Since Gemini is built directly in Sheets, it removes the barrier to writing complex formulas for advanced analysis right where you work.

When you encounter a formula error, Gemini can analyze the surrounding data structure to help provide an easy-to-understand explanation of the core issue alongside a corrected version of the formula.

Serves as the first generative core for social world models, a foundation for next-generation AI-native social platforms.

MaineCoon is optimized for social-interactive applications using several novