Find your next rabbit hole.

David Luan’s account of OpenAI, Google and Adept connects research organization to a practical challenge: making models operate software reliably and learn from the people they help.

Part 5 of 5 examines why reversing a learned fact differs from reversing one supplied in a prompt, then explores how DSPy optimizes language-model programs—and why it still needs a useful evaluation metric.

Part 1 of Latent Space’s conversation with Yi Tay follows the path from general-purpose model research to Reka, where research judgment meets the difficulty of keeping rented compute running.

Building video models, preserving history, and combining generation with reasoning and editing tools: a reading edition of the Latent Space conversation with Ethan He.

Controlling reasoning is one part of building with Gemini. Live voice adds session limits, tool execution, conversational timing and the decision to stay silent.

Medal’s clips pair what players saw with what they did. General Intuition aims to use that experience to teach agents to act, then reconstruct situations where they can learn through interaction.

From reconstructing a workweek to redesigning Copilot, GitHub’s agent ambitions depend on shared context, credible review, and infrastructure built for changing workloads.

Cowork combines a coding agent, a controlled execution environment, browser access, and reusable instructions. Its central challenge is making work easy to delegate—and safe enough to leave running.

Production monitoring, live steering, and scientific discovery expose both the promise of mechanistic interpretability and the distance between finding a feature and controlling a capability.

Long-context reasoning, controlled coding agents, practical prompting, and workload-dependent costs in the April 2025 conversation.

Output specifications, validation and repair give developers more control over model behavior. SQL makes the mechanism concrete; an email assistant exposes the harder problem of specifying what the user wants.

Part 2 follows coding agents from demonstrating a change to working inside an enterprise—and asks which harness investments will remain useful as models improve.