赛博义父Tibo最新访谈：专门实体按钮搞重置，“我想重置就重置”
Ranking
Overall
61
Content
65
Popularity
N/A
No observed public metrics; popularity remains neutral/archived.
Merged summary
TL;DR - OpenAI Codex lead Thibault Sottiaux outlined a future in which Codex and ChatGPT merge into one personalized, multimodal agent backed by cloud-scale compute. The shift could replace today’s manually managed skills, memory, and agent swarms with faster, context-aware interaction and automated workflows.
- Future agents are expected to continuously understand users, teams, goals, and routines rather than requiring explicit skill files, memory management, or sub-agent orchestration.
- Laptop resources may become the limiting factor as models operate many applications concurrently, pushing advanced agents toward cloud infrastructure.
- OpenAI’s proposed “Ultra Fast” mode could favor real-time, voice-driven collaboration over running 10–15 slow agents in parallel and repeatedly switching context.
- OpenAI is using strong models to optimize CUDA kernels, inference systems, and infrastructure, creating a recursive efficiency-and-capability improvement loop.
Sources (1)
赛博义父Tibo最新访谈：专门实体按钮搞重置，“我想重置就重置”
Public signals
N/A
TL;DR - OpenAI Codex lead Thibault Sottiaux outlined a future in which Codex and ChatGPT merge into one personalized, multimodal agent backed by cloud-scale compute. The shift could replace today’s manually managed skills, memory, and agent swarms with faster, context-aware interaction and automated workflows.
- Future agents are expected to continuously understand users, teams, goals, and routines rather than requiring explicit skill files, memory management, or sub-agent orchestration.
- Laptop resources may become the limiting factor as models operate many applications concurrently, pushing advanced agents toward cloud infrastructure.
- OpenAI’s proposed “Ultra Fast” mode could favor real-time, voice-driven collaboration over running 10–15 slow agents in parallel and repeatedly switching context.
- OpenAI is using strong models to optimize CUDA kernels, inference systems, and infrastructure, creating a recursive efficiency-and-capability improvement loop.