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马斯克中选后要被“秋后算账”？德银：算力越紧张SpaceX越赚钱，政治风险被高估

华尔街见闻德银认为，SpaceX政治风险被市场高估——核心合同由联邦行政机构签订，国会干预空间极为有限，且多数业务领域无可替代供应商。最直接风险来自得州候任州长对数据中心的强硬表态，但算力供给收紧反可能推高SpaceX定价能力与收入。
· 算力芯片,xAI,基础设施,财报

事关AI交易走向，硅谷、华尔街热议“黄仁勋绯闻”，马斯克力挺、特朗普也发声

华尔街见闻黄仁勋婚外情事件持续发酵，红杉合伙人Maguire力挺黄仁勋，马斯克转发称“我同意”。Bill Ackman表示，黄仁勋是人，都会犯错。特朗普称愿带黄仁勋出访亚洲。散户看戏表示，“贝佐斯有过婚外情，比尔·盖茨有过婚外情，马斯克也有过婚外情”，但都没影响公司。但华尔街担心黄仁勋会辞职，“很多基金经理将在未来几周减持英伟达”。
· 算力芯片,NVIDIA,xAI,金融

美国蓝潮，为马斯克的太空算力按下加速键？

华尔街见闻美国蓝潮正在给地面AI数据中心建设带来新的政策与审批变量，太空算力由此获得新的催化。马斯克重申太空数据中心的战略吸引力，并给出SpaceX首批计算卫星拟于2027年发射的时间表。 与此同时，Google、Starcloud及中国计算卫星项目相继取得工程进展。太空算力正在从远期构想走向在轨验证，卫星智能化、空间能源、热控与激光通信等产业链环节也有望迎来新的价值增量。
· 算力芯片,政策监管,Google,xAI,基础设施
AI 资讯

马斯克最新透露：特斯拉可能将出租而非出售Opti 12:22

网易科技
2026-11-09T12:22:06+08:00 · 自动驾驶,xAI
AI 资讯

Unlocking any car from X brand in most of the world

Hacker NewsComments
2026-11-07T10:56:40.000Z · 大模型,算力芯片,AI应用,xAI,搜索RAG,Transformer,强化学习,招聘HR,榜单评测,开发者生态
AI 资讯

MotionBench-XAI: Benchmarking What Shapes Shapley Explanations in Spatiotemporal Machine Learning

arXiv cs.LGarXiv:2611.08797v1 Announce Type: new Abstract: Predicting outcomes from spatiotemporal data, such as assessing Parkinson's severity from human motion, often requires explainability in addition to accuracy. Practitioners need to know what drove the prediction, whether it be spatial (a body joint), temporal (a specific window), or both. Shapley values are attributions computed by treating input features as players in a cooperative game and measuring how the model output changes as coalitions of players are removed. Computing them involves choosing what counts as players, how absent players are imputed, and how the game is evaluated. How these choices affect explanation quality is poorly understood for spatiotemporal data, and real data carries no ground-truth attributions to check against. We present MotionBench-XAI, a benchmark built on i) five synthetic spatiotemporal datasets with exact ground-truth Shapley values, ii) three real datasets across clinical gait, electrocardiogram, and audio, iii) ten attribution methods across three player sets, and iv) three trained architectures. We show that grouping features into players, across time, space, or both, matters more than the choice of imputer. Generative imputers improve explanations only when their conditional models are accurate. On real data, the standard proxy metrics select contradictory best methods. We distill these findings into practical insights and provide an open-source pipeline.
2026-11-09 05:00:00 · 算力芯片,Google,xAI,扩散模型,微调蒸馏,模型评测,端侧AI,招聘HR,论文
AI 资讯

