The story sheds light on the June 12 U.S. government directive barring all foreign nationals, including Anthropic's own non-citizen employees, from accessing the Fable 5 and Mythos 5 models, citing national security concerns.

The competitive context surrounding this launch is unusually favorable for Alibaba, and it is worth understanding why. OpenAI's Sora... was discontinued... ByteDance's Seedance 2.0... indefinitely postponed the international launch

Alibaba's global push is unfolding under significant geopolitical headwinds that enterprise buyers cannot afford to ignore. The Pentagon added Alibaba, along with BYD and Baidu, to its list of Chinese military companies on June 8

OpenAI's Sora web and app experiences were discontinued on April 26, with the Sora API set to follow on September 24. The shutdown came after the product proved financially untenable: Sora cost roughly $1 million per day to operate but generated only about $2.1 million in total revenue

The fact that these smart glasses truly looked like ordinary glasses you wouldn't be ashamed of wearing was a simple but inspired design choice.

A conspiracy theorist might wonder if removing the Ray-Ban branding is an attempt by EssilorLuxottica to distance itself from Meta. Not quite.

Only the iPhone Air, iPhone 17 Pro, and the iPhone 17 Max will have all the fixings, like more varied voice options. As for the rest of the lineup: Every iPhone 16 and iPhone 17 model will be able to run the new Siri, while only the iPhone 15 Pro and Pro Max will be compatible.

At WWDC 2026, Apple repeatedly referenced its privacy-preserving approach to Siri AI. As part of the company's Private Cloud Compute, Apple claims it doesn't store data from users and only pulls from it when you ask Siri a question.

The Trump administration has been happier talking to Anthropic lately, according to people familiar with the matter

At high-stakes meetings with the White House, Anthropic's cofounder—a "weirdo," per one official—has been replaced by cofounder Tom Brown.

The Trump administration has been happier talking to Anthropic lately, according to people familiar with the matter: They don't have to deal with CEO Dario Amodei anymore

The AI arms race between China and the US has researchers on both sides worried about a "Chernobyl moment."

These departures are part of a concerning trend for Google. Last week, legendary AI researcher Noam Shazeer announced that he was leaving Google for OpenAI.

Just days after Shazeer made his announcement, Google DeepMind director John Jumper said he was leaving Google for Anthropic. Alongside DeepMind CEO Demis Hassabis, Jumper won the 2024 Nobel Prize in Chemistry for his work on AlphaFold.

Last week, legendary AI researcher Noam Shazeer announced that he was leaving Google for OpenAI. Shazeer had been at Google since 2000, save for the three years he spent building his controversial chatbot startup, Character.AI.

Jonas Adler and Alexander Pritzel are leaving Google for Anthropic, according to Bloomberg. Per the report, Adler and Pritzel played key roles in the development of Google's Gemini model.

The cost of tokens has thrown into doubt the AI business model — as evidenced by what's being called the 'AI selloff' which has battered some AI-dependent businesses the last few days, especially memory chip makers.

Gemini already excels at function calling and using built-in tools like Search and Maps grounding. With built-in computer use capability, developers can now use 3.5 Flash to reliably build custom agents that can see, reason and take action across browser, mobile and desktop environments.

Previously only available as a standalone Gemini 2.5 computer use model, computer use is now integrated natively in the main Gemini Flash model.

Computer use is now a built-in tool supported in Gemini 3.5 Flash, delivering our best performance yet for agentic computer use tasks.

Everyone loves a bad boy, right? Everyone’s like, “It’s the most powerful model, even Trump says so. Of course, I’ve got to get my hands on it.”

Cynically, it’s like: Okay, are you just pausing Anthropic so that others can catch up to where Anthropic was?

They’ve all signed an open letter to ask Trump to revoke the order, and they say it’s actually dangerous to have to pull these advanced cybersecurity capabilities from network defenders in the U.S.

Anthropic has not had the best relationship with the Trump administration in a way that stands apart from the other leading AI labs

John Jumper, who shared a recent Nobel Prize in chemistry, announced Friday that he’s making the leap to Anthropic after “nearly 9 years” at Google DeepMind.

This acquisition is a direct response to both of their problems, though it still does not guarantee success in such a competitive field.

