Find your next rabbit hole.

Part 4 of 4 moves from the Keep AI Open party to Julius AI’s code-execution workflows: why latency matters, how parallel tools could shorten repairs, and where startups can build alongside a platform.

Part 2 follows the move from conversational configuration to connected workflows—and the consequences for retrieval control, hosting costs, and developers building on OpenAI.

How Genesis combines physics-guided co-folding, demanding evaluations, and laboratory feedback—and why useful drug-discovery agents depend on all three.

Why API traffic cannot easily reveal training intent, why the strongest model may be a poor teacher, and how coding benchmarks can reward knowledge their tasks never supplied.

An April 2023 conversation with Mike Conover about shaping model behavior, building open datasets, and evaluating what applications actually need.

Shared tools, coordinated agents, inspectable code, and the product judgment behind personal software.

Useful workplace agents depend on bounded instructions, carefully prepared company data, and a product employees can shape around their own work.

From oversight experiments to reusable notebooks, Elicit builds research workflows whose steps can be trained, checked, and scaled.

Chatbots for adult creators must coordinate persona, sales, memory, and boundaries. Convincing language is only one part of the system.

Glean’s workplace search problem opens a broader discussion of ranking, tool use, model costs, and knowing which work to automate.

Learning evolution’s constraints from protein sequences opens a route to structure prediction, molecular design, and discovery. Extending that approach to cells will require new data and experimental feedback.

Enterprise agents need useful context, bounded access, a workspace, and human oversight. Making those pieces fit an organization is as much a workflow problem as a model problem.