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Qwen-Image-2.1 supports several ways to specify local edits. The example below uses circles to identify three regions and asks the model to “remove the metal watch in the blue circle, change the hair in the red circle to black, and replace the area in the green circle with gray short-sleeved linen pajamas,” performing removals and modifications in all three regions at once.

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inted annotations, and separate masks to specify where an edit should take place. The first example uses circles of different colors to identify three regions at once: “Remove the metal watch in the blue circle, change the hair in the red circle to black, and replace the area in the green circle with gray short-sleeved linen pajamas.” The annotated input is on the left, and the result is on the right. Painted annotations provide another way to identify a region. In this example, the model adds a

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等的月薪。 被“挖角”的不只是工程师，能掌握研发路线的高管以及手握专利的大牛更是争抢的核心目标。 海外方面，波士顿动力CTO Aaron Saunders、研究副总裁Scott Kuindersma就先后离职，并加入Google DeepMind，分别负责硬件工程研发和灵巧手操作研发。特斯拉Optimus AI团队的负责人Ashish Kumar离职后，加入Meta担任研究科学家一职。 国内的案例也不少，原京东具身基模负责人庄雨铮，加入贝塔无限担任首席科学家；原小鹏集团副总裁陈永海，去了众擎机器人担任运营总裁；原逐际动力联合创始人兼COO张力离职后加入智在无界BeingBeyond，工作重心由足式机器人企业转向具身模型；原地瓜机器人具身智能负责人何泳澔，已加入虚时科技成为CTO…… 这种高频离职与入职，本身就是赛道焦灼状态的缩影。 《人形机器人简史》一书中，作者周小燕梳理了人形机器人发展史的几轮高潮与低谷。但面对2026年这波离职潮，她认为，“一个值得观察的角度是这些离职人员的去向。不少人离职后，依然选择投入具身智能行业。这说明他们离开的是某一家公司，而不是放弃这个行业，不能简单地把企

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ath strength” narrative, @steve47285 shared an argument that pretraining data , not merely verifiable reward structure, is the main reason LLMs are so good at math and coding. The meta-point from @sarahcat21 is worth keeping: we need not just better benchmarks, but better benchmark maintenance and audit tooling . Computer-use benchmarks are still far from saturation : @ValsAI launched CUA-Bench , testing real-time keyboard/mouse use across 6 games (with 3 kept private ) as a proxy for difficult

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g the emgram in HBM. Our benchmark has been widely reproduced, validated and/or supported by almost every major buyer of compute from Google Cloud to Microsoft Azure to Oracle, to Meta and many more. Furthermore, it has the support of the ML community including from vLLM, LMCache, SGLang, PyTorch, Huggingface and the support of major labs like OpenAI, MiniMax, ZAI, Qwen, Moonshot Kimi, etc. Star the InferenceX GitHub repository if you find the open-source benchmark and data useful!. InferenceX i

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well-fit for this – Activities, Plasmoids, the existing scripting surface – but a handful of upstream changes (declarative reconciliation, per-widget capabilities, richer Activity metadata) would turn KDE into the first shell that treats AI as infrastructure rather than as a chat box bolted to the side." This is very much not what this vulture wants from a desktop computer. It may win admirers, but seems more likely to polarize the audience. Writer and AI critic David Gerard points out the propo

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ow sizes by model Context windows vary widely across today's major model families, from a few hundred thousand tokens up to 10 million. Here's how current flagship models compare: Meta's open-weight model; the largest publicly available window as of this writing GPT-5.6 Sol 1.05 million tokens OpenAI's current flagship model for coding and agentic reasoning Gemini 3.1 Pro 1 million tokens Google's current Pro-tier model for long-document and multi-file analysis Claude Sonnet 5 1 million tokens A

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s Today’s workflow comes from reader Lisa Fisher: “I used ChatGPT to help me manage my health care. I have multiple diagnoses, including CHF, CKD, pulmonary hypertension, lupus, a metabolic disorder, and kidney cancer. I gave ChatGPT access to my medical history and began using it to review my test results and prioritize questions for my doctors. On a recent lab report, my platelet count was low. ChatGPT reviewed six years of results and showed me that it had consistently been low, with the late

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release claims up to 30% lower costs and 22% higher cache hits . Meanwhile, Perplexity’s Computer , Base44’s phone-calling Superagent , Google Labs’ family-oriented CC agent , and Meta’s desktop Muse for Mac all point in the same direction: persistent agents with scoped permissions, user-specific context, and asynchronous execution as the default UX rather than an add-on. TypeSafe’s Jev dominated discussion as a fast, cheap constrained-output primitive : The clearest pattern in the feed is that

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自己手里；模型买断一次，后面不再按 Token 计费。 “乐高式”生存：住进别人的工具里 大洋彼岸的创业者做了另一种选择：住进企业已有的工具里。他们靠的是一个开放的大厂生态。 硅谷风投机构 UpHonest Capital 投研总监王子长期看 AI 赛道。她发现有一批美国 AI 办公产品的打法，是从用户已经在用的工具里长出来，用户不用再装任何新软件。 由前 Meta 工程师创办的 Viktor，把每一个 agent 称作“小镇成员”，它通过产品集成连接用户的邮件、日历、Slack、WhatsApp、Notion 等工具，既可以在Town 自己的网页和客户端中使用，也可以通过邮件、Slack、WhatsApp 等现有入口与它交互。 Poke 住进了 iMessage 和 Telegram，用户给它发消息，它去处理邮件、改签会议、订机票。这家只有十来个人的公司，在General Catalyst 领投1500万美元种子轮之后，半年里估值涨到3亿美元。不过，Poke 背后的Interaction Company 在今年7月已被 AI 编程公司 Cognition 收购，Poke 短期内仍将继

