Find your next rabbit hole.

Ryan Lopopolo’s experiment began with a constraint: write none of the code himself. Part 1 follows the resulting work on builds, observability, review, and Symphony’s agent orchestration—and the human attention those systems still require.

A roundtable on local tools, fast decisions, human review and the permissions that shape everyday assistants.

Elicit’s approach combines software engineering, curiosity about model capabilities, and product judgment about when to recover, experiment, or report a failure.

From enterprise search to coding agents, durable advantages depend on integration work, product habits, research progress, and the judgment needed to use the results.

Pokémon exposes the gap between choosing a goal and reliably acting on it—and makes improvements in long-running agents visible.

Ari Weinstein and Nikunj Handa explain the interface and API choices behind faster agents, and where their limits remain.

Jason Ma explains human-video pretraining, robot demonstrations, and the hardware and workflow decisions behind Dyna’s physical agents.

From asking questions of a textbook to generating two-person audio discussions, NotebookLM’s development joined model capability with editorial judgment—and a deliberately simple interface.

Inside WorldPrompt, real-time diffusion, robot simulation, and the limits of predicting what happens next.

From selecting wind-tunnel experiments to evaluating generative applications, Humanloop’s story follows a changing bottleneck: turning model behavior into evidence a team can act on.

Part 1 follows the VAE retrospective, Würstchen’s compressed image generation, concept-based controls for diffusion, the compression argument for unsupervised learning, and the opening of Christian Szegedy’s adversarial-examples retrospective.

Part 1 follows the path from rebuilding an NLP course after ChatGPT to web agents, social evaluation, code optimization and the compute constraints shaping academic research.