Results for “agents”

…Vibe Coding Manifesto: From Writing Code to Coordinating Agents Coding agents move the work toward asking questions, supervising execution, and integrating changes. The promise comes with a learning curve—and a bottleneck that more code…

Why OpenPipe Bet on RL: From Cheaper Tokens to More Reliable Agents Falling model prices weakened OpenPipe’s distillation business. Reinforcement learning offered a new opportunity: improve agents through experience, provided their training…

Async Agents Need More Than a Coding Model Devin and OpenInspect show what background coding requires: shared sessions, working environments, useful tests, scoped memory and review that keeps repositories maintainable.

Building agents that can find, fix, and verify their work Claude’s SWE-bench reference agent shows how much depends on tool design. Computer use and robotics extend the lesson to integration cost, reliability, and trust.

…AGI in Real Time — Part 3 of 3: From Model Capability to Useful Agents Part 3 of Latent Space’s OpenAI DevDay 2024 coverage explores deployment safety, agent reliability, product discovery, internal workflows, and the gap between generated …

…Company Intelligence: Private Evals, Open Harnesses, and Agents That Run the Work A reading edition of the Microsoft Build 2026 conversation on company-specific learning, reusable software assets, and AI whose value appears in completed…

Context Engineering for Agents: What to Keep, Retrieve, and Rebuild A research agent’s growing tool history turns context into an engineering problem—one that reaches into retrieval, multi-agent coordination, memory, and the architecture…

Debugging the Internet: CodiumAI’s Case for Specialized Coding Agents Generating tests is one step toward a larger goal: keeping specifications, tests, and code aligned, with developers deciding what the software should do.

DevDay 2025: From Tool Calls to Production Agents Apps SDK gives developers an interface inside ChatGPT. AgentKit brings workflow design, evaluation, and deployment together—with consequential limits at launch.

From Coding Agents to Inference Economics — Part 2 of 3 Part 2 follows the work of making AI useful: turning intent into tests, choosing long context or retrieval, and separating fast responses from sustainable inference.

From scaling models to building reliable agents 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.

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