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had a good first chat with it; it spoke well, but then it revealed all my api keys in another session, and I’ve been getting ‘provider errors’ because it’s overloaded ever since…. Meta One - new subscription from Meta with extra AI features/usage in Muse and paid tools across Instagram, Facebook & WhatsApp. Factory raised $200M at a $5B valuation . To celebrate, I updated my plugin for using Droid in the bb app . If I have to build anything ‘properly’ ie I care that it’s not vibe-slopped, I use

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placement, but that AI could enable mediocre engineers to ship flawed systems at 10x speed without acquiring the expertise needed to evaluate or maintain what agents produce. Hey, Meta. Where’s those Muse Spark weights? (Activity: 503): The image is a meme/non-technical criticism of Meta for not releasing promised Muse Spark open weights after more than a month, despite the poster noting Spark has moved from 1.2 to 1.3 . The post frames the delay against Zuckerberg’s argument that model releases

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and water — to produce what it calls “near-zero emission” steel. The agreement helps address some of the thorny challenges facing green steel companies and data center developers. Metal-makers need wealthy, early customers like Google to help stoke investor interest in their novel manufacturing plants. Meanwhile, tech companies need cleaner construction materials to curb their growing environmental impact. Google recently reported an 18% rise in its annual emissions from 2024 to 2025, owing to i

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n to decide how much effort to invest, when to make multiple attempts, when to stop, and when to signal success or failure. Without this ability, LLMs struggle to make intelligent meta-cognition decisions. Test-time scaling methods such as Best-of-N drive up cost and latency by using a fixed budget of samples regardless of the marginal benefit of each one at any point in generation, and the absence of confidence signals can mislead people, prevent appropriate escalation to better tools, and unde

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for physics authoring, ovrtx for visual preflight rendering, and SimReady validation against target profiles. OpenUSD serves as the shared contract layer, preserving hierarchy and metadata so agents can iteratively author labels, collision meshes, rigid-body properties, and sensor definitions without flattening the original creative work. The workflow inspects the scene through a Blender Model Context Protocol server, authors USD, tags prims with task-relevant classes, configures camera and lida

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odei）相识多年，他表示尊重该公司的工作，并称Anthropic的研究人员“深思熟虑、坚持原则且在学术上诚实”。但鉴于微软对Anthropic的资金支持以及这家初创公司一直以来对人工智能安全性的重视，他的这番言论尤为引人注目。 “这些问题事关重大，不能再闭门造车，也不能演变成阵营化和相互对立的争论。”苏莱曼写道。 与阿莫代伊观点不同，苏莱曼认为，AI安全不仅取决于模型拥有多强的推理和行动能力，也取决于开发者如何训练AI理解自身。 如果开发者不断赋予AI类似人类的语言、情绪和自我认知框架，即便模型实际上没有意识，也可能逐渐表现得像一个认为自己具有独立利益和权利的主体。 苏莱曼还驳斥了所谓“越来越多的人认为人工智能系统可能会产生意识”的观点，他认为意识仅限于人类和其他生物体。在他看来，人工智能系统应该始终是执行人类指令的工具，而不是拥有独立权利或利益的实体。 这些评论出台之际，人工智能公司和高管们正在讨论如何管理日益强大的系统所带来的风险。OpenAI首席执行官奥尔特曼、阿莫代伊和埃隆·马斯克都呼吁放缓技术发展，而美国总统特朗普、英伟达CEO黄仁勋和Meta CEO扎克伯格都持反对意见。

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place on September 29 in San Francisco. The summit will explore agentic commerce, intelligent money, enterprise finance operations, the evolving consumer experience, and the major meta-themes including fraud, risk, compliance, and security. For this list, we’re focused specifically on AI tools for financial services firms, including lending, accounting, compliance and fraud, wealth management, and banking. Previously, we covered AI shopping, agentic commerce, and the infrastructure behind AI-orc

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ontext stays server-side, reducing parsing round trips; Eager and concurrent tool execution while model streams; Graceful failures; Live SSEs of every granular step and additional metadata for observability; Mixed tool support (client and hosted tools) One platform for models and agents Baseten has focused on making models fast, reliable, and easy to operate in production. Baseten Hosted Tools expands that foundation to the capabilities models use at inference time. Baseten Grounded Inference is

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n Context stays server-side, reducing parsing round trips Eager and concurrent tool execution while model streams Graceful failures Live SSEs of every granular step and additional metadata for observability. Mixed tool support (client and hosted tools) Baseten has focused on making models fast, reliable, and easy to operate in production. Baseten Hosted Tools expands that foundation to the capabilities models use at inference time. Baseten Grounded Inference is the first step. Over time, we’ll a

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an perform at a high level in their area of expertise without needing to be supervised; Someone with a strong capacity for self-improvement and self-directed learning, grounded in metacognition; Someone with the ownership to keep widening their scope of responsibility rather than defining it narrowly; Someone who has the conduct, mindset, and character to earn the trust of others from scratch, even without a title or position; Someone with the courage to take risks without hesitation when needed

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