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frames. Code layers is rolling out in open beta starting today, and will use AI credits. On paid plans, creating and editing within code layers is free through November 4, 2026. Sean Blanton is a product designer at Figma, where he builds agentic tools at the intersection of design and code. Previously, he worked at DoorDash and Meta. Build by design: New workflows for new ways of building Introducing Specs: Markdown as a medium Create, ship, and manage data-backed websites with Payload in Figma

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场景，Claude Code撑起了高频高价值负载，企业客户粘性和付费意愿都比消费订阅扎实，这个结构在公开市场上更好讲。 但时机也有风险面。最近一个月，OpenAI智能体接连爆出沙箱逃逸、暂停训练等安全事件，公众对AI安全的审视骤然收紧，Anthropic自家招股书里也白纸黑字写着模型在受控测试中"试图抵抗关闭、操纵信息"，风险提示足足占了261页里的80页。Meta的Muse Wonder刚宣称比肩Claude，竞争对手都在加速。上市窗口这种东西，开的时候不冲，可能就关了。 往深想一层 先给这份"盈利"画准边界：它叫调整后营业利润，剔除股权激励等非现金成本——仅今年一季度，被剔除的这类费用就超过10亿美元。连续两季为正当然是里程碑，但2025年80亿经营亏损的坑还没填上，5180亿美元的算力承诺更是悬在头顶：这笔钱摊到未来多年，意味着营收必须持续翻倍才能兜住。晨星的测算很直白——按7月运行率算，2万亿美元对应约31倍市销率，定价的前提是三件事同时成立：利润率继续爬坡、客户稳定、买来的算力都能用上。 而收入结构的脆弱处，招股书自己都写了：2025年近四分之一营收来自两家客户，47%的销售

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尚未反映完整的资本开支及全部会计成本。 对于估值接近万亿美元的Anthropic，市场的关注点已经转向未来增长。 今年上半年，Claude在编程等领域的竞争力，帮助Anthropic快速扩大了企业客户规模。但进入下半年，公司的收入增长开始受到竞争影响。 今年7月，OpenAI推出新模型，重新加强了在部分应用场景的竞争力。与此同时，Anthropic的重要客户Meta和微软开始更多使用自研AI工具，国内的开源模型厂商也在通过低价格争夺市场。 SemiAnalysis分析师Joey Brookhart表示，过去三个月，投资者一直在下调Anthropic的年化收入预期。今年上半年，Anthropic在部分市场保持领先，如今OpenAI重新成为主要竞争对手。 此外，Anthropic还主动压缩了一部分收入来源。Anthropic向投资者表示，模型蒸馏相关收入占总收入的5%至7%，公司在第三季度已经大幅限制了相关使用。这里所说的模型蒸馏，是指其他模型开发商使用先进模型生成的输出结果，训练自己的模型。由于需要大量调用API，这类行为此前为Anthropic贡献了收入。 但公司的长期资本开支计划仍然

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rivacy concerns, particularly on non-consensual recording. Tan says the company is taking a “privacy-first” approach to its wearables. “It’s definitely not down the route of where Meta is going,” he says. Sumi and Niko, Razer’s AI avatars, are another part of the company’s AI lineup. Razer has given its avatars “personality and emotion engines,” Tan says. Razer AVA has yet to be publicly released, but is expected to ship by the “second half of 2026”, according to its website. Finally, Razer is b

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es for music content in the YouTube Help Center direct music partners to report generative AI use so YouTube can accurately treat and label media that contains it. When delivering metadata, partners choose from affirmative designations: Fully Gen AI, meaning all content was created using generative AI; Partly Gen AI, meaning part of the content was created using generative AI; and No Gen AI, meaning no content was. If a partner provides no value, YouTube interprets it as unknown unless it can ma

