Reasoning models show both a one-off jump in performance and a roughly 2-3x faster trend compared to non-reasoning models.

Three of four metrics show strong evidence of acceleration, seemingly driven by reasoning models.

Three of four metrics show strong evidence of acceleration, seemingly driven by reasoning models.

A free, good-enough product is enough to change market dynamics.

The commoditization flywheel : both companies give away complements to drive usage of the core.

Commoditizing complements doesn't always work because focus is scarce even for the largest, fastest growing businesses.

A free, good-enough product is enough to change market dynamics.

Some categories never developed a competitive response to this strategy : email, advertising infrastructure, user-generated video.

The risk of this strategy to the ecosystem is that it makes previously attractive categories no longer viable. Commoditizing the complement does not demand a best-in-class replacement.

If 90% is AI, do we even need a team?

Cursor counted the entire file as AI, even though we can see from the diff that it left plenty of the lines unchanged.

customers should expect PCW values of 85%+, often 95%+. This is not a hallucination and is accurate given how we compute this metric

placing constraints upon them not only helps users and services build trust in them, but it also helps people more easily conceptualise what they do.

lack of a well-defined user agent role in AI that's backed up by transparent, public standards... leaves a gap – it makes it harder for a marketplace to form.

Every time you use an Internet-connected computer, you're trusting someone (and most likely, a multitude) to act on your behalf.

The agent interprets new information and adapts the logic. The engine applies that logic continuously and emits precise updates.

Agents and CDC streams are powerful together because they split the work well.

With change data capture (CDC), the system emits a stream of precise updates: inserts, updates, deletes, each tied to specific records.

The fix is not smarter prompts. It is software built to meet agents halfway.

Humans are not a good target for calm technology.

Today's agents, the copilots, the chatbots are designed to be human like.

A LeadDev survey found 54% of engineering leaders believe AI copilots will reduce junior hiring long-term.

When juniors skip debugging and skip the formative mistakes, they don't build the tacit expertise. And when my generation of engineers retires, that knowledge doesn't transfer to the AI.

The Pentagon told defense CEOs to consolidate or die. Fifty-one major defense contractors collapsed into five.

A nuclear weapons program lost the ability to make a material it invented. The knowledge existed only in people, and the people were gone.

memory-driven experience scaling represents a crucial new frontier for agent scaling

simple checklists evolved into memories with compositional, preventative logic structures

existing TTS methods often discard the exploration trajectory and treat the final answer as the only useful outcome

this self-judgement does not need to be perfectly accurate, as we find ReasoningBank to be quite robust against judgment noise

by over-emphasizing successful experiences, they miss out on a primary source of learning — their own failures

by recording detailed actions instead of tactical foresight, they fail to distill higher-level, transferable reasoning patterns

When a Fugu model is allowed to call itself recursively, reading its own prior output as context and deciding whether to revise its coordination strategy, a new form of test-time scaling emerges.

A core conviction at Sakana AI is that the most capable AI systems will not be monolithic models scaled in isolation, but collections of specialized agents working together.

Sakana Fugu coordinates pools of frontier foundation models to achieve state-of-the-art performance across coding, mathematics, scientific reasoning, etc.

Chinese authorities have reportedly been pushing data centers and public computing projects to use more domestic chips, including through reported bans on foreign-made chips, sourcing quotas, and requirements to pair Nvidia chips with Chinese alternatives.

DeepSeek does not appear to have fully moved beyond Nvidia. The company's technical report reveals that it is using Chinese chips to run the model for inference, but...appears to have adapted only part of V4's training process for Chinese chips.

DeepSeek V4 exceeds them all on coding, math, and STEM problems, making it one of the strongest open-source models ever released.

The filing cabinet keeps getting bigger. But a bigger filing cabinet is still a filing cabinet.

The current separation between training and deployment is not just an engineering convenience – it is a safety, auditability, and governance boundary.

The intelligence lives in the static parameters, and the apparent capabilities change radically depending on what you feed into the window.

