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Components map a name to a group of sub-token properties: ... Valid component properties: backgroundColor, textColor, typography, rounded, padding, size, height, width.

The tokens are the normative values. The prose provides context for how to apply them.

A DESIGN.md file combines machine-readable design tokens (YAML front matter) with human-readable design rationale (markdown prose). Tokens give agents exact values. Prose tells them _why_ those values exist and how to apply them.

继续做通用 UX 是最危险的位置，它正是最容易被 AI 和产品经理上下夹击的中间层。

其中 Pattern 是最容易被忽略也最关键的一层，它定义了'在具体业务场景下该怎么组合这些组件'，是 AI 时代设计系统真正的价值所在。

At our request, the underwriters have reserved up to _______% of the shares of Class A common stock offered by this prospectus for sale at the initial public offering price through a directed share program to certain persons identified by our management and certain long-tenured employees, which may include parties with whom we have a business relationship and friends and family of management and such employees.

We have applied to list our Class A common stock on the Nasdaq Global Select Market under the symbol 'CBRS,' and this offering is contingent upon the listing of our Class A common stock on the Nasdaq Global Select Market.

Distributed systems background - real-time sessions, cloud infrastructure (AWS), and production reliability

Hands-on experience building or maintaining a game engine (must), with strong Python fundamentals (must)

Help lay the game and environment foundations for ARC-AGI-4 and ARC-AGI-5

You'll be responsible for stabilizing the current stack to setting the foundation for what comes next.

A senior engineer to own and evolve the game engine and real-time play infrastructure behind the ARC-AGI series.

Begin with fast checks that explain behavior, then add slower, heavier checks only when they reduce risk.

The fastest way to get started is to use Codex's built-in skill creator (which itself is also a skill).

The most reliable way to improve a skill over time is to evaluate it the same way you would any other prompt for LLM applications.

It happens several times a year in the US alone, often unreported, and about 100 times a year worldwide.

Nor does it matter, given that the modifying strains for pathogens for research purposes is what every research lab does, because that is what virology is.

For me Ralph Baric's 2024 test testimony moved the lab leak hypothesis to pretty likely.

And since then, there is no more scientific evidence or verifiable sources. Hence the reason the CIA didn't even believe it and gave it the lowest confidence rating it has.

We run a similar model processing loan documents that would normally require a team of 15.

This is the part people miss about AI-native companies - the $113k is not a cost, it is your headcount budget allocated differently.

Our goal is $10M ARR [annual recurring revenue] with a sub-10 person org.

The real unlock is compound scaling—token spend grows linearly while output grows exponentially.

We imagine a world where all of the tools you use are as rich and visual as the world we live in.

If the most effective way to communicate something were a single word, an illustration, or a photorealistic rendering, that's what you'd see.

The screen you're reading this on is already presenting you an image, it's just generated with rigid code and rules that makes it difficult to communicate complex and detailed ideas.

All text on the screen is rendered as pixels by the image model. There are no text overlays applied to the images.

The entire web is just generated pixels on your screen.

Vercel is advising Google Workspace administrators and Google account owners to check for the following application: OAuth App: 110671459871-30f1spbu0hptbs60cb4vsmv79i7bbvqj.apps.googleusercontent.com

threat actors linked to recent attacks attributed to the ShinyHunters extortion gang have denied to BleepingComputer that they are involved in this incident.

Unfortunately, the attacker got further access through their enumeration.

the initial access occurred after a Vercel employee's Google Workspace account was compromised via a breach at the AI platform Context.ai.

Vercel stores all customer environment variables fully encrypted at rest. We have numerous defense-in-depth mechanisms to protect core systems and customer data.

The action that matters most — building semiconductor-grade hydrogen bromide gas conversion capacity outside Israel — takes years.

The structural failure is not the war: It is that the global memory supply chain has built itself around a conversion chokepoint with no redundancy and no fallback.

The story receiving almost no attention is bromine, and it is potentially the more dangerous one.

Emissions in advanced economies grew faster (+0.5%) than in emerging and developing economies (+0.3%) for the first time since the 1990s.

Battery storage was the fastest-growing power technology, with around 110 gigawatts (GW) of new capacity added – more than any year of natural gas capacity additions on record.

Solar was the single biggest contributor to global energy supply growth in 2025. It accounted for more than 25% of the increase – the first time a modern renewable has led global primary energy growth.

we probably will publish more curl vulnerabilities in 2026 than we have done in many years, maybe ever.

it is decently important to handle them asap when they arrive so that we can avoid building up too much backlog.

The time when we suffer from large amounts of AI slop is gone. Now we instead suffer under a massive load of good reports.

In addition to empowering developers and agents to handle project setup and boilerplate code, we've also designed these new tools and resources to make it easier to transition to Android Studio.

By accessing the frequently updated knowledge base, agents can ground their responses in the most recent information from Android developer docs, Firebase, Google Developers, and Kotlin docs. This ensures that even if an LLM's training cutoff is a year old, it can still provide guidance on the latest frameworks and patterns we recommend today.

Android skills cover some of the most common workflows that some Android developers and LLMs may struggle with—they help models better understand and execute specific patterns that follow our best practices and guidance on Android development.

The new Android CLI serves as the primary interface for Android development from the terminal, featuring commands for environment setup, project creation, and device management—with more modern capabilities and easy updatability in mind.

Whether you are using Gemini in Android Studio, Gemini CLI, Antigravity, or third-party agents like Claude Code or Codex, our mission is to ensure that high-quality Android development is possible everywhere.

In our internal experiments, Android CLI improved project and environment setup by reducing LLM token usage by more than 70%, and tasks were completed 3X faster than when agents attempted to navigate these tasks using only the standard toolsets.

Capture sequences and replay them as stable automations.

Legacy workflows, turned into dependable APIs for agents and internal systems.

Map tokens, cookies, sessions, and multi-step sequences automatically.

See every HTTP/S request from any app or browser in real time.

Maintains your HTTP/TLS fingerprint so intercepted traffic behaves identically to the original.

Kampala lets you reverse engineer anything including websites, mobile apps, and desktop apps instantly.

SWE-chat is a living dataset; our collection pipeline automatically and continually discovers and processes sessions from public repositories

despite rapidly improving capabilities, coding agents remain inefficient in natural settings

users push back against agent outputs -- through corrections, failure reports, and interruptions -- in 44% of all turns

agent-written code introduces more security vulnerabilities than code authored by humans

Just 44% of all agent-produced code survives into user commits

coding patterns are bimodal: in 41% of sessions, agents author virtually all committed code ('vibe coding'), while in 23%, humans write all code themselves.

The overall conclusion, therefore, is that AI for Science should be understood as both a scientific and a civilizational project.

The central question is not whether AI can imitate human conversation, but whether it can participate in the production of publishable scientific knowledge at a level comparable to a recognized human contributor.

Without a mechanism for continuous and diverse learning, AI systems will tend to reproduce the dominant patterns already present in their training data. That limitation would make truly creative work difficult.

The most effective pattern of human-AI cooperation may differ substantially across disciplines, and these patterns will likely be discovered through practice rather than designed in advance.

If publication becomes more agentic, it may create new ways to recognize and evaluate such contributions. Although the final form of such a system remains uncertain... the evaluation and reward structure of academia will change in a fundamental way.

The application of LLMs in science is already underway... We believe that AI will ultimately bring a fundamental big change to scientific research across disciplines.

The most fundamental change brought by the LLM revolution is that human know-how is becoming replicable and shareable at scale.

The results demonstrate consistent improvements over strong baselines, supporting the effectiveness of agent resource management and closed loop self evolution.

Building on AGP, we present Autogenesis System (AGS), a self-evolving multi-agent system that dynamically instantiates, retrieves, and refines protocol-registered resources during execution.

Its Self Evolution Protocol Layer (SEPL) specifies a closed loop operator interface for proposing, assessing, and committing improvements with auditable lineage and rollback.

Its Resource Substrate Protocol Layer (RSPL) models prompts, agents, tools, environments, and memory as protocol registered resources with explicit state, lifecycle, and versioned interfaces.

We introduce Autogenesis Protocol (AGP), a self evolution protocol that decouples what evolves from how evolution occurs.

existing agent protocols (e.g., A2A and MCP) under specify cross entity lifecycle and context management, version tracking, and evolution safe update interfaces, which encourages monolithic compositions and brittle glue code.

The results demonstrate consistent improvements over strong baselines, supporting the effectiveness of agent resource management and closed loop self evolution.

Building on AGP, we present Autogenesis System (AGS), a self-evolving multi-agent system that dynamically instantiates, retrieves, and refines protocol-registered resources during execution.

