全部 AI 动态

MiniMax-H3 + ORB360 LoRAで画角を自由に動かせる漫画を作ってみた。 キャラを参照画像で渡せば、顔も固定できる。

Introducing Natura-1, our first general foundation model for the physical world. It follows natural-language instructions zero-shot and turns them into action with dexterous hands. Natura-1 is a 50-billion-parameter mixture-of-experts (MoE) model pretrained on one million hours of egocentric human experience. On top of this foundation, we conduct efficient post-training on a limited amount of robot data to adapt pretrained world knowledge to dexterous manipulation, yielding a model that generalizes to a wide range of unseen scenarios. Natura-1 follows complex natural-language instructions zero-shot, reasoning through spatial constraints, multi-object relationships, and negative conditions. It can also autonomously carry out long and continuous workflows to perform long-horizon executions. Natura-1 provides early evidence that generalization—and even emergent behavior—can coexist with the complexity of dexterous hands. Further progress depends on the full stack: infrastructure for faster training and real-time inference, pretraining data at greater scale and coverage, and models that transfer to robots with less adaptation. We expect it to be a long-term research and engineering effort.

Cheeky Pint is back! Mark Zuckerberg @finkd and Alexandr Wang @alexandr_wang came to the pub to discuss their new frontier model, Muse Wonder. 00:00:24 Muse’s breakout use cases 00:07:28 The model is the product 00:14:35 Muse Wonder 00:24:56 How frontier models are built 00:48:26 Betting $100B 01:07:21 Agentic commerce 01:19:34 OpenRouter 01:20:06 Beyond the smartphone 01:27:00 Funding 01:34:06 Why AI won’t be winner-take-all 01:40:46 Meta Superintelligence Labs (MSL) 02:08:11 Working for Mark Zuckerberg

The IMF just published its latest forecasts and it assume no extra productivity growth from AI, and the report finds that economy-wide data still show little effect on wages, jobs or measured productivity, despite gains on specific tasks. > The surge in AI hardware trade comes mostly from higher prices rather than more units shipped, and rising memory chip costs are already pushing up core-goods inflation in some rich economies. > In the IMF’s models, an AI bust costs the world economy almost twice what an AI boom adds: 1.1% of output lost by 2028, against 0.6% gained by 2029. > AI hardware trade has roughly tripled in value since 2019 because prices rose, not because more was shipped, with Taiwan and Korea supplying 62% of net exports and the US and China taking over 75% of net imports.

The IMF just published its latest forecasts and it assume no extra productivity growth from AI, and the report finds that economy-wide data still show little effect on wages, jobs or measured productivity, despite gains on specific tasks. > The surge in AI hardware trade comes mostly from higher prices rather than more units shipped, and rising memory chip costs are already pushing up core-goods inflation in some rich economies. > In the IMF’s models, an AI bust costs the world economy almost twice what an AI boom adds: 1.1% of output lost by 2028, against 0.6% gained by 2029. > AI hardware trade has roughly tripled in value since 2019 because prices rose, not because more was shipped, with Taiwan and Korea supplying 62% of net exports and the US and China taking over 75% of net imports.

NEW GROK BOT! AI MEMORY TRANSFER! Your old AI knows you. Your new one shouldn't have to start from zero. 🧠➡️ Meet Memory Transfer, a @Grok @Bot that brings over what Claude or ChatGPT learned about you, so you don't spend weeks re-explaining yourself. There are two ways to bring it over: • Paste one prompt into your old chat and send me the answer. • Or drop in your full ChatGPT or Claude data export. The part that matters most is that nothing gets in automatically. We review every item together, one line at a time. Only what you keep gets saved, and each item is tagged with where it came from and when. Other AIs' guesses don't become facts, and nothing you've already saved gets overwritten unless you say so. Switch assistants and keep your context, without the junk. Try Memory Transfer on Grok Bot today. https://x.ai/bot/Ww-RXj-lLqpg_oWqzccKZ

Meta Muse's retention is out, and it's almost as good as ChatGPT! It's higher than ChatGPT at D7 (>30%) and just lower at D14 (22%). Not quite in the elite D14 tier of WA (67%) YT (59%) Insta (54%) or TikTok/Snap (37%), though. Muse is one of the highest retaining AI products!