GPT‑5.5 found a proof of a longstanding asymptotic fact about off-diagonal Ramsey numbers, later verified in Lean. The result is a concrete example of GPT‑5.5 contributing not just code or explanation, but a surprising and useful mathematical argument in a core research area.

The viable path is trusted access, robust safeguards that scale with capability, and the operational capacity to detect and respond to serious misuse.

We are treating the biological/chemical and cybersecurity capabilities of GPT‑5.5 as High under our Preparedness Framework. While GPT‑5.5 didn't reach Critical cybersecurity capability level, our evaluations and testing showed that its cybersecurity capabilities are a step up compared to GPT‑5.4.

GPT‑5.5 delivers this step up in intelligence without compromising on speed: larger, more capable models are often slower to serve, but GPT‑5.5 matches GPT‑5.4 per-token latency in real-world serving, while performing at a much higher level of intelligence.

The gains are especially strong in agentic coding, computer use, knowledge work, and early scientific research—areas where progress depends on reasoning across context and taking action over time.

We are treating the biological/chemical and cybersecurity capabilities of GPT‑5.5 as High under our Preparedness Framework. While GPT‑5.5 didn't reach Critical cybersecurity capability level, our evaluations and testing showed that its cybersecurity capabilities are a step up compared to GPT‑5.4.

GPT‑5.5 is priced higher than GPT‑5.4, it is both more intelligent and much more token efficient. In Codex, we have carefully tuned the experience so GPT‑5.5 delivers better results with fewer tokens than GPT‑5.4 for most users

The viable path is trusted access, robust safeguards that scale with capability, and the operational capacity to detect and respond to serious misuse.

GPT‑5.5 is our strongest agentic coding model to date. On **Terminal-Bench 2.0,** which tests complex command-line workflows requiring planning, iteration, and tool coordination, it achieves a state-of-the-art accuracy of 82.7%.

GPT‑5.5 delivers this step up in intelligence without compromising on speed: larger, more capable models are often slower to serve, but GPT‑5.5 matches GPT‑5.4 per-token latency in real-world serving, while performing at a much higher level of intelligence.

Without our safeguards in place (which we do to measure a model's raw capabilities), only Mythos Preview and Opus 4.7 completed more than half the tasks.

We also welcome feedback and input from third parties and industry experts. We're currently working with The Future of Free Speech (an independent think tank at Vanderbilt University), the Foundation for American Innovation, and the Collective Intelligence Project

if AI models can answer these questions well (that is, accurately and impartially), they can be a positive force for the democratic process.

No encryption protects that layer. The router can read, change, or replace anything.

Out of 28 paid and 400 free routers: > 9 injected malicious code into tool calls > 17 touched researcher-owned AWS credentials > 1 drained $500k from an Ethereum wallet

Client-side defenses caught 89% of injections. But real protection needs providers to sign responses.

Some attacks only fired after 50 prior calls. Others activated only in auto-approve mode.

a lightweight surrogate trained on them can absorb a significant portion of future traffic at near-zero marginal inference cost

On a 150-class benchmark, the surrogate fully replaces the teacher

Duplicate section heading | Error; reject the file

Unknown component property | Accept with warning

The DESIGN.md format is at version `alpha`. The spec, token schema, and CLI are under active development. Expect changes to the format as it matures.

Components map a name to a group of sub-token properties: ... Variants (hover, active, pressed) are expressed as separate component entries with a related key name.

Unknown section heading | Preserve; do not error

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.

Unknown component property | Accept with warning

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

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.

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

Unfortunately, the attacker got further access through their enumeration.

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.

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

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.

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

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.'

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.

TypeScript 7.0 now performs many steps in parallel, including parsing, type-checking, and emitting.

We have reworked our JavaScript support to be more consistent with how we analyze TypeScript files.

The stable release of TypeScript 7.0 will be published under the `typescript` package and will use the `tsc` entry point.

The new Go codebase was methodically ported from our existing implementation rather than rewritten from scratch.

We spent days loading the system with hundreds of threads, refining rough edges and polishing corners that developers may never see.

Multi-agent orchestration isn't new, but we believe we've built a great experience for working with agents at scale.

What we've found works best for crafting high-quality software is somewhere in between: using AI, and also engaging directly with code.

At one extreme, there's [fully giving into the vibes], and at the other extreme, there's [disabling all AI features].

Ask ten different programmers how they use AI, and you can get ten different answers.

Meta is not alone in pursuing such a vision: Anthropic debuted tech capable of doing this [in 2024] and OpenAI last year announced [“Operator”] – a tool that can use a web browser on a human’s behalf.

Meta, the company built on watching everything its billions of users do online so it can keep them clicking on ragebait and targeted ads, is reportedly now installing surveillance software on employees’ work computers.

Reasoning models are generally assumed to be better at coding tasks, and they do score higher on Pass@1.

While the output passes the tests and is functionally correct, the diff is enormous, and none of those additions were asked for or even necessary.

A common piece of advice for working with AI coding tools is to simply write more tests because if the tests pass, the code is fine.

Because agents have memory and can be guided and corrected in conversation, they get better as teams use them.

They run in the cloud, so they can keep working even when you’re not.

Workspace agents will be free until May 6, 2026, with credit-based pricing starting on that date.

Because agents have memory and can be guided and corrected in conversation, they get better as teams use them.

AI has already helped people work faster on their own, but many of the most important workflows inside an organization depend on shared context, handoffs, and decisions across teams.

And it’s not just office work. Multi-agent tools like Google DeepMind’s Co-Scientist let researchers use teams of AI agents to coordinate literature searches, generate and test hypotheses, design experiments, and more.

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.

That matters because AI hype is dying down, and companies are shifting focus from buzzy pilots to deployment and integration, where cheaper and more customizable tools tend to win.

US tech CEOs believe the best models should stay proprietary, partly so they can recoup enormous training costs and partly out of concern that powerful frontier models could be weaponized. Chinese labs, for their part, are not purely idealistic: Open-source is not only free advertising but also a shrewd workaround.

Chinese open-weight models accounted for 17.1% of global AI model downloads over the year ending in August 2025. That narrowly surpassed the US share of 15.86%—the first time China had led in this metric.

Telling people to avoid using generative AI is increasingly telling them they must avoid taking part in society.

We have not really begun to make this progress with AI. Why, for example, is this dashboard not found on a government website?

to stand out from the AI-generated pack we will need to become so weird and unexpected as to be off-putting to most people

The AI has learned to code. The AI is building itself.

Is this what we signed up for? Is today the day? Did the drones wake up? Did it achieve consciousness? Is it alive?

We have not really begun to make this progress with AI. Why, for example, is this dashboard not found on a government website?

to stand out from the AI-generated pack we will need to become so weird and unexpected as to be off-putting to most people

The 21st-century average American lies in bed staring at their phone. ... Talking for hours and ages to melted sand.

We do not plan to make Claude Mythos Preview generally available, but our eventual goal is to enable our users to safely deploy Mythos-class models at scale.

There will be more attacks, faster attacks, and more sophisticated attacks. Now is the time to modernize cybersecurity stacks everywhere.

In the past, security expertise has been a luxury reserved for organizations with large security teams. Open source maintainers—whose software underpins much of the world's critical infrastructure—have historically been left to figure out security on their own.

The window between a vulnerability being discovered and being exploited by an adversary has collapsed—what once took months now happens in minutes with AI.

AI models have reached a level of coding capability where they can surpass all but the most skilled humans at finding and exploiting software vulnerabilities.

RED outperforms eight competitive baselines, achieving performance gains of up to 19.0% while reducing token consumption by 37.7% ~ 70.4%

alternative solutions are not merely suboptimal but potentially detrimental

We characterize errors as a forest-structured Forest of Errors (FoE) and conclude that FoE makes the First the Best