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e company also plans to release a new version of Haiku — its smallest model — in the coming weeks, although it didn’t give a firm date as to when that would happen. The last year has seen a flurry of new model releases from the major AI labs. Just last week, OpenAI released a number of new models — including enhanced versions of Sol and Luna, its mid-tier and budget-friendly models. Meta also announced a new model , which it said would power an upcoming feature associated with its smart glasses.

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ices aimed at under-16s. (SCMP); Separately, DeepSeek is reportedly betting more heavily on Huawei chips as a way to route around continued U.S. export controls. (The Information) Meta’s own AI agent set off a China-adjacent stock rally, while Unitree’s post-IPO slide kept the humanoid-robot bubble debate alive. Meta’s AI agent Muse topped the U.S. App Store, and investors immediately began drawing comparisons to Manus, the Tencent-backed Chinese AI agent startup; the read-across sent Tencent an

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之列，只可惜他们已搬去了纽约。 此刻Fluidstack在资本眼里的画像，不是云厂商，不是英伟达的客户代理，而是一个“算力撮合与交付”的轻资产平台： 数据中心的壳、电、芯片，大部分靠租，从TeraWulf、Cipher Mining、Hut 8等转型的比特币矿商手里锁定未来十年近1吉瓦的电力，与地产和资本方签长协，再打包成长期算力合同租给Anthropic、Meta、Mistral、Poolside、Black Forest Labs。 营收侧，据一家机构的投资备忘录记载，2026年Fluidstack将在10多个站点运营1.3吉瓦算力，收入6.6亿美元，这个数字是2025年预期值2亿美元的两倍多，2024年实际值的10倍。 关键是，它赚的不是硬件升值的钱，是“把1吉瓦电在正确的时间内接通”的服务费。它的Atlas OS平台几天内能自动化上线裸金属GPU集群，模块化建设的机房约6个月通电，而传统数据中心工期是18到36个月。 Jane Street的另一条路 如果按传统市销率看，180亿美元对2026年预计6.6亿美元收入，是27倍，对一个带着建设风险、电力审批风险和单一客户集中度的生

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his week, in case you missed them: Safety in numbness. On originality, intellectual bravery, and why I take a disagreement with an LLM as a good sign. Your agent, whose interests? Meta’s Muse promises a digital butler. I asked who it really works for. More AI numbers, more clarity? We looked at what companies’ increasingly specific AI claims reveal about adoption. A new essay from DeepMind argues that AGI will not emerge from a single winning AI, but rather through “ cooperative interactions amo

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rs on the PTL-U compute tile, both the 4-core and 12-core GPU tiles, as well as the 12-lane I/O tile. In conventional chips, power and signal are routed through the same frontside metal stack towards the device frontend. Power rails consume scarce routing resources near the transistors, while tall via stacks carry VDD and VSS from the coarse upper wires to local rails. Backside power delivery network (BSPDN) moves the main power network behind the transistor layer, to the backside, separating it

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ery hotel room, right? For every word that appears in a story in The New York Times , that reporter has written down probably a hundred words somewhere else. That’s all that other metadata, which traditionally has been constrained by how many column inches you had in the physical paper, or how much attention a human would actually spend on that. Imagine if you could say to the LLMs, “Hey, listen, we’re gonna sell — “ and again, there are all kinds of things around protecting sources you have to

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a FSD的智驾系统。 此外，团队里还有两位灵魂人物。 1、占方能 。 香港科技大学助理教授、World Mind Lab负责人，长期研究世界模型和物理AI。 2、 季马泽宇 。 00后青年科学家，研究方向横跨3D感知、灵巧操作和人形机器人全身控制。 加入Simate前，他还是硅谷Assured Robot Intelligence的创始成员。 ARI后来被Meta收购。 资本市场，也已经用真金白银投票。 成立仅三个月， Simate 连续完成 多轮每轮数亿元人民币融资 。 而这一切，都是在押注同一件事—— Physical RSI。 Simate从创立第一天便瞄准了这个方向，并且定义了三种不同类型的RSI。 1、弱SI：边界明确，快速闭环 人机共驾模式，研究员设定目标与约束，正常执行过程中不再需要研究员逐步参与，异常可以升级处理。 这次登顶RoboDojo，就是第一阶段的工程验证。 2、中SI：方向明确，但答案未知 怎么改善模型记忆？某类任务的数据比例到底怎么配？ 方向已经有了，但具体哪个方案有效，还得靠多轮实验去找。 这时候，Agent负责整理上下文、实现方案、跑实验、比较结果；研

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s have value. Alex Atallah [00:26:40]: Investors are wrappers and LPs, right? Alex Atallah [00:26:42]: Like venture capitalists. So, yeah, it’s all wrappers down, all down to bare metal, I guess, and like energy. Swyx [00:26:46]: Yeah, there - When I started the whole AI engineer, I guess, the coining, in 2023, like, that was, like, the number one pushback is that this is no value. You should just train models. Anjney Midha [00:26:56]: Right. Swyx [00:26:57]: And, yeah, obviously this is, like.