用 AirDrop 传专业设备拍的超大照片和视频（动辄 20GB）一直很痛苦。 更难受的是 iPhone 的“文件”和“相册”之间几乎不互通：在“文件”里解压一个大包，想全部存进相册，没有进度，还经常闪退，存一半都不知道存到哪了。 这两天啵啵自己把它解决了。她完全没写过代码，让 Dots 操控云端电脑写了个 App，拿到她的 Mac 上，用数据线直接装到了自己手机上。没有 Apple 开发者账号，整个 App 只有 66KB。 功能很简单：把超大的 ZIP 解压之后一张一张存进相册，能看到进度，中断了还能接着存，已经存过的会自动跳过。 刚才实测 953 个文件、15.32GB，4 分钟左右全部存完。 以前遇到这种事儿只能忍或者不断自己再试试，现在不会写代码的人也能花两天给自己做个工具了，今年 vibe coding 的发展可以说是真的震撼。

THAT'S F*CKING INSANE, GROK BOT JUST PLANNED MY TRIP TO LISBON AND BOOKED IT WITH MY CARD. I ONLY HAD TO ASK. IT HANDLED THE RESEARCH AND THE BOOKINGS. Elon Musk recently used Grok Bot to buy LEGO for his kids. That made me want to test something bigger: whether it could fully plan, research, and book my trip to Lisbon on its own. Grok Bot searched for travel options, compared prices, and booked what I needed using my card and the details I provided. Here’s what it handled: → Researched options for my Lisbon trip. → Compared prices and picked the best-priced options it found for my request. → Completed the bookings using my travel and payment details. My trip was booked. Think about how much time usually goes into this: searching, comparing options, checking availability, and filling out booking forms. This time, I gave that job to Grok Bot. That’s what makes this interesting to me. A task that normally takes my time and attention ended with real bookings after I asked an agent to handle it. Now I want to see what else I can take off my to-do list. I covered the latest major Grok Bot updates and more ways to use them in my article below. Bookmark this and read the full breakdown ↓

今天，@TheLancet 发表了我们的 AMIE 研究，与 @BIDMC_Medicine 合作完成，这是首个在真实诊所中面向患者的对话式诊断的前瞻性研究。 AMIE 是一个研究系统，人们可以在就诊前与之聊天。在实验室之外，用真实的急诊护理患者测试 AMIE，展示了技术如何帮助安全地收集病史。临床医生报告称，在 75% 的病例中，这些摘要帮助他们为就诊做准备，并在超过一半的病例中影响了他们的诊疗方式。AMIE 的鉴别诊断也在 90% 的情况下与医生的最终诊断一致，让临床医生有更多时间专注于患者护理。 这是 Google 有史以来第一篇发表在 The Lancet 主刊上的论文！

我们已统一了 Grok、Cursor 和 X Premium 的订阅方案。 隆重推出 The X Subscription。

我们已统一了 Grok、Cursor 和 X Premium 的订阅方案。 隆重推出 The X Subscription。

我们已统一了 Grok、Cursor 和 X Premium 的订阅方案。 隆重推出 The X Subscription。

我们已统一了 Grok、Cursor 和 X Premium 的订阅方案。 隆重推出 The X Subscription。

我们已统一了 Grok、Cursor 和 X Premium 的订阅方案。 隆重推出 The X Subscription。

我们已统一了 Grok、Cursor 和 X Premium 的订阅方案。 隆重推出 The X Subscription。

我们已统一了 Grok、Cursor 和 X Premium 的订阅方案。 隆重推出 The X Subscription。

我们已统一了 Grok、Cursor 和 X Premium 的订阅方案。 隆重推出 The X Subscription。

This is how we built the filesystem behind Perplexity Projects as a Git forge for agents. Each agent works in its own sandbox and commits to a shared repository where people and other agents can build on its work across sessions. Read more: https://www.perplexity.ai/hub/blog/building-shared-workspaces-for-people-and-agents

Your API is now your product, and it needs to move as fast as you do. Introducing Restless. Every call, support question, pull request, and error message leaves your API better for the next agent.