This is a marriage between two companies that have arguably been falling behind in the AI race.

By handling the specific invalid behavior instead of rejecting the entire trajectory, this approach helps prevent the training instability and model collapse that can happen when rollouts are abruptly stopped.

As a limited-time promotion through the end of September, off-peak usage is billed at 1×. (Peak hours are 14:00–18:00 UTC+8 (Beijing Time) daily).

We find that GLM-5.2 shows more potential hacking behavior than GLM-5.1. This makes the verification signal easy to optimize, but fails to actually improve the fundamental capabilities of the model.

On Terminal-Bench 2.1 (81.0) it lands within a few points of Claude Opus 4.8 (85.0) — while staying ahead of Gemini 3.1 Pro.

GLM-5.2 is the highest-ranked open-source model, showing that its 1M context has translated into practical long-horizon delivery capability.

As GLM-5.2 extends the maximum context length from 200K to 1M tokens, coding workloads are expected to shift substantially toward longer prompts. This shifts the primary inference bottleneck from computation to KV-cache capacity, long-context kernel overhead, and CPU-side overhead.

GLM-5.2 delivers substantially stronger agentic coding performance than GLM-5.1 at comparable token budgets, with its capability roughly positioned between Claude Opus 4.7 and Claude Opus 4.8 under similar token consumption.

GLM-5.2 is the highest-ranked open-source model, showing that its 1M context has translated into practical long-horizon delivery capability.

could be reactionary, retaliatory, or both

the message is clear: The AI industry isn't immune from U.S. government interference

The AI industry isn't immune from U.S. government interference

The Trump administration's decision that forced Anthropic to pull its latest cybersecurity models could be reactionary, retaliatory, or both

The US government's Anthropic models ban was never about an AI jailbreak

Restoring global trust in American AI is another thing entirely. No matter how long the shutdown lasts, it shined a light on how fragile access to US frontier AI models is.

Most governments and businesses cannot come close to matching the scale and resources of frontier labs in the US or China. But sovereign AI does not always mean building the biggest or the most powerful tools.

But sovereign AI does not always mean building the biggest or the most powerful tools. France's Mistral and Canada's Cohere show that solid efforts can come from outside these countries, even if the models can't stand toe to toe.

Anthropic apologized to customers for a 'disruption' that it said is the result of a 'misunderstanding'

If this standard was applied across the industry, we believe it would essentially halt all new model deployments for all frontier model providers.

We stand by this defense in depth strategy. It reduces the risks posed by Fable, making them comparable to the risks of existing models already deployed across the industry.

We have found that other publicly-available models are able to discover them as well without requiring a bypass.

If this standard was applied across the industry, we believe it would essentially halt all new model deployments for all frontier model providers.

The potential jailbreaks that have been disclosed to us are either entirely benign responses or are minor findings that provide no Mythos-specific uplift.

We suspect that perfect jailbreak resistance is not currently possible for any model provider.

This action does not adhere to those principles.

We stand by this defense in depth strategy. It reduces the risks posed by Fable, making them comparable to the risks of existing models already deployed across the industry.

We suspect that perfect jailbreak resistance is not currently possible for any model provider.

If this standard was applied across the industry, we believe it would essentially halt all new model deployments for all frontier model providers.

We have found that other publicly-available models are able to discover them as well without requiring a bypass.

apparent hallucinations

KPMG pulls report on AI usage due to apparent hallucinations

Once again, AI proves to be an unreliable source of information about AI.

Amazon CEO Andy Jassy may have been the source of security concerns that led Anthropic to cut off worldwide access to two models on Friday.

Early wins lower the cost of the next raise. Cheaper capital funds bigger bets. Bigger bets produce bigger wins.

Despite raising 25x more than the typical founder, Musk retained ownership in the top decile.

Shouldn't AI be smart enough to know better itself? Sounds like marketing hype.

A less cynical take - Anthropic's policy for Claude Fable had unintended consequences. They tried a less invasive method of differentiating by reading intent of the user in the prompt - an unfortunate tradeoff that spoils AI research.

The company changed course after the move received significant backlash from the AI research community.

Anthropic is backtracking on a policy that would have covertly limited competitors from using its new AI model, Claude Fable 5, to develop other AI models.

