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A practical account of coding agents: how they use tools, recover from errors, reuse workflows, and turn human feedback into completed work.

Video generation, segmentation with memory, faster detectors, and the challenge of teaching small models to see precise details.

Statsbot’s early Slack interface exposed two problems: understanding a question and understanding the data. Cube’s semantic layer separated them—and foundation models reopened the conversational side.

Part 3 of 3: the closing exchange of Latent Space’s conversation on NVIDIA’s AI engineering.

Ambient documentation gives Abridge a starting point for a harder problem: delivering accurate, timely assistance while clinicians can still act on it.

Part 4 of 4 moves from an open-source community gathering to Julius AI’s practical priorities: faster answers, multiple tool calls, visual feedback, and building useful products alongside a competing platform.

From broader task coverage to persistent codebases, coding evaluations are confronting what it means for an agent to do useful development.

Pydantic’s approach to agents connects data contracts, explicit control flow, and visibility into running applications.

A June 2024 conversation about turning foundation models into products, learning from deployment, and planning for changing inference costs and capabilities.

Unexpected polymer designs, active learning and quantum chemistry reveal what it takes to turn a promising prediction into a useful material.

As assistants take over discovery and reading, businesses need content that survives search selection, answers concrete questions, and reaches the model intact.

Closer model competition widened the opportunity for startups. Conviction’s NeurIPS 2024 keynote examined where that opportunity depended on better models—and where it depended on data, distribution and execution.