TopoSHAP: Shapley for Explainable and Efficient Topological Deep Learning

arXiv cs.LGarXiv:2611.08789v1 Announce Type: new Abstract: Topological deep learning (TDL) extends learning on graphs, such as molecules or social networks, to higher-order structures, such as rings in a molecule or groups in a social network. Explaining which of these structures drive a model's predictions, and which architectural components matter most for its performance, remains challenging. We introduce TopoSHAP, an efficient and general explanation framework that addresses both questions across topological domains and model architectures. TopoSHAP-cell assigns Shapley values to topological data cells and extracts compact explanations. These explanations match or outperform graph-based counterparts on GraphXAI benchmark data while running up to 112$\times$ faster. They also reveal advantageous topological feature design, leading to measured improvements in explanation recovery and predictive accuracy. On the architecture side, TopoSHAP-neighbor explains how neighborhoods contribute to model performance, and turns these evaluations into a backward-elimination search that identifies smaller, competitive architectures early in training. This is up to $11.3\times$ cheaper than traditional TDL training sweeps.
2026-11-09 05:00:00 · xAI,扩散模型,模型评测,网络安全,榜单评测,论文
AI 资讯

Foundation-Model Residuals as Signals of Urban Bus Irregularity

arXiv cs.LGarXiv:2611.08703v1 Announce Type: new Abstract: Pretrained trajectory models encode mobility patterns learned across cities, but their errors when transferred to a new city are usually treated only as performance limitations. We instead ask whether these residuals reveal systematic local conditions. Architectures such as UniTraj and TrajFM learn transferable trajectory representations, although cross-context transfer remains challenging. Using the NetMob26 bus location, ticketing, weather, network, and socioeconomic data for Niteroi, we analyze spatial and travel-time residuals of these recent trajectory foundation models. We test whether high residuals concentrate spatially, whether spatial and travel-time residuals overlap, and which urban-context variables explain their distribution. We interpret high residuals as candidate proxy signals of transit irregularity rather than direct evidence of service disruption, and analyze their contextual associations using Explainable AI (XAI). Insights into such varied environments are increasingly valuable for evaluating new urban paradigms; for instance, the 15-minute-city perspective serves as a key motivation for our approach, guiding our methods and expanding the analysis beyond standard mobility metrics.
2026-11-09 05:00:00 · xAI,扩散模型,预训练,网络安全,论文
AI 资讯

PolSAM: Polarimetric Scattering Mechanism Informed Segment Anything Model

arXiv cs.CVarXiv:2412.12737v3 Announce Type: replace Abstract: PolSAR data presents unique challenges due to its rich and complex characteristics. Existing data representations, such as complex-valued data, polarimetric features, and amplitude images, are widely used. However, these formats often face issues related to usability, interpretability, and data integrity. Most feature extraction networks for PolSAR are small, limiting their ability to capture features effectively. To address these issues, We propose the Polarimetric Scattering Mechanism-Informed SAM (PolSAM), an enhanced Segment Anything Model (SAM) that integrates domain-specific scattering characteristics and a novel prompt generation strategy. PolSAM introduces Microwave Vision Data (MVD), a lightweight and interpretable data representation derived from polarimetric decomposition and semantic correlations. We propose two key components: the Feature-Level Fusion Prompt (FFP), which fuses visual tokens from pseudo-colored SAR images and MVD to address modality incompatibility in the frozen SAM encoder, and the Semantic-Level Fusion Prompt (SFP), which refines sparse and dense segmentation prompts using semantic information. Experimental results on the PhySAR-Seg datasets demonstrate that PolSAM significantly outperforms existing SAM-based and multimodal fusion models, improving segmentation accuracy, reducing data storage, and accelerating inference time. The source code and datasets will be made publicly available at https://github.com/XAI4SAR/PolSAM.
2026-11-09 05:00:00 · AI应用,开源,xAI,多模态,搜索RAG,提示工程,论文
AI 资讯