We're launching our leaderboard for FrontierCode 1.1, a private evaluation from Cognition that tests models on completing coding tasks in a way that would actually get merged by maintainers of real-world repositories @cognition announced FrontierCode in June 2026 and updated it to version 1.1 in July. Cognition built the dataset with open source maintainers who spent more than 40 hours per task. Each task requires the agent to make a change to a real open source repository, then checks both unit tests and qualitative tests for what maintainers would require to merge the change. We run FrontierCode 1.1 Main, the primary 100-task set of the benchmark's 150 tasks, using our Stirrup open source reference harness for apples-to-apples comparisons across models. All tasks are private to avoid contamination. Each attempt is graded with both blocking and non-blocking criteria. An attempt that fails any blocking check scores zero, and one that passes them all scores between 70% and 100%. Unlike other coding evaluations, FrontierCode has a more holistic view of success based on real usage. Models need to pass unit tests, but are also assessed on behaviors like making excessive changes and large diffs, modifying files outside the relevant scope, or adding unhelpful tests. We think this approach is valuable in the current context: models are now very capable at coding tasks, but their changes can still require substantial cleanup before a maintainer would merge them. Key takeaways: ➤ Leading models still fail more than 40% of the time: Claude Opus 5.5 (max with fallback) leads at a score of 52% after passing blockers in 58% of its attempts. Nine of the 100 tasks were not passed in any attempt by any of the models we evaluated for launch ➤ The top of the leaderboard is tightly packed: nine models from six labs score between 43% and 52%. MiMo-V2.6-Pro is the leading open weights model at 45%, followed by DeepSeek V4.1 Flash (max) and Kimi K3 (max) at 44% ➤ GPT-6.1 Sol (max) offers the strongest performance for its cost: At $0.81 per task it scores 49%, matching Claude Fable 5.1 (max with fallback). This means it achieves similar performance to leading Anthropic models, at less than a tenth of the cost per task Thank you to @silasalberti, @ybenpan, @cl571128, and the rest of the team at Cognition for building FrontierCode and collaborating with us to bring it to Artificial Analysis - we’re working to add FrontierCode to our coding agent leaderboard very soon.

Muse Wonder will soon power experiences in @Muse , and rolled out first to subscribers earlier today. I’ve been using it in my own Muse for a little while and appreciate how this model works harder and draws on memory more precisely with Active Focus and Active Memory.

Introducing SubQ Snap, the first long-context decision model for real enterprise work. Snap is the only decision model or LLM where compute per token stays constant as context grows. • 384K tokens of multimodal context, 12x larger than Jev. • 61.7 on Decision Index v0.2.1 vs Jev’s 57.9, outperforming on 13 of 16 enterprise benchmarks. •Processes 100K tokens and returns a decision in under one second. •$0.05/MM input tokens. Output is free. Available today to anyone. The first 10,000 sign-ups get 100M free input tokens. We’ll likely hit capacity fast and will add users as we add compute. Compute offers welcome 🙂. Latency will be variable as we tune the platform to this new model and category of users. Model speeds are 80ms for minimal context, 250ms for 32k, 660ms for 100k, and 2.5 seconds for 384k. Visit: https://subq.ai/

introducing muse wonder our new frontier model. state-of-the-art at personal agents and professional agents. our largest and most capable model yet—rivals the big bois

我们很高兴推出 Muse Wonder 系列，包括 Muse Wonder 和 Muse Wonder Full，这是我们迄今为止最强大的模型。两者都是前沿模型，旨在为个人和专业代理任务提供最先进的性能。 Muse Wonder 今天可通过 Meta Model API 和 Muse Code 使用。Muse Wonder Full 今天面向 Muse Code 订阅者提供。Muse Wonder 很快还将为我们的个人代理产品 Muse 中的体验提供支持，首先向订阅者推出。

introducing muse wonder our new frontier model. state-of-the-art at personal agents and professional agents. our largest and most capable model yet—rivals the big bois

introducing muse wonder our new frontier model. state-of-the-art at personal agents and professional agents. our largest and most capable model yet—rivals the big bois

MUSE WONDER 🍉 this model was developed to power personal agents. three highlights: - ACTIVE MEMORY: best memory across days/weeks/months - ACTIVE FOCUS: gets your tasks done - DOMAIN EXPERTISE: sota in finance & legal, strong in health