Engineering Ceiling: Why Better Instructions Won't Save You

The Prompt Engineering Ceiling: Why Better Instructions Won't Save You

On frontier models, sophisticated prompting underperforms zero-shot queries. The techniques that made mid-tier models usable are now making frontier models worse.

8 min read
When Models See and Speak: The Multimodal Agent Arrives

When Models See and Speak: The Multimodal Agent Arrives

Multimodal agents are navigating websites, controlling robots, and generating 3D scenes. But perception is the bottleneck, and bridging it requires rethinking how models attend to the world.

5 min read
Robots With Reasoning: When Language Models Meet the Physical World

Robots With Reasoning: When Language Models Meet the Physical World

A robot arm completing 84.9% of manipulation tasks without a single demonstration. Not through months of reinforcement learning: through pure language model reasoning. The line between software agents and physical robots is blurring.

5 min read
Interpretability as Infrastructure: Why Understanding AI Matters More Than Controlling It

Interpretability as Infrastructure: Why Understanding AI Matters More Than Controlling It

Mechanistic interpretability has moved from describing what models do to engineering how they work. If you can identify the neurons responsible for a specific behavior, you don't need to control the entire system.

7 min read
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