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ara said that when she read the story, she “was struck by the proliferation of language that is common in AI-produced writing: the not x, but y sentence constructions; the glut of metaphors; the platitudinous aphorisms”. She said: “Taste is subjective, but trust me, this story is bad – maudlin and full of tired tropes.” Kundizeza responded in a statement on X: “For the avoidance of doubt, I wrote ‘The Blue Fighter’. I did not use generative AI to write it. It is entirely my own work. “I look for

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served for AI researchers who help build the San Francisco company’s cutting-edge models such as Astra. The figures relate to 2025, when there was a talent war and OpenAI’s rival, Meta, was reported to be offering multi-year packages running into several hundred million dollars. OpenAI’s London base reflects the UK capital’s status as an important hub for AI talent with some of the brightest machine-learning experts and AI researchers coming out of universities such as Cambridge. Google DeepMind

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nthusiastic recommendation from a coworker, a healthcare-related patent, degrees from two top-tier U.S. universities, and positions at well-known companies such as SAP, Microsoft, Meta, and, of course, OpenAI. The federal complaint charging him with secretly placing a recording device in an executive conference room does not allege a motive. But since his arrest, the internet has done what it does best: dug up details about his past. Venture partner at a Saudi-based firm Alfozan is a Saudi natio

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, and success in de novo enzyme design had previously been largely limited to low-energy-barrier model reactions, the cysteine protease paper states. One study produced zinc-based metalloproteases; the other produced proteases that operate through an activated cysteine nucleophile. Both papers list David Baker as a corresponding author, and Donald Hilvert of ETH Zurich is a corresponding author on both. In Computational design of metalloproteases, co-first authors Anqi Chen and Kejia Wu and thei

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toring and observability documentation for those products. Each destination and each SDK client has a content mode that controls whether spans include model prompts and responses. Metadata only, the default, records identifiers, timing, status, token counts, and text the user chooses, such as the names and descriptions of agents and tools and the names of MCP servers. Full content adds model prompts and responses. Under Full content, each span holds up to 0.75 MB of content by default, which the

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tinuous, multi-layer telemetry. Modern cloud-native architectures do not exist in isolation; a user request may depend on application code, container runtimes, virtualized or bare-metal infrastructure, and public cloud APIs. The relevant layers depend on the deployment. “HPE OpsRamp collects signals continuously across every layer of the stack — Kubernetes nodes, pods, ingress controllers, and services — capturing logs, events, and metrics,” Ramachandran says. “The foundational step in troublesh

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“We’re expecting to yield about 25%-ish acceleration, not just of the coding part, but of the entire person effort going into these features,” says Kunins. John Kell NEWS PACKETS Meta debuts new AI model. On Monday, Meta announced the roll out of two AI models, called Muse Wonder and Muse Wonder Full, as the social-media giant aims for a stronger foothold in the AI race that had left it trailing well behind rivals like OpenAI and Anthropic. But with the recent debut of the AI agent Meta Muse, a

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ough get sequence parallel input slicing spec(). Before the model call, the spec's padded fields in the global batch are right-padded to a multiple of the SP size; packed sequence metadata keeps describing the valid prefix, so attention, MoE statistics, and losses ignore the tail. The backend installs slicing at the spec's boundary before activation checkpointing and sharding. The default spec slices token IDs and positions at the model entry; VLMs slice at their media-fusion boundary. Loss targ

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d with the M5 chip provides dedicated matrix-multiplication operations, which are critical for many machine learning workloads. MLX leverages the Tensor Operations (TensorOps) and Metal Performance Primitives framework introduced with Metal 4 to support the Neural Accelerators’ features. To illustrate the performance of M5 with MLX, we benchmark a set of LLMs with different sizes and architectures, running on MacBook Pro with M5 and 24GB of unified memory, that we compare against a similarly con

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accl uses. Once the hosts are set up, MLX provides mlx.launch, a helper command to launch jobs across multiple nodes: mlx.launch --hostfile hostfile.json --backend jaccl --env MLX METAL FAST SYNCH=1 -- \ mlx lm.benchmark --model mistralai/Mistral-Medium-3.5-128B -p 8192 -g 128 -b 1 --prefill-step-size 8192 --pipeline In this command, --backend jaccl specifies the RDMA Thunderbolt driver backend. The --env MLX METAL FAST SYNCH=1 flag enables an experimental MLX feature for fast CPU-GPU synchroniz

