全部 AI 动态

http://x.com/i/article/2049579443216338944

The Codex App Server is massively underrated. You can inject Codex-level intelligence into any platform using your ChatGPT account. I embedded it into Chrome… and it works flawlessly. And yes… it’s 100% open source. https://github.com/GENEXIS-AI/chromex

Codex is not like claude code. if you know the limit is going to end, like last 10 to 8%, give an very long run task, and even after the limit got ever, it will continue to do the task until the task was completed. Shout out to @OpenAI team.

Today we’re releasing Laguna XS.2, Poolside’s first open-weight model. It’s a 33B total / 3B active MoE model built for agentic coding and long-horizon tasks. Trained fully in-house on our own stack. Runs on a single GPU. Released under Apache 2.0. Links 👇 Weights: https://huggingface.co/poolside/Laguna-XS.2 API: https://platform.poolside.ai/ Blog: https://poolside.ai/blog/laguna-a-deeper-dive

28 分钟学会 95% 的 Codex 这 7 项知识工作能力…… 就在 Codex 这个超级应用里 00:00 简介 02:19 能力 1 - 完整文件访问 07:41 能力 2 - 持久记忆 10:46 能力 3 - 插件 13:52 能力 4 - 技能 19:22 能力 5 - GPT 图像访问 21:03 能力 6 - 浏览器与计算机使用 23:58 能力 7 - 自动化 25:31 附加功能 - Chronicle 27:21 总结

Introducing the /browser-trace skill, Give your agent 100% observability into its browser: dump network requests, DOM content, screenshots, and CDP logs into a searchable filesystem. Great for reverse engineering, autoresearch loops, and monitoring the situation ™️.

just updated ui-skills: It's now a collection of the best skills for design engineering

GPT-5.5 Pro achieves a new high score of 159 on the Epoch Capabilities Index! ECI is our statistical tool that combines multiple benchmarks into a unified scale.

THE 64 MINUTE OPENAI CODEX MASTERCLASS IS HERE if you've been meaning to learn Codex, this is the episode for you, we cover: 1/ Claude vs Codex (what's better?) 2/How to run Claude Code inside Codex (really cool) 3/Why GPT 5.5 changed everything for browser agents 4/How to create skills (reusable agents you build once and call by name forever) 5/How to connect Notion with surgical permissions (one database, not the whole workspace) 6/How to use Remotion inside Codex to create videos 7/ How to one-shot full mobile apps in Swift 8/The 4 projects to run on day one to get up to speed fast shoutout to @rileybrown for coming onto @startupideaspod for the 5th time. every episode he delivers. this one is the Codex masterclass. if you've been meaning to learn Codex, this is the episode. 100% free i just want to see you build your ideas. http://youtube.com/watch?v=LWx4FGam2aQ&t=1804s watch

An important message from @thsottiaux

gpt-5.5 prompt for codex seems to have a duplicated line trying to get it to not talk about creatures? Never talk about goblins, gremlins, raccoons, trolls, ogres, pigeons, or other animals or creatures unless it is absolutely and unambiguously relevant to the user's query. [...] Never talk about goblins, gremlins, raccoons, trolls, ogres, pigeons, or other animals or creatures unless it is absolutely and unambiguously relevant to the user's query gh link: https://github.com/openai/codex/blob/main/codex-rs/models-manager/models.json#L55

GPT 2 is totally insane… 🙀⚡️ I asked for a prehistoric predator and it built an entire museum around it. This is not just an image. It feels like discovering history.🤯 Prompt Drop ⤵️

Lately I've been having fun with running coding agents fully locally. The setup I landed on is: - Pi agent - Gemma 4 26B A4B - Server of choice: LM Studio/Ollama/llama.cpp I wrote a step-by-step guide with instructions on how to set it up: http://patloeber.com/gemma-4-pi-agent/

