OpenAI Developers@OpenAIDevs · 4月8日42

We’re retiring older models in Codex when you sign in to Codex with your ChatGPT account on April 14: • gpt-5.2-codex • gpt-5.1-codex-mini • gpt-5.1-codex-max • gpt-5.1-codex • gpt-5.1 • gpt-5

译当您在4月14日使用ChatGPT账户登录Codex时，我们将停用Codex中的旧版模型： • gpt-5.2-codex • gpt-5.1-codex-mini • gpt-5.1-codex-max • gpt-5.1-codex • gpt-5.1 • gpt-5

Hao AI Lab@haoailab · 3月18日65

http://x.com/i/article/2034009793598464000 # Into the DreamVerse TL;DR: Our new real-time inference stack in FastVideo enables Dreamverse, a prototype for a new interface where users can vibe direct their own “multiverse” of videos. AI video generation is already good enough to make a convincing clip. But real creative work is not about getting a clip in one shot. It’s about iteration. An idea appears, you test it: keep the subject, change the camera angle, continue the scene, and try again. The problem is that ideas move faster than generations. If every attempt takes minutes, the creative loop breaks; your imagination moves on before the video does. We think there is a better interface for AI video generation, which is why we created Dreamverse, an interface that enables a new workflow called vibe directing. Vibe directing is to video what vibe coding is to software. Instead of rewriting giant prompts from scratch, you talk to the system in natural language and steer the video through fast revision. Keep the subject, change the background, slow the camera, or anything else! Rather than jamming everything into a single prompt, iterate with multiple simple prompts. This kind of workflow is only possible when video generation is done in real-time. Current video generation models like Sora take 1-2 minutes to generate a 5s 1080p clip. We can do it in ~4.55 seconds on a single GPU. In other words, our inference stack in FastVideo can generate a clip faster than you can watch it. This capability completely changes the feel of video generation inference; it stops feeling like a passive experience and starts feeling like directing your own scenes. This allows us to create a longer 30-second scene that unfolds as a chain of these 5-second clips, while keeping a chat window open so you can keep directing in real time. This matters because serious video creation is almost never perfect on the first try. A shot may look off. Motion may break halfway through. Characters may drift between frames. In addition, creators may have multiple versions of a scene and want to play them out to determine which is better. In practice, creators are constantly making small adjustments and trying again. When revisions are slow, it’s much more difficult to explore many ideas. However, when the next result comes back almost immediately, it becomes possible to quickly try many ideas rather than just one. Better creative work comes from a faster feedback loop, not just a better model. We think this is where video generation is going: a way to direct the video as it unfolds. The best systems will not just generate impressive clips. They will let people explore ideas at the speed of their imagination. That is what vibe directing is all about. Step into the Dreamverse today with our demo. The Team Core contributors: Will Lin*, Matthew Noto*, Junda Su*, Yechen Xu*, Peiyuan Zhang* (* equal contribution) Contributors: Shao Duan, Minshen Zhang, Loay Rashid, Kevin Lin UI: Tina Mai Tech leads: Will Lin, Hao Zhang Advisors: Hao Zhang (corresponding), Danyang Zhuo, Eric Xing, Zhengzhong Liu Learn More - FastVideo Documentation - FastVideo Roadmap for 26Q1

译FastVideo团队发布Dreamverse原型界面，引入创新的“氛围导演”工作流。该模式允许用户通过自然语言实时、迭代地引导视频生成，如更换背景或调整运镜，无需编写复杂的长提示词。其核心是全新的实时推理栈，能在单GPU上以约4.55秒生成5秒1080p视频，速度快于观看时间，从而将生成过程从被动等待转变为实时导演体验。团队认为，视频生成的未来在于让创作速度跟上想象速度，快速的反馈循环比单纯追求模型性能更能催生优质作品。

DeepSeek@deepseek_ai · 2月27日59

🚀 Day 4 of #OpenSourceWeek: Optimized Parallelism Strategies ✅ DualPipe - a bidirectional pipeline parallelism algorithm for computation-communication overlap in V3/R1 training. 🔗 https://github.com/deepseek-ai/DualPipe ✅ EPLB - an expert-parallel load balancer for V3/R1. 🔗 https://github.com/deepseek-ai/eplb 📊 Analyze computation-communication overlap in V3/R1. 🔗 https://github.com/deepseek-ai/profile-data

译🚀 #开源周 第4天：优化的并行策略 ✅ DualPipe - 一种用于V3/R1训练中计算-通信重叠的双向流水线并行算法。 🔗 https://github.com/deepseek-ai/DualPipe ✅ EPLB - 适用于V3/R1的专家并行负载均衡器。 🔗 https://github.com/deepseek-ai/eplb 📊 分析V3/R1中的计算-通信重叠情况。 🔗 https://github.com/deepseek-ai/profile-data