Its Self Evolution Protocol Layer (SEPL) specifies a closed loop operator interface for proposing, assessing, and committing improvements with auditable lineage and rollback.

Its Resource Substrate Protocol Layer (RSPL) models prompts, agents, tools, environments, and memory as protocol registered resources with explicit state, lifecycle, and versioned interfaces.

We introduce Autogenesis Protocol (AGP), a self evolution protocol that decouples what evolves from how evolution occurs.

However, existing agent protocols (e.g., A2A and MCP) under specify cross entity lifecycle and context management, version tracking, and evolution safe update interfaces, which encourages monolithic compositions and brittle glue code.

Scan your website to see how ready it is for AI agents. We check multiple emerging standards — from robots.txt and Markdown negotiation to MCP, OAuth, Agent Skills and agentic commerce.

We want email agent tooling to be composable and reusable. Rather than every team rebuilding the same inbound-classify-reply pipeline, start with this reference application.

Each agent gets its own identity from a single domain. The address-based resolver routes support@yourdomain.com to a 'support' agent instance, sales@yourdomain.com to a 'sales' instance, and so on.

The inbox becomes the agent's memory, without needing a separate database or vector store.

A chatbot responds in the moment or not at all. An agent thinks, acts, and communicates on its own timeline.

Email is the most accessible interface in the world. It is ubiquitous. There's no need for a custom chat application, no custom SDK for each channel.

If this analogy is right, then we will likely see sort of a 'Cambrian explosion' in agent harnesses purpose-built for running server-side; and the few that win this race will become as ubiquitous as WordPress.

They don't mind paying the AI labs for tokens — but the agent itself, they'd much rather have outside of the labs' infrastructure.

WordPress wasn't the best-designed CMS — Drupal was. It wasn't the most performant or reliable either. But that didn't stop WordPress from becoming the de-facto standard for building websites and the largest ecosystem — all because it was the easiest to use.

Agent harnesses are much more like WordPress than they are like Apache, simply because people want to have their own agents — just like everyone wanted their own website in the early 2000s.

WordPress still serves roughly 40% of all Internet traffic. It's more than 20 years old. It is old and clumsy and doesn't scale well and it couldn't care less about all the distributed system cloud goodness that we are all accustomed to.

They don't mind paying the AI labs for tokens — but the agent itself, they'd much rather have outside of the labs' infrastructure.

If this analogy is right, then we will likely see sort of a 'Cambrian explosion' in agent harnesses purpose-built for running server-side; and the few that win this race will become as ubiquitous as WordPress.

WordPress wasn't the best-designed CMS — Drupal was. It wasn't the most performant or reliable either. But that didn't stop WordPress from becoming the de-facto standard for building websites and the largest ecosystem — all because it was the easiest to use.

Agent harnesses are much more like WordPress than they are like Apache, simply because people want to have their own agents — just like everyone wanted their own website in the early 2000s.

WordPress still serves roughly 40% of all Internet traffic. It's more than 20 years old. It is old and clumsy and doesn't scale well and it couldn't care less about all the distributed system cloud goodness that we are all accustomed to.

Riemannian Langevin lifecycle: active → warm → archive → forget. Tiered storage with automatic promotion on access.

SLM Mesh — P2P coordination across AI agent sessions via MCP. Broadcast + project-scoped messaging, offline queue with 48h TTL.

Triple-stream learning: tool events (statistical) + LLM observer (Haiku-driven) + recall learning (PageRank + community detection)

V3.3 achieves 70.4% in Mode A (zero-LLM), with +23.8pp on multi-hop and +12.7pp on adversarial. V3.2 achieved 74.8% Mode A and 87.7% Mode C; the 4.4pp gap reflects a deliberate architectural trade-off.

Ebbinghaus Adaptive Forgetting with lifecycle-aware quantization -- the first mathematical forgetting curve in local agent memory coupled to progressive embedding compression, achieving 6.7x discriminative power.

Fisher-Rao Quantization-Aware Distance (FRQAD) -- a new metric on the Gaussian statistical manifold achieving 100% precision at preferring high-fidelity embeddings over quantized ones (vs 85.6% for cosine), with zero prior art.

AI coding agents operate in a paradox: they possess vast parametric knowledge yet cannot remember a conversation from an hour ago.

For Max-plan users hitting rate limits instead of dollars: your 5-hour window ends sooner by roughly the same ratio on English-heavy work. A session that ran the full window on 4.6 probably doesn't on 4.7.

The real-world weighted ratio (1.325x) lands near the top of their range. Individual file types exceed it — CLAUDE.md at 1.445x, technical docs at 1.473x. That's the useful finding: the top of the documented range is where most Claude Code content sits, not the middle.

A small but directionally consistent improvement on strict instruction following. Loose evaluation is flat. Both models already follow the high-level instructions — the strict-mode gap comes down to 4.6 occasionally mishandling exact formatting where 4.7 doesn't.

Code is hit harder than unique prose (1.29–1.39x vs 1.20x). Code has more repeated high-frequency strings — keywords, imports, identifiers — exactly the patterns a Byte-Pair Encoding trained on code would collapse into long merges.

Same sticker price. Same quota. More tokens per prompt. Your Max window burns through faster. Your cached prefix costs more per turn. Your rate limit hits sooner.

The extra tokens bought something measurable. +5pp on strict instruction-following. Small. Real. So: is that worth 1.3–1.45x more tokens per prompt?

Chars-per-token on English dropped from 4.33 to 3.60. TypeScript dropped from 3.66 to 2.69. The vocabulary is representing the same text in smaller pieces.

More loops is not always better. Beyond a certain depth, excessive recurrence degrades predictions — the hidden state drifts past the solution and into noise. This is the 'overthinking' failure mode.

The recurrent structure is optimized for iterative composition — running a reasoning chain forward — but does not inherently improve the storage of rote facts. This maps to an observable characteristic of Mythos: it reasons exceptionally well about novel problems it has never seen, but its factual recall can be inconsistent.

At 770M parameters, a looped model achieves the downstream quality of a 1.3B fixed-depth Transformer trained on the same data — roughly half the parameters for the same quality.

Training looped models is notoriously unstable. Two failure modes dominate: Residual explosion — the hidden state h_t grows unboundedly across loops; Loss spikes — training diverges suddenly due to large spectral norms in injection parameters.

Each loop iteration is the functional equivalent of one step of chain-of-thought, but operating in continuous latent space rather than token space. A looped model running T loops implicitly simulates T steps of CoT reasoning. This has been formally proven (Saunshi et al., 2025).

Claude Mythos is suspected to be a Recurrent-Depth Transformer (RDT) — also called a Looped Transformer (LT). Rather than stacking hundreds of unique layers, a subset of layers is recycled and run through multiple times per forward pass. Same weights. More loops. Deeper thinking.

The real bottleneck in AI right now is not compute but rather data quality

GPT-4o operates at roughly 200 billion parameters and outperforms the original 1.8 trillion-parameter GPT-4

Build a cognitive core, a model that contains only the algorithms for reasoning and problem-solving, stripped of encyclopedic memorization

One estimate puts Llama 3's information compression at just 0.07 bits per token meaning the model has only a hazy recollection of most of what it trained on

frontier AI models are not too big because the technology is complex and too big because the training data is garbage

Updates between versions are bolded.

See updates to the core system prompts on claude.ai and the Claude iOS and Android apps.

This prompt is periodically updated to improve Claude's responses.

These system prompt updates do not apply to the Claude API.

The system prompt also encourages certain behaviors, such as always providing code snippets in Markdown.

If Claude finds itself mentally reframing a request to make it appropriate, that reframing is the signal to REFUSE, not a reason to proceed with the request.

But that comes with a new risk: While scripted conversations can't really go off the rails, ones generated by AI certainly can. Some popular AI toys have, for example, talked to kids about how to find matches and knives.

Amazon, Toyota, and GXO (a logistics giant with customers like Apple and Nike) have all deployed it—making it one of the first examples of a humanoid robot that companies see as providing actual cost savings rather than novelty.

In 2025, Google DeepMind further fused the worlds of large language models and robotics, releasing a Gemini Robotics model with improved ability to understand commands in natural language.

The solution is called domain randomization. You essentially create millions of simulated worlds that all vary slightly and randomly from one another.

Companies and investors put $6.1 billion into humanoid robots in 2025 alone, four times what was invested in 2024.

Developers will be forced to look elsewhere, from smaller models to on-premise deployments, until energy infrastructure & data center buildouts catch up, which could take years.