我很好奇，在给出同样详细的提示来制作数学讲解信息图时，新的 ChatGPT 图像模型在输出上会有多大差异。 结果：差异相当大！如果这对你很重要，不妨多生成几次，即使第一次看起来很棒。

You can try this: Turn any photo into a beautiful woodcut/linocut style, GPT Image-2 does a great job with details, expressions. Perfect thing for the profile picture or the family photo. Or why not a gift? Try for yourself, full prompt below ⤵️

Age-worn, damaged images can now turn into 4k with just one prompt using Chatgpt. & Its Free 💸 Made on Chatgpt, GPT-2 Prompt: 👇🏻

Age-worn, damaged images can now turn into 4k with just one prompt using Chatgpt. & Its Free 💸 Made on Chatgpt, GPT-2 Prompt: 👇🏻

ChatGPT Images 2.0 explains “Tenet” in a simple way!

GPT Image 2 on ChatGPT Prompt Create a visually rich infographic about an endangered animal. Start by finding one online, research its habitat, diet, and unique traits. Present information through annotated visuals and structured callouts, not generic sections. Style it like a bold graphic illustration: a detailed, photorealistic central animal as the focal point, supported by diagrams, callouts, and concise text elements. Use clean backgrounds and a mix of photorealism with strong graphic elements (shapes, icons, color blocking) in a layered composition. Make it dense, tactile, and professionally authored.

Nano banana 2: "Show me a photo taken of pages 113-114 from the books": "Eldritch Horrors as Pets: A Guide" "How Womblenauts Work" "Photographs of the People of New York Who Look Like Birds". "Cakes shaped like fish shaped like cakes"

This is one-shot assembly: you show examples of what to build, and the robot just does it. (see original post: https://generalistai.com/blog) To share more on how this works, the robot is controlled in real time by a neural network that takes in video pixels and outputs 100Hz actions. The video below is part of the raw input passed directly into the model. I also like this view (at 1x speed) because it shows more of the (I think very cool) subtle moments of dexterity near the fingertips 👌 One-shot assembly seemed like a dream even just a year ago — it's not easy. It requires both the high-level reasoning of "what to build" (recognizing the geometry of the structures presented by the human), and the low-level visuomotor control of "how to build it" (purposefully re-orienting individual pieces and nudging them together in place). While possible to manually engineer a complex system for this (e.g. w/ hierarchical control, or explicit state representations), we were curious if our own Foundation model could do it all end-to-end with just some post-training data. Surprisingly, it just worked. Nothing about the recipe is substantially different than any other demo we’ve run in the past, and we’re excited about its implications on model capabilities: • On contextual reasoning, these models can (i) attend to task-related pixels in the peripheral view of the video inputs, and (ii) retain this knowledge in-context while ignoring irrelevant background. This is useful for generalizing to a wide range of real workflows: e.g. paying attention to what’s coming down the conveyor line, or glancing at the instructions displayed on a nearby monitor. • On dexterity, these models can produce contact-rich "commonsense" behaviors that can be difficult to pre-program or write language instructions for e.g. rolling a brick slightly to align its studs against the bottom of another, re-grasping to get a better grip or to move out of the way before a forceful press, or gently pushing the corners of a brick against the mat to rotate it in hand and stand it up vertically (i.e. extrinsic dexterity). These aspects work together to form a capability that resembles fast adaptation — a hallmark of intelligence, relevant for real use cases. This has also expanded my own perspective on what's possible with robot learning, using a recipe that's repeatable for many more skills. This milestone stands on top of the solid technical foundations we’ve built here at Generalist: hardcore controls & hardware, all in-house built models, and a data engine that "just works." We're a small group of hyper-focused engineers, and hands-down the highest talent-density team I’ve ever worked with. We're accelerating and scaling aggressively towards unlocking next-generation robot intelligence. Building Legos is just one example, and it's clear to me that we're headed towards a future where robots can do just about anything we want them to. Its coming, and we're going to make it happen.