PathXAI: Evidence-guided structured pathology reporting by transferable tissue segmentation

arXiv cs.CLarXiv:2611.09117v1 Announce Type: cross Abstract: Pathology reporting requires translating tissue observations across regions and slides into specific diagnoses and supporting morphological descriptions. However, the use of explicit tissue-composition measurements to guide H&E-based report generation remains underexplored. Thus, we developed PathXAI, which combines segmentation-derived tissue information with H&E images and optional domain knowledge to guide diagnostic reasoning, targeted reinspection and structured report writing. Its segmentation component exceeded baselines in mean Dice on internal and external tests. In automated report evaluation on 1,200 public and 151 private cases, PathXAI achieved higher agreement with reference diagnoses and documented morphology than the H&E-based comparators. On the public evaluation cohort, PathXAI-Report exceeded Qwen + H&E in cancer family match rate by 4.4 percentage points and in finding F1 by 5.7 percentage points. PathXAI generates evidence-linked reports with a self-hosted pretrained vision-language model, without updating its weights.
2026-11-09 05:00:00 · 大模型,阿里巴巴,xAI,推理思考,强化学习,预训练,论文
AI 资讯

Comparing Explanations is Not Enough, Explain the Change: New Standards are Needed to Explain Behavioral Shifts in Large Language Models

arXiv cs.AIarXiv:2602.02304v4 Announce Type: replace Abstract: Large-scale foundation models exhibit behavioral shifts when subjected to interventions such as scaling, fine-tuning, reinforcement learning with human feedback, or in-context learning. Current explainability methods are structurally ill-suited to explain these shifts, because they either treat models as static objects, as traditional eXplainable AI (XAI) approaches do, or merely compare independent explanations across different checkpoints of a model. As a result, these approaches fail to explain the functional transition between two model instances in which a certain behavior has shifted following an intervention. This gap creates significant governance risks across jurisdictions including the EU AI Act, US state legislation, and Chinese AI regulations, which require documenting causal chains for substantial system modifications. This position paper argues that explaining behavioral shifts in large language models requires a principled approach that treats the shift itself as the primary object of explanation: namely, one that explains how and why an intervention transforms a reference model into an updated model with different behavior. To support this claim, we introduce Comparative XAI (XAI$_\Delta$), a novel XAI paradigm aimed at explaining the difference between two model checkpoints where a behavior has shifted, together with a set of desiderata specifying what XAI$_\Delta$ explainers and explanations must satisfy, including comparability, validity, actionability, and monitoring, with the goal of grounding model auditing in explicit, measurable requirements. Finally, we provide preliminary evidence suggesting the need for XAI$_\Delta$ in practice through illustrative experiments, compiling the resulting findings into a transition report directly usable for governance and incident documentation.
2026-11-09 05:00:00 · xAI,扩散模型,强化学习,微调蒸馏,提示工程,招聘HR,论文
AI 资讯

RAG-Enhanced LLMs for Interactive Explainability in Clinical Decision Support Systems

arXiv cs.AIarXiv:2611.09232v1 Announce Type: new Abstract: Artificial Intelligence (AI) is increasingly embedded in high-risks decision-making systems across domains such as healthcare, finance, and the judicial system. As reliance on predictive models grows, so does the demand for Explainable AI (XAI) to ensure transparency, trust, and interpretability. However, existing XAI approaches often fail to present explanations in a format accessible to domain experts, limiting their practical utility. Large Language Models (LLMs) have recently emerged as a bridge between complex models and end-users, offering natural language explanations. Yet, their use introduces new risks, including hallucinated outputs and lack of source traceability. To address these challenges, we propose a Clinical Decision Support System (CDSS) that not only combines standard predictive models with XAI techniques but also incorporates a Retrieval-Augmented Generation module as an interactive enhancer. This module grounds explanations in verified medical knowledge, reformulates technical outputs into a human-like format, and enables users-such as clinicians-to actively query the system. By fostering an interactive, human-in-the-loop environment, our approach empowers domain experts to explore model decisions, contextualize explanations, and build trust in AI-assisted diagnostics. The study concludes with preliminary experiments validating the proposed methodology.
2026-11-09 05:00:00 · 大模型,算力芯片,AI应用,Google,xAI,搜索RAG,强化学习,论文

Meta Superintelligence – Leadership Compute, Talent, and Data

SemiAnalysis

Meta’s shocking purchase of 49% of Scale AI at a ~$30B valuation shows that money is of no concern for the $100B annual cashflow ad machine. Despite seemingly unlimited resources, Meta has been falling behind foundation labs in model performance.