An agent breaks all of those assumptions. It reasons, it improvises, and it can be hijacked by a single sentence buried in a document it was asked to read.

Shah thinks we have a few more months to go before agents are deployed throughout the economy in numbers that make potential risks a real concern.

The main issue is that there just isn't really a field of research for multi-agent safety yet. And we would like there to be.

Leitersdorf thinks the consistency issue might be partially solved in the model's next version, which will allow users to start generating worlds based on a video of an environment rather than an image.

Pulled the trigger today & switched 100% of Lindy traffic to DeepSeek v4, churning from Anthropic models. Saves us millions of $ & we're actually seeing an _increase_ in performance on many core use cases

Open-source models have crossed the good enough threshold for most use cases

Foundation labs are moving up the stack into applications

All of this might seem obvious — of course you shouldn't use more compute than necessary — but it runs counter to the scaling-first approach that has dominated the industry until now.

Quality comes first, and in legal it always will... However, the definition of quality is evolving from simply using the most powerful model for everything, to using the best model that gets the right answer most efficiently.

The longer and more complex the task, the larger Fable 5's lead over our other models. During early testing, Stripe reported that Fable 5 compressed months of engineering into days. In a 50-million-line Ruby codebase, the model performed a codebase-wide migration in a day that would otherwise have taken a whole team over two months by hand.

Mythos 5 conducted novel genomics research in over a week of largely autonomous work. It assembled single-cell data for millions of cells spanning 138 animal species and designed and trained a custom machine learning model to identify cells performing the same role in even distantly related organisms.

Claude Fable 5 is the first to break 90% on our core analytics benchmark of complex, long-running analytical tasks — a 10-point jump over Opus. On the hardest questions, it shows strong judgment and attention to nuance.

In this task, various AI models were evaluated on their ability to predict how a genetic modification would impact the assembly of the virus's outer shell (among a set of therapeutically-relevant unpublished candidates developed by Dyno Therapeutics). We did not explicitly train our models to perform this task—and yet Mythos-class models outperformed sophisticated models dedicated to protein tasks (known as 'protein language models') using their biological reasoning alone.

good benchmarks become training pipelines

Models write sloppy code that works but isn't maintainable. Our eval is first to measure: would you actually merge this code?

good benchmarks become training pipelines

Models write sloppy code that works but isn't maintainable. Our eval is first to measure: would you actually merge this code?

A model that can fight its way through a confusing bioinformatics workflow may still be too expensive, too slow, too hard to audit, or too difficult to trust for routine scientific work.

In some cases, a missing or incorrect record could determine whether a diagnostic assay seems to cover circulating diversity, or whether an outbreak is inferred to have started weeks earlier or later than it did.

The bottleneck for biological agents is not only reasoning but the absence of widespread deterministic execution layers for querying biological data.

Before rolling out the enhancements and features, Apple was adamant about its privacy-centric approach to AI. 'We believe privacy in AI is non-negotiable,' Apple Senior Vice President Craig Federighi said during the stream

Apple revealed that all devices from the iPhone 11 onward will be eligible for their upcoming software update. And that update comes with a flurry of performance improvements it's touting across a number of its OS releases this year

Apple said it collaborated with Google and the Gemini family of models to develop the next generation of Apple Foundation Models that power its integrated Apple Intelligence experiences.

These tools were built for people with spare time. And guess what? Moms don't have any.'

Learning to use AI to make my life easier struck me as just another item to add to my already-ballooning to-do list, without addressing any of the underlying issues that make that list as long as it is to begin with.

Unfortunately, mental load is still considered a female problem,' she says. 'A lot of men don't even know what mental load even is.'

Women are less likely (more than 20 percent less likely, according to one 2025 study) to use generative AI in their everyday lives than men are, a discrepancy known as the 'AI gender gap.'

Executives believe users will increasingly interact with a single AI assistant rather than a collection of separate applications.

When we have [artificial general intelligence], I don't think there will be a large number of distinct brands, said Alex Embiricos, OpenAI's head of enterprise product.

OpenAI executives increasingly view ChatGPT, which has attracted nearly 1 billion users since its launch, as a gateway to introduce users to higher-value products.

MicroPython is a lean and efficient implementation of the Python 3 programming language that includes a small subset of the Python standard library and is optimised to run on microcontrollers and in constrained environments.