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needs to be able to get to the network without hopping around through Kubernetes. And you need to do this in a way where it doesn't matter if it's a well-supported cluster or bare metal in a closet. “And then,” Schmidtchen continued, “you need to tie this to world-class networking and media streaming that supports different codecs, different resolutions, and allows that to connect to APIs and SDKs across different languages that can support all different types of applications.” The global reach

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t, and generated a new image of that exact device. The image prompt it wrote for the new one is pretty specific: Subject: silver Casio Data Bank CD-401 (1984), matching the silver metal bracelet model shown in reference. Square silver case, black calculator face with four small silver memory buttons ABOVE the rectangular gray LCD, twelve small silver calculator buttons BELOW it… Exact reference layout, not later DBC model. Then I thought wouldn’t it be fun if you could say you had one of these,

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and embeddings as separate records, so a new embedding model can be adopted without crawling and uploading the same content again. At search time, they sit in one index with their metadata, reducing the need to keep each system in sync. Retrieve: Combine search strategies in one request. Semantic, sparse, and keyword search can run independently or together, balancing recall and precision. Permissions are enforced during retrieval rather than left to the application. Rerank: Send stronger eviden

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finally ready to hit the road The long-delayed electric truck is reaching customers this week.( Verge ) + It could be a big deal for electric trucking. ( MIT Technology Review ) 6 Meta has gained an early lead over OpenAI in the AI device market The Muse Charm is expected to ship before OpenAI’s hardware. ( CNBC ) + Meta promises to put privacy at the center of the products. ( Axios ) 7 A mathematician has discovered a rare eight-faced shape He argues that the strange three-holed object can exis

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set on the hub cluster and projects it to spoke clusters as a unified Portable Operating System Interface (POSIX) namespace. When a spoke is created, CDF replicates the filesystem metadata to it, so the full namespace is browsable within seconds, before file data has moved. When the spoke mounts the filesystem, CDF services each read from the nearest valid source: local NVMe cache on a hit, the hub on a miss. CDF also uses advanced congestion control, which paces to measured bottleneck bandwidth

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an absolutely MONSTER week already, from new Chinese Open Weight Frontier Lab claiming the throne for the first time, to new SOTA LLM and price cuts from Anthropic and OpenAI , to Meta Connect , to TypeSafe AI’s $10B fundraise after our exclusive podcast this weekend (already one of our top of all time, with two pods on genomic language models and AI scientists sending us above heavyweights like TBPN and MKBHD in Apple Podcasts , and helping cross 200K on YouTube ). Today is the calm before the

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杯，Skild这条看似疯狂的技术路线并非横空出世。 把这些想法从论文一路推向真实机器人的，是两位在机器人学习领域深耕多年的卡内基梅隆大学教授。 两位CMU教授，联手造机器人大脑 Skild AI成立于2023年，脱胎于 卡内基梅隆大学（CMU）机器人研究体系 。 团队成员既包括来自卡内基梅隆大学、斯坦福大学、加州大学伯克利分校等高校的研究人才，也包括曾任职于Meta、Tesla、NVIDIA、Google、Amazon和Everyday Robotics等机构的研究员及工程师。 而联合创始人兼CEO Deepak Pathak 的经历，几乎串起了Skild AI的整条技术路线。 他本科毕业于印度理工学院坎普尔校区计算机专业，后赴加州大学伯克利分校攻读计算机博士，师从计算机视觉领域知名学者Alyosha Efros和Trevor Darrell。 博士毕业后，他曾在Meta（前Facebook）AI Research从事研究工作，也曾在伯克利与机器人学习专家Pieter Abbeel合作，担任访问博士后。 此后，他进入卡内基梅隆大学，成为机器人研究所和机器学习系教授。 Deepak Pa

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st as Office defined work for the PC era, the new Copilot is built to define it for the AI era,” says Spataro. That might well be Microsoft’s goal, but there’s a lot more competition in this AI era than there ever was in the PC era. Microsoft has certainly fallen behind the likes of Google, OpenAI, Anthropic, and now Meta, and it’ll be interesting to see if “the AI built for work” is enough to help it catch up. Update, September 25th : Article updated with Microsoft CEO Satya Nadella’s comments.

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sis [01:30:29]: And so, yeah, Vibhu [01:30:30]: You got a good essay that people should read on Anastasis [01:30:33]: Yeah. Yeah Vibhu [01:30:33]: Real-world. Swyx [01:30:33]: No, Meta released a model that was, like, six modalities in one, right? Vibhu [01:30:37]: Yeah. Swyx [01:30:37]: I forget what the name of the thing was, but it was like, yeah, okay, depth is one of them, but depth is like a transformation of RGB in some sense. Vibhu [01:30:45]: ImageBind. Swyx [01:30:45]: ImageBind, yeah.

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ow-load study used the same 200 test questions with one warmup and ten measured rounds for each mode. The SVG figure-generation script stores the plotted measurements and revision metadata, including achieved QPS in the load-chart tooltips. Read the Results and Check the Measurements Each output directory contains summary.json and per-request samples.jsonl. For Figure 2-style comparisons, use end to end latency ms.p95 at matched target QPS, together with achieved QPS and the error count. For Fig

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% of the vote on most corporate matters, as long as at least three of them keep a minimum stake. This isn’t a new idea; super-voting shares are how Mark Zuckerberg kept control of Meta, and how Evan Spiegel maintained his hold of Snap. The list is long. What’s unusual is the group approach. Why do it? The seven co-founders reportedly own just 2% of the company apiece, Amodei included, and have pledged to give away 80% of their wealth, a commitment the CEO announced in January alongside a warning

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MLX-VLM requires the respective build ( PR 2280 ). mlx vlm.server --model LiquidAI/LFM2.5-VL-3B --draft-model LiquidAI/LFM2.5-VL-3B-DSpark The block size is read from the sidecar metadata (n-max is clamped to it). Speculative decoding is exact : the target verifies every proposed token, so greedy output equals the target alone; per-response timings report draft n / draft n accepted . Our vision DSpark draft model is available on Hugging Face in Safetensors and GGUF formats . With LFM2.5, we're

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