The real wake-up call came when Meta lost its lead in open-weight models to DeepSeek. That stirred the sleeping giant. Now in full Founder Mode, Mark Zuckerberg is personally leading Meta’s charge, identifying Meta’s two core shortcomings: Talent and Compute. As one of the last founders still running a tech behemoth, Mark doesn’t need SemiAnalysis to tell him to slow down stock buybacks to fund the future!

Source: Meta financials and SemiAnalysis estimates

In addition to throwing money at the problem, he’s fundamentally rethinking Meta’s approach to GenAI. He’s starting a new “Superintelligence” team from scratch and personally poaching top AI talent with pay that makes top athlete pay look like chump change. The typical offer for the folks being poached for this team is $200 million over 4 years. That is 100x that of their peers. Furthermore, there have been some billion dollar offers that were not accepted by researcher/engineering leadership at OpenAI. While these offers aren’t all successful, Zuck is crushing the competitors by drastically increasing their cost per employee.

Perhaps even more iconic, Zuck threw his entire Datacenter playbook into the trash and is now building multi-billion-dollar GPU clusters in “Tents”!

Source: SemiAnalysis Datacenter Model – as of 07/06/2025

As this report details, nothing is off the table. We unpack Meta’s unprecedented reinvention from Compute to Talent in the pursuit of Superintelligence as well as the story of how we got here. From Llama 3.0 open-sourced dominance to the epic fail of Llama 4 Behemoth, this Titan of AI is down but not out. In fact, we believe Meta’s ramp in training FLOPS will rival even that of OAI. The company is going from GPU-poor to GPU-filthy-rich on a per researcher basis.

Meta GenAI 1.0: AI Incrementalism

Compared to pure-play AI labs like OpenAI, companies like Meta and Google have followed an “AI Incrementalism” strategy by enhancing existing products with better recommendation systems and GenAI to improve ad targeting, content tagging, and internal tools. This has paid off handsomely in financial results, allowing Meta to shrug off Apple’s attempts at stopping them from tracking users with the release of their App Tracking Transparency (ATT) feature in iOS 14.5 (late 2021, early 2022).

Source: Company filings

While Meta is arguably more insulated from GenAI disruption than Google, LLM efforts at both companies have been somewhat disappointing. One reason is the allocation of capital primarily for the core business rather than pursuing superintelligence.

“Our CapEx growth this year is going toward both generative AI and core business needs with the majority of overall CapEx supporting the core.”

Source: Meta Q1 2025 earnings call, emphasis SemiAnalysis

GenAI acts as an extension to these tech giants’ businesses. They don’t have the same existential need to dominate new use cases like OpenAI in chatbots and Anthropic in coding APIs. This is readily apparent in the compute vs human capital allocation of a leading AI foundation lab like OpenAI compared to Meta. Zuck’s impact on competing offers and inflation of salary will hurt badly.

Source: The Information, SemiAnalysis estimates 

As a result, when measuring GenAI consumer app traction, Meta and Google meaningfully lag ChatGPT in its reach and engagement.

But that’s changing. Leveraging our proprietary Accelerator Industry Model and Datacenter Industry Model, we forecast a meaningful step up in GenAI investment from Meta in the coming years.

Source: SemiAnalysis Core Research, actual numbers are shown beyond paywall, at the bottom of the report. 

Meta GenAI 2.0 – Part 1, Re-Inventing the Datacenter Strategy (Again)

From Buildings to Tents

Just a year ago, Meta scrapped its decade-old “H”-shaped datacenter blueprint for a new AI-optimized design.

