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Part 2 of the OpenAI DevDay 2024 conversation moves from voice interfaces and application-specific evaluation to Cosine’s software-engineering models, then to planning products around unpredictable research advances.

Part 3 of Latent Space’s OpenAI DevDay 2024 coverage explores deployment safety, agent reliability, product discovery, internal workflows, and the gap between generated code and useful software.

Part 1 follows realtime voice from database lookups to a 400-strawberry order, then explores evaluation-driven model improvement and o1-assisted application development.

Modular connects asynchronous execution, compiler fusion and Mojo’s Python-oriented language design to a common goal: making new computations easier to express without giving up hardware performance.

Singapore’s AI strategy connects workplace adoption, practical skills and compute access—with distinct roles for government, employers and engineers.

Superhuman connects deep search, focused tools and demanding evaluations with a harder product question: how much work can an assistant remove without creating more review?

A reading edition of the Microsoft Build 2026 conversation on company-specific learning, reusable software assets, and AI whose value appears in completed work.

Google Labs’ coding agent puts independent execution, simpler orchestration, and persistent context in service of a harder problem: specifying and verifying useful software.

From capability discovery and conversational interfaces to product differentiation, model evaluation, and personalized AI.

Saving an insight takes a triple-tap. Making it searchable and useful later takes audio alignment, careful model choices and an interface built around listening habits. This conversation was published [March 14, 2025](https://www.latent.space/p/snipd).

From calendar classifiers to company-wide search: useful AI, the infrastructure around it, and the judgment needed to rethink work.

Designing an antibody means specifying where it should bind, what it should avoid, and how it should behave. Chai’s models and visual tools aim to make that process programmable, while experiments remain the test.