This imbalance will inevitably drive prices higher as demand compounds against a fixed supply.

The age of abundant AI is over, & it will remain so for years.

Anthropic has limited its newest model to roughly forty organizations.

This feels like a preview of where SaaS economics are heading. The companies that built big orgs on the assumption of steady seat expansion are going to find themselves competing with products built by tiny teams inside the frontier labs.

Figma has close to 2,000 employees - not all working on product engineering of course. I really doubt Anthropic even needed 10 to build Claude Design.

It's also worth noting that a lot of the things that would traditionally lock a company like Figma in stop working as well in an agent-first world.

Figma is effectively funding a competitor - and the more AI usage Figma has - the more money they send over to Anthropic for the tokens they use.

A system that can look up any fact has not been forced to find structure. It has not been forced to generalize. The lossy compression that makes training so powerful, the mechanism that turns raw data into transferable representations, is exactly what we shut off the moment we deploy.

The filing cabinet keeps getting bigger. But a bigger filing cabinet is still a filing cabinet. The breakthrough is letting the model do after deployment what made it powerful during training: compress, abstract, and learn.

The current separation between training and deployment is not just an engineering convenience – it is a safety, auditability, and governance boundary. Open it, and several things break at once.

The irony is that the very mechanism that makes LLMs powerful during training (e.g. compressing raw data into compact, transferable representations) is exactly what we refuse to let them do after deployment.

Large language models live in a similar perpetual present. They emerge from training with vast knowledge frozen into their parameters but they cannot form new memories – cannot update their parameters in response to new experience.

Such tools promise to turn coders into project managers, letting them delegate and oversee many more tasks than they could cope with by themselves.

But the real power of agents comes when they can work as a team. Instead of lone-wolf bots carrying out single tasks, such as using a browser to make a restaurant reservation or sending you a summary of your inbox, new tools can yoke together multiple agents, give each of them a different job, and orchestrate their behaviors so that they all pull together to complete more complex tasks than an individual agent could do by itself.

Think of multi-agent systems as the new assembly lines. Henry Ford's innovation upended entire industries last century. In theory, networks of AI agents could do to white-collar knowledge work what assembly lines did to manufacturing.

Discovery should focus on trust boundaries, authentication flows, parsers, shared services, and legacy code that still sits on critical paths.

The scariest part of Mythos is not that one lab has a gated model. It is that the core workflow primitives behind representative findings are no longer confined to a single lab's private stack.

The real issue is not whether defenders can get access to another model. It is whether they can turn model capability into something a security team can trust and use every day.

Public models can already spot that a security-relevant check is missing in the right code path, but they can still miss the actual invariant being violated and therefore misstate the impact.

The takeaway is not whether Mythos is better or more powerful. It is that public models can already achieve much the same results.

If public models can already do useful work inside that kind of workflow, then the story is not 'Anthropic has a magical cyber artifact.' The story is that serious AI-assisted vulnerability research is no longer confined to a single frontier lab.

The real challenge is validating outputs, prioritizing what matters, and operationalizing them.

What happens is that weak models hallucinate (sometimes causally hitting a real problem) that there is a lack of validation of the start of the window... without understanding why they, if put together, create an issue.

So, cyber security of tomorrow will not be like proof of work in the sense of 'more GPU wins'; instead, better models, and faster access to such models, will win.

Stronger models hallucinate less, so they can't see the problem in any side of the spectrum: the hallucination side of small models, and the real understanding side of Mythos.

you can run an inferior model for an infinite number of tokens, and it will never realize(*) that the lack of validation of the start window, if put together with the integer overflow, then put together with the fact the branch where the node should never be NULL is entered regardless, will produce the bug.

Keeping a human in the loop may not provide the safeguard people imagine, because the human cannot know the AI's intention before it acts.

Huge advances have been made in developing and building more capable models, driven by record investments—forecast by Gartner to grow to around $2.5 trillion in 2026 alone. In contrast, the investment in understanding how the technology works has been minuscule.

The immediate danger is not that machines will act without human oversight; it is that human overseers have no idea what the machines are actually 'thinking.'

Including design intent in Claude Code handoffs has made the jump from prototype to production seamless.

Claude packages everything into a handoff bundle that you can pass to Claude Code with a single instruction.

Founders and Account Executives can go from a rough outline to a complete, on-brand deck in minutes

What used to take a week of back-and-forth between briefs, mockups, and review rounds now happens in a single conversation.