Source: SemiAnalysis Datacenter Model

Now in 2025, Zuck decided to re-invent the strategy again. Inspired by xAI’s unprecedented time-to-market, Meta is embracing a datacenter design that prioritizes speed above all else. They’re already building more of them!

2025-07-11 20:12:19 · 大模型,算力芯片,AI应用,具身智能,OpenAI,Google,Anthropic,Meta,DeepSeek,xAI,对话助手,搜索RAG,扩散模型,强化学习,招聘HR,财报,榜单评测,开发者生态

Amazon’s AI Resurgence: AWS & Anthropic’s Multi-Gigawatt Trainium Expansion

SemiAnalysis

Two-and-a-half years ago, we flagged a looming “cloud crisis” at AWS. Today, the evidence has mounted. AWS is the crown jewel of the Amazon empire, generating ~60% of group profits, and dominating the lucrative Cloud Computing market. But it struggles to translate this strength into the new GPU/XPU Cloud era.

Microsoft Azure now leads the market on quarterly new cloud revenue, and the gap between Google Cloud and AWS has materially narrowed especially with Google’s big moves on the TPU that we’ve been posting about for over a month. Markets have noticed. Year-to-date, Amazon is the clear laggard among the four tech-and-AI titans as investors mark down the company most for losing momentum in AI.

Source: SemiAnalysis Core Research, company filings

Today, SemiAnalysis is back with another out-of-consensus call. While the market overplays the Cloud Crisis theme, we call for an AWS AI Resurgence. We laid out our thesis a month ago to our Core Research subscribers, forecasting an upcoming acceleration beyond 20% year-over-year growth by the end of 2025.

Source: SemiAnalysis Core Research

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Amazon’s savior has a name: Anthropic. The startup has been the clear outperformer in the GenAI market in 2025, multiplying revenue fivefold year-to-date to reach $5B annualized.

Source: The Information, Reuters, Bloomberg, SemiAnalysis Core Research

To keep that trajectory, Anthropic is betting hard on Scaling Laws. While Dario’s startup draws fewer headlines than OpenAI, xAI and Meta Superintelligence, it isn’t shy about investment. AWS has well over a gigawatt of datacenter capacity in final stages of construction for its anchor customer. AWS is building datacenters faster than it ever has in its entire history. And there’s much more on the horizon.

Source: SemiAnalysis Datacenter Industry Model

To understand and forecast GPU/XPU power capacity by AI Lab broken down by Cloud Provider, we rely on our proprietary Datacenter Industry Model powered by real-time satellite imagery. Trusted by all hyperscalers, AI labs, and the world’s largest investors, it provides a quarterly building-by-building datacenter forecast for OpenAI, Anthropic, xAI, Meta Superintelligence, Google DeepMind, and more. Contact us for more information.

Trainium vs GPUs

While Amazon’s AI Datacenters are impressive in scale and speed, the design of individual building is unremarkable. Hyper-optimized for air-cooling, this blueprint is identical to 5-year-old traditional AWS Cloud datacenters.

What makes these facilities unique is their inside: they’ll host the world’s largest cluster of non-Nvidia AI chips, with just under a million Trainium2 in the largest campus. To understand everything about the Trainium2 system, read our December 2024 technical deep dive.

Trainium2 lags Nvidia’s systems in many ways, but it was pivotal to the multi-gigawatt AWS/Anthropic deal. Its memory bandwidth per TCO advantage perfectly fits into Anthropic’s aggressive Reinforcement Learning roadmap. Dario Amodei’s startup was heavily involved in the design process, and its influence on the Trainium roadmap only grows from here.