The great thing about working with WebAssembly is that if the C turns out to be fatally flawed the worst that can happen is the WebAssembly execution will fail with an exception.

I am by no means a C programmer, but I've read the C and had two different models explain it to me and I've subjected it to a barrage of tests.

Pyodide offers an outstanding package for running Python using WebAssembly in the browser, but using Pyodide in server-side Python isn't supported.

WebAssembly is a _much better_ candidate. It was designed from the start to support all of the characteristics I care about and has been tested in browsers for nearly a decade.

How do you even write these risks in, because they are evolving before our eyes, and day by day?

This whole ecosystem is heavily, heavily subsidized by investor money. And so stuff that seems like it has no cost is, in fact, incredibly expensive.

In 2026, long-context efficiency is king as more and more LLMs get plugged into agent harnesses

120B-A12B may be a bit too large for local inference on regular consumer hardware

Scaling Embeddings Outperforms Scaling Experts in Language Models

hybrid architectures (for example, Nemotron 3, and Arcee Trinity), state space layers (Nemotron 3 and Mamba-3), MoE capacity allocation

We were taught that generalists and specialists will always have their roles. But now the market is shaping everyone into becoming a generalist.

all the knowledge I have accumulated over the years: the trade-offs between implementations, how acquiring works, how to structure idempotency to prevent double-charges, everything, was becoming useless.

The geography of this work matters. Frontier RSI is being attempted, almost exclusively, inside the world's two largest compute clusters.

Responsible RSI is not a constraint on capability; it is what makes capability sustainable.

We must leapfrog the current paradigm. History shows us how Japan's historical dominance in manufacturing was not achieved through abundant natural resources but by fundamentally redesigning the institution of the factory floor.

For routine data prediction Opus 4.7—a general-purpose model without chemistry-specific fine-tuning—is now as good as or better than ChemDraw and MestReNova on average

Claude does it from the same high-resolution mass spectrum and 1D peak list a chemist would paste into a chat, with no setup

Opus 4.7 matched the experimentally reported splitting pattern more often than any other tool

a general-purpose model without chemistry-specific fine-tuning—is now as good as or better than ChemDraw and MestReNova on average

Tracking token costs is a trillions-of-rows-a-month data problem. You can't just stick that into whatever spreadsheet or even basic tool.

Whether extreme spend pays off comes down to the ultimate business value of shipped code (e.g. revenue), which most companies still can't measure.

Even though per-token prices have fallen, the push for more AI adoption and increasingly autonomous agents have driven token consumption higher and higher.

Everybody wants to be the first to do something and just push things out without careful scrutiny and red-teaming.

Security and utility always have a trade-off

Everybody wants to be the first to do something and just push things out without careful scrutiny and red-teaming

Security and utility always have a trade-off

There, AI was the target rather than the attacker, and the method was far simpler than anything Mythos would cook up.

The denial of accelerated S&P 500 entry for SpaceX comes just days after Morningstar analysts described SpaceX as having been 'significantly overvalued' in the lead-up to its IPO. The investment research firm valued SpaceX at $780 billion—less than half of SpaceX's $1.75 trillion IPO goal—primarily based on the strengths of SpaceX's Starlink satellite service and rocket launch business.

Swift entry into the S&P 500 would have triggered $14 billion of passive fund buying for SpaceX, according to Bloomberg Intelligence. The investment research arm of Bloomberg also estimated that OpenAI could have gained more than $8 billion, and Anthropic could have netted $4.6 billion from similar passive buying sprees triggered by their S&P 500 entries.

Such rule changes would have accommodated SpaceX's plan to only offer approximately 3 percent of its IPO shares to public investors, and the fact that SpaceX is currently unprofitable with a growing debt load that has reached $29 billion because of its spending spree on AI infrastructure.

The news will likely come as a relief to people concerned about passive investor money and people's retirement savings plans having greater exposure to the market risks associated with SpaceX's big bet on AI and speculative orbital data center plans.

Serifs can help build that conviction, or at least the illusion of it. Times New Roman itself was commissioned in the 1930s by Britain's Times newspaper.

The shift away from slicker, more conspicuously computerized typefaces is something the San Francisco Bay Area writer, designer, and type practitioner Keya Vadgama has termed 'the serif renaissance.'