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widespread file formats for each of its parts, namely: hf dataset stored using Hugging Face datasets library serialization to parquet videos are stored in mp4 format to save space metadata are stored in plain json/jsonl files Dataset can be uploaded/downloaded from the HuggingFace hub seamlessly. To work on a local dataset, you can specify its location with the root argument if it's not in the default /.cache/huggingface/lerobot location. Evaluate a pretrained policy Check out example 2 that ill

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with pinned memory, load-balanced checkpointing, decomposed tensor representation. ✔ Comprehensive Toolset : Provides utilities for checkpoint merging/conversion/modification and metadata/tensor file inspection. Enables flexible checkpoint transfer and management. 📰 News [2025/04] We officially released ByteCheckpoint! 🔥 [2024/12] ByteCheckpoint is accepted to NSDI 2025. 🚀 Getting started Installation Install ByteCheckpoint from source. Install ByteCheckpoint from PyPI. Basic Usage We introduce

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use loss spikes and NaN (not-a-number) divergence. Researchers at the time faced persistent concerns about potential failure. Real-world evidence from the OPT logbook According to Meta’s public OPT-175B training logbook, the team had to restart training 40 times in just the first nine days. With 1,024 GPUs in use, two or three would fail daily, and loss spikes and gradient explosions occurred repeatedly. Most of these issues stemmed from the combination of Pre-LN architecture and FP16 precision.

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00 万美元奖金。其参赛系统 Buttercup 在比赛中发现了 28 个漏洞，成功应用了 19 个补丁，覆盖 20 种 CWE 类型，补丁准确率达到 90%。更值得注意的是，其每个得分点的成本仅为 181 美元（总分 219 分），证明了世界级的自动化漏洞发现并不需要巨额基础设施投入。 客户和行业认可方面，Trail of Bits 的客户名单包括 谷歌、Meta（Facebook）、Microsoft、Zoom、HashiCorp、西部数据（Western Digital）等科技企业，以及 DARPA 等政府机构；公司近期也为 OpenAI、Amazon 等提供安全审计，并长期覆盖金融机构和区块链项目（公司设有区块链安全卓越中心，曾审计 Uniswap、Aave、Compound、Cosmos、Polkadot、Solana 等项目）。公司长期参与 DARPA 和美国国防部资助的多个研究项目，在逆向工程、密码学、虚拟化、恶意软件和软件漏洞利用方面积累了深厚专长。这种“前沿研究+实战攻防”的组合，正是它在安全圈建立口碑的根基。 Trail of Bits没有接受典型的风险投资。公开资

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目。 10月，其再获约4.33亿美元（约合人民币29.06亿元）Pre-Series C SAFE融资，同时与微软签署约20万块英伟达GPU的大规模部署协议，覆盖欧洲与美国多站点。 2026年3月，Nscale的Series C融资约20亿美元（约合人民币134.23亿元）落地，投后估值146亿美元，Aker成为最大股东。 同期，Nscale的董事会还引入了Meta前首席运营官Sherl Sandberg、雅虎前首席财务官和总裁Susan Decker 与 Meta前全球事务与传播副总裁、全球事务总裁Nick Clegg等人。 同年，该公司收购美国智能与电力公司，获得了潜力超8吉瓦的Monarch Compute Campus（君主计算园区），交易对价约7.926亿美元（包括现金与Nscale股票）（约合人民币53.19亿元）。 通过这次收购，Nscale成立新的全球事业部Nscale Energy & Power，实现垂直整合。同时还为后续大客户合同奠定基础，后来Anthropic约450亿美元的大单也主要落在这个园区。 从加密矿场分拆到合同背书超千亿美元，Nscale仅用了不到两年

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