Put plainly: Trainium2 is converging toward an Anthropic custom-silicon program. This will enable Anthropic to be, alongside Google DeepMind, the only AI labs benefiting from tight hardware–soft

2025-09-03 20:55:46 · 大模型,算力芯片,AI应用,具身智能,OpenAI,Google,Anthropic,Meta,Microsoft,NVIDIA,xAI,Agent智能体,推理思考,搜索RAG,扩散模型,强化学习,招聘HR

xAI’s Colossus 2 – First Gigawatt Datacenter In The World, Unique RL Methodology, Capital Raise

SemiAnalysis

Much has been written about xAI’s Colossus 1. The Memphis build belongs in the history books: the largest AI training cluster, erected from scratch in 122 days. With roughly 200,000 H100/H200s and ~30,000 GB200 NVL72, it remains, today, the largest fully operational, single-coherent cluster (setting apart Google, master of multi-datacenter-training).

However, Colossus 1’s ~300 MW looks modest next to the Gigawatt-scale clusters under construction by OpenAI, Meta and Anthropic. Their hyperscaler partners are happy to leverage their balance sheet and win the market by throwing dollars at it.

Was xAI’s prowess a one-time wonder? Today we will publicize some data from our industry leading datacenter model over the last year that is accessible to clients. This is the our same proprietary data that called the Oracle deals many months ahead of the announcement.

Source: SemiAnalysis Datacenter Industry Model – note: there is a lag between Datacenter operational and GPUs operational – Google and exact figures available in model

Short answer: no. xAI is still squarely in the frontier-AI race and is positioned to leapfrog most rivals again on compute. By our estimates, its total datacenter capacity for a single training cluster will surpass Meta Superintelligence and Anthropic by Q3 2025. The datacenter capacity will be ready for the GPUs to be moved in to create the largest single datacenter in the world, yet again. xAI has to raise the capital for those GPUs, but they have the allocations from Nvidia to have it fully training large scale models early next year.

Elon came up with a new genius trick to beat rivals at time-to-market. Colossus 2 will be an even more impressive achievement than xAI’s first cluster. Let’s dig in.

The first half of this report will dig into the Colossus 2 prowess. The second half will discuss Grok models, our mid-to-long term thoughts on xAI, and the unique RL method xAI is using that may lead them to leapfrog OpenAI, Anthropic, and Google.  

SemiAnalysis Is Hiring

We are seeking a highly motivated & skilled Member of Technical Staff to join our growing special projects engineering team. You will play a crucial role in developing industry leading gpu cloud benchmarks & evaluation framework.  Our gpu cloud evaluation frameworks is endorsed by many tier 1 & 2 frontier labs. You may be a good fit if you have the following experience:

  • Demonstrated experience in ML frameworks such as PyTorch or JAX through professional experience, personal projects, or personal Substack blogs
  • 1-2 years using GPU or TPU clusters and/or running a multi-tenant GPU cluster
  • Past experience working at a hyperscaler or a GPU cloud (preferred)
  • Solid understanding of SLURM, Kubernetes, NCCL & GPU Cloud industry
  • Strong research skills and the ability to synthesize information from various sources to draw insights

Compensation is competitive & as part of the interview process, you’ll complete a paid coding challenge designed to reflect typical daily tasks at SemiAnalysis

Colossus 2: from zero to 200MW in six months

The Colossus 2 project was kicked off on March 7th, 2025, when xAI acquired a 1m sqft warehouse in Memphis, and two adjacent sites totaling 100 acres. By August 22nd, 2025, we count 119 air-cooled chillers on site, i.e. roughly 200MW of cooling capacity. That’s enough to power roughly 110k GB200 NVL72. And an Elon tweet shows some racks were already installed in July.

xAI built in six months what took 15 months for Oracle, Crusoe and OpenAI!

Source: SemiAnalysis Datacenter Industry Model

Looking closer at the picture above, readers familiar with our Datacenter Anatomy series might wonder where the power infrastructure is. The picture doesn’t show any onsite substation, nor does it show onsite turbines…  How is the datacenter powered?

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Colossus 2: in Tennessee, Mississippi… or both?