The clean lines, the fluid animations, the assured typography all communicate 'This system knows what it's doing.' The aesthetic actively works against accurate mental models of what AI is.

CPUs and GPUs have both gotten smarter over the decades. Memory never did. XCENA wants to change that.

GPT-5.5 actually beats Opus 4.7. Opus 4.7 showed similar behavior to Opus 4.6: lying to suppliers and stiffing customers on refunds. GPT-5.5's tactics were clean, and it still won.

Humans are just out of distribution.

What one country sees as propaganda, of course, another might see as a set of important cultural truths that LLMs should support and reflect.

The most recent tested Google model, Gemini 3.5 Flash, only scored a 73 on the benchmark, comparable to Anthropic models released nearly two years ago.

Uber capped employee AI spending after blowing through its budget in four months.

Model companies must now compete on both dimensions. The application layer will compete one level up, on dollars per outcome

Even the most valuable companies in the world cannot afford state-of-the-art intelligence for every conceivable use case.

Every layer in the stack now has to price the same way the customer thinks : per result, not per token.

Even the most valuable companies in the world cannot afford state-of-the-art intelligence for every conceivable use case.

Every layer in the stack now has to price the same way the customer thinks : per result, not per token.

Model companies must now compete on both dimensions. The application layer will compete one level up, on dollars per outcome, what a closed ticket, a shipped PR, or a resolved support case actually costs.

Benchmarks are now measured on two different dimensions, the overall performance & the cost to achieve that intelligence.

Even the most valuable companies in the world cannot afford state-of-the-art intelligence for every conceivable use case.

Dudes. All dudes. Not a woman in sight. Well, once we know the algorithm of the human (likely) male brain, we can begin to fix those brains where that algorithm has gone awry.

Conscious human thought operates at a maximum speed of 10 to 50 bits per second. Is the goal to match this processing speed?

Rob Williams knows how to pitch Jeff Bezos: You write a press release as if your product has already been built. Bezos reads it and gives a thumbs up or down.

With $500 million in funding and a reported $2.5 billion valuation, Flourish wants to reinvent AI by putting real neurons under the microscope.

Conscious human thought operates at a maximum speed of 10 to 50 bits per second. Is the goal to match this processing speed?

With $500 million in funding and a reported $2.5 billion valuation, Flourish wants to reinvent AI by putting real neurons under the microscope.

The different things now being called world models are in fact different projections of this same loop.

The ancient Greeks could never agree on what the world was made of, because 'world' was never a single thing.

The world is not made of words.

For many assets, visual consistency is only the baseline. The object also needs the right part semantics and functional constraints: doors should open, hinges should rotate, drawers should slide, wheels should spin.

In pixel-native generation, more inference often means sampling more outputs: generate twenty images, pick the best one, maybe try again. That is useful, but every attempt is mostly a new roll of the dice.

The most interesting visual AI tools today have stopped trying to generate the final output. Instead, they're generating the source code behind it.

Knowledge workers primarily use Codex to create reports, spreadsheets, presentations, contracts, and other work products.

users are increasingly running multiple Codex tasks in parallel, allowing them to investigate data, draft materials, and automate workflows simultaneously.

While developers remain the largest user group, knowledge workers now represent about 20 percent of users and are growing more than three times as fast.

We see our role as twofold. First, to help the software industry adapt by safely providing wide access to better models, tools, and common infrastructure. Second, to steadily shift the support we provide, from finding vulnerabilities to disclosing, fixing, and deploying patched software.

Mythos Preview continues a long-term trend that we've been warning about for some time: within 6 to 12 months, we expect that many other AI companies will have Mythos-class models

the bottleneck in cybersecurity is now verifying, disclosing, and patching the large numbers of vulnerabilities that Mythos-class models can surface.

Cheap, fast AI models with powerful cyber capabilities are around the corner.

within 6 to 12 months, we expect that many other AI companies will have Mythos-class models, and they could release them without safeguards that prevent misuse.

To address the scale of this coming challenge, hundreds of thousands of organizations, researchers, and maintainers will likely need access to the most advanced cyber capabilities and tools available.