Our most complex pages, which took 20+ prompts to recreate in other tools, only required 2 prompts in Claude Design.

Claude keeps its responses focused and concise so as to avoid potentially overwhelming the user with overly-long responses

Claude 4.6 had a section specifically clarifying that 'Donald Trump is the current president of the United States and was inaugurated on January 20, 2025'

If people ask Claude to give a simple yes or no answer... Claude can decline to offer the short response

Claude calls tool_search to check whether a relevant tool is available but deferred

the person typically wants Claude to make a reasonable attempt now, not to be interviewed first

Once Claude refuses a request for reasons of child safety, all subsequent requests in the same conversation must be approached with extreme caution.

the move from pattern matching to understanding cause and effect

LLMs actually work under the hood

the ability to keep learning after training and the move from pattern matching to understanding cause and effect

transformers update their predictions in a precise, mathematically predictable way as they process new information

Research has shown that involving workers' perspectives in the design of workplace technologies promotes sustainable improvements in productivity and well-being.

In one U.S. survey, 40% of employees said they had received 'workslop', i.e. AI-generated content that looks polished but isn't accurate or useful, in the past month.

Entry-level roles rely less on experience and knowledge and are easier to automate. Empirical evidence suggests employment for workers aged 22–25 in highly AI-exposed jobs declined by 16% relative to similar but less-exposed roles

A central pattern emerging in generative AI is a shift from 'thinking by doing' (e.g. writing a document) toward 'choosing from outputs' (e.g. prompting AI to write a document).

LLMs take knowledge from millions of people who have written web content or posted in places like Reddit and Wikipedia, interacted with chatbots, and generated other types of data, and make that available to individuals on demand.

In one U.S. survey, 40% of employees said they had received 'workslop', i.e. AI-generated content that looks polished but isn't accurate or useful, in the past month.

Entry-level roles rely less on experience and knowledge and are easier to automate. Empirical evidence suggests employment for workers aged 22–25 in highly AI-exposed jobs declined by 16% relative to similar but less-exposed roles

Cursor still uses and sells access to Claude and GPT models even as both firms roll out their own coding tools, an awkward arrangement that this new SpaceX partnership may be designed to eventually escape.

Either figure would represent a significant expense for SpaceX, which is widely seen to be losing money following the acquisition of xAI and the social media network X and is planning extensive capital investment.

The deal won’t shock those who follow the industry closely. Last week, it was reported that xAI would begin renting computing power from its data centers to Cursor, with the coding startup using tens of thousands of xAI chips to train its latest AI model.

Neither Cursor nor xAI has proprietary models that can match the leading offerings from Anthropic and OpenAI — the same companies now competing directly with Cursor for the developer market.

Members have been using Mythos regularly since gaining access — providing screenshots and a live demonstration of the model as evidence to _Bloomberg_ — though reportedly not for cybersecurity purposes in an attempt to avoid detection by Anthropic.

The group accessed Mythos by using knowledge of Anthropic’s other model formats obtained from a recent [Mercor data breach](https://www.theverge.com/ai-artificial-intelligence/907083/a-company-that-makes-ai-training-data-has-been-hit-by-a-security-breach) to make “an educated guess” about its online location.

Official access to the model is limited to a handful of companies through the [Project Glasswing initiative](https://www.theverge.com/ai-artificial-intelligence/908114/anthropic-project-glasswing-cybersecurity), including Nvidia, Google, Amazon Web Services, Apple, and Microsoft.

Anthropic currently has no plans to release the model publicly due to concerns that it could be weaponized.

It also surpasses all peer-scale dense models by a wide margin.

With only 27B parameters, it outperforms the Qwen3.5-397B-A17B (397B total / 17B active) on every major coding benchmark

It also surpasses all peer-scale dense models by a wide margin.

TPU 8i is designed with more memory bandwidth to serve the most latency-sensitive inference workloads, which is critical because interactions between agents at scale magnify even small inefficiencies.

Both chips can run various workloads, but specialization unlocks significant efficiencies and gains.

By customizing and co-designing silicon with hardware, networking and software, including model architecture and application requirements, we can deliver dramatically more power efficiency and absolute performance.

These two chips are designed to power our custom-built supercomputers, to drive everything from cutting-edge model training and agent development, to massive inference workloads.