Things get more confusing once factoring-in a Greater Memphis Chamber&

2025-09-16 17:38:01 · 算力芯片,AI应用,具身智能,OpenAI,Google,Anthropic,Meta,NVIDIA,xAI,搜索RAG,扩散模型,模型评测,招聘HR,收购并购,开发者生态

Text is so 2023

Ben's Bites (Substack)

Hi folks,

Personal agent season is still in full-swing. Hark (from Brett Adcock - Figure robots) launched which suggests tasks as one-tap action buttons. Tab came out of stealth at a $300M valuation. Every put an agent in Slack. Meta and Sierra want a standard for how these agents deal with businesses.

My favourite take came from Sonia Baschez (who worked with me on Makerpad years ago!):

Proactive has a downside, though. Shane Mac’s agent posted his personal bank balances in the company Slack, as him. Then said sorry 😂. A community note popped up to flag this may have been a fake post, but I’m not sure…

I’m now carefully trying to think about which agents have access to what, and where that access is. So I’m using Executor (v2). It’s basically one app that you connect all your other apps to, and you can add your skills too. My whole setup was a bit of a mess so I’ve started spring cleaning and will let you know if/when I feel like I have a good setup.

Headlines

ChatGPT now answers with interfaces. GPT-6 and Intelligent UI are rolling out to everyone. Answers can include charts, maps, forms and small tools, like a bill splitter for dinner with friends. Free users get GPT-6 Luna from today. (tweet)

Claude Haiku 5.5 costs ~75% less to run than Haiku 4.5. Anthropic pitches it as a subagent for Opus and Sonnet. Simon has great notes on the model. Max and Team plans also now get monthly API credits to build your own apps and agents. (tweet). Anthropic also expanded their Claude startup program, and people were getting accepted in minutes.

Nano Banana 2.1 is Google’s new image model. Live in AI Studio. But ChatGPT images is still better.

OpenAI released 700+ new maths results from an unreleased model. Someone built a tool to spend your spare tokens on open math problems.

Grok Bot can search and monitor X now. Also, it now uses Claude Opus 5.5 as the primary model. It’ll still delegate tasks to other models based on the complexity.

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My feed

  • AgentHog - stop guessing what your users do: pipe product analytics into Claude and Codex so your agents learn from real user behavior.*

  • How OpenAI’s Dots took over Every: pulling info out of school emails and catching missed Slack messages. I, however, still can’t get Dots to do anything good at all. Currently diving more into Grok Bot (until a completely custom Pi personal agent comes out!)

  • Lauren (poteto) automated Cursor’s release and QA with Grok bot.

  • New ChatGPT plugin: Meetings. It takes notes and helps with follow-ups. Pro and Business, Mac app only.

  • Claude now sits in a sidebar in Google Docs, Sheets & Slides. You approve each edit.

  • Gamma 5 is a full rebuild, so your decks look less like everyone else’s.

  • Figma Agent is out of beta and on all paid plans. It now uses AI credits.

  • Playground - make a game from a prompt. A Google Labs experiment, US only.

  • Rhem - a robot for your parents. It makes calls and books appointments.

  • Someone rebuilt Adobe’s apps with Opus 5.5 and open-sourced them.

  • An open-source Clay alternative for people search inside Claude Code/Codex.

  • OptionAFK - Transcribe large audio/video files on your device. Private and fast.

Try this

Turn a podcast into an interactive explainer. Boris Cherny asked Opus 5.5 to make one for Acquired’s Home Depot episode (a great episode btw).

Make an interactive website companion for [episode link or transcript]. Add a timeline, the key people and the big ideas, with illustrations.

Here was the result:

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Afters

and Theo reckons its way less…

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2026-10-08 13:00:52 · 大模型,算力芯片,AI应用,具身智能,开源,OpenAI,Google,Anthropic,Meta,xAI,代码生成,Agent智能体,扩散模型,强化学习,提示工程,端侧AI,招聘HR,收购并购,榜单评测,开发者生态
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