OpenAI Codex Team: From Coding Autocomplete to Asynchronous Autonomous Agents скачать в хорошем качестве

OpenAI Codex Team: From Coding Autocomplete to Asynchronous Autonomous Agents

Hanson Wang and Alexander Embiricos from OpenAI's Codex team discuss their latest AI coding agent that works independently in its own environment for up to 30 minutes, generating full pull requests from simple task descriptions. They explain how they trained the model beyond competitive programming to match real-world software engineering needs, the shift from pairing with AI to delegating to autonomous agents, and their vision for a future where the majority of code is written by agents working on their own computers. The conversation covers the technical challenges of long-running inference, the importance of creating realistic training environments, and how developers are already using Codex to fix bugs and implement features at OpenAI. Hosted by Sonya Huang and Lauren Reeder, Sequoia Capital Subscribe to our Substack for more AI insights: https://inferencebysequoia.substack.com/ 00:00 Introduction 01:46 Meet the Codex Team 03:24 The Evolution of Codex 07:07 Codex in Action: Real-World Applications 08:40 Internal Use and Future Vision 16:21 Technical Insights and Challenges 18:40 Challenges in Long-Running AI Tasks 19:01 User Intent and Task Granularity 20:47 Model Behavior in Extended Tasks 21:35 Future of Codex and AI Integration 24:18 Developer Tools and Market Evolution 26:25 The Role of Agents in Software Development 27:13 Practical Tips for AI-Enhanced Coding 31:43 Speculations on Future UI and Agent Interactions 33:40 Lightning Round: AI Insights and Predictions