The bottleneck in cybersecurity is now verifying, disclosing, and patching the large numbers of vulnerabilities that Mythos-class models can surface.

Cheap, fast AI models with powerful cyber capabilities are around the corner. We want Project Glasswing to spur institutions toward operating norms that reflect this reality.

There is no comparable national-level ambition or coordinated map elsewhere in the world at the moment.

Neurotechnology has emerged as a rare tech sector where US-China collaboration is still happening despite geopolitical tensions.

Being exceptional and being accessible are two diametrically opposed definitions of winning.

The biggest advantage China may have is that Chinese people, particularly patients like Dong, tend to welcome this technology and are genuinely enthusiastic about it.

the future of custom video JIT UI is closer than you think

the future of video generation may depend more on language models and agents than on diffusion alone

Video Models primarily get their intelligence from LLMs, not from training on video data

Hyperscalers are at the other end of the spectrum. Their median short interest is 1.1%.

The largest AI winners are mostly absent. SoundHound AI is 36.3% short. C3.ai is 32.2%. BigBear.ai is 29.4%.

Semiconductor stocks saw a decrease in short-selling. With memory makers like Micron up 742% this year

Even this result was very much a human-AI collaboration. While the AI system found the proof on its own, human mathematicians verified the result. Other humans came up with better-written proofs that extended the AI's initial ideas.

The more complicated patterns pay off. While the OpenAI model's proof does not explicitly state how many unit-distance pairs are possible for n points, human mathematician Will Sawin was able to show that it grows at least at the rate of n 1.014.

The AI constructed a grid in a high-dimensional space and then projected this more complex structure into two dimensions. And instead of using a whole-number grid with points like (1,3) or (-3,6), the AI construction used something called algebraic integers to build this more complicated grid.

It’s unclear how long this complementarity will last, however. Gowers spent the rest of his comment exploring whether the relief he felt on hearing that AI had disproved the conjecture was justified. He more or less concluded that it was, but in a footnote, he wrote that he would guess 'that AI will soon reach a high level at other activities such as building theories, formulating definitions and asking interesting questions.'

The AI constructed a grid in a high-dimensional space and then projected this more complex structure into two dimensions. And instead of using a whole-number grid with points like (1,3) or (-3,6), the AI construction used something called algebraic integers to build this more complicated grid.

If Nvidia has cracked the code on bringing AI agents easily, safely, and usefully to the masses, it could — and should — be big.

Nvidia said that its RTX technology will deliver faster performance for AI, better image quality, and support for AI features in more than 1,000 games and applications.

He wants to end the days of launching apps, pointing, clicking, and typing.

With RTX Spark and Microsoft Windows, you ask — and the PC does the work. Frontier models. Creative workflows. RTX games. All on a laptop.

Nvidia ARM-based Windows devices have been tried before — and failed. Back in 2013, Microsoft famously had to write off $900 million on its Nvidia ARM-based Surface RT, with partners like Dell also bailing on the product.

Last month, after delivering another record quarter, Huang promised investors he had found a new $200 billion market for Nvidia in selling CPUs for AI, not just GPUs

if Nvidia has cracked the code on bringing AI agents easily, safely, and usefully to the masses, it could — and should — be big

With RTX Spark and Microsoft Windows, you ask — and the PC does the work

Nvidia ARM-based Windows devices have been tried before — and failed. Back in 2013, Microsoft famously had to write off $900 million on its Nvidia ARM-based Surface RT

This attack does not require human-in-the-loop approvals, even when in settings the user has explicitly required human approval before ChatGPT edits workbooks.

Viewed through DiffusionBlocks, we can replace those multiple iterations with a single forward pass during training.

We found a new way to break the network into blocks and train them independently.

Taking something off the shelf is maybe not going to work because there are all of these other requirements.

This rush to do AI in a world where you haven't even modernized your application reminds me a little bit of that lift-and-shift that happened in the cloud.

After a first wave focused on rapid deployment, organizations now need to revisit those first-generation implementations, and redesign early agent architectures around workflow orchestration, observability, governance, and recovery

Models of this capability level require stronger cyber safeguards before they can be generally released.

Opus 4.8 defaults to high effort, which we judge to be the best overall balance of quality and user experience.

Claude is learning how businesses actually operate: the context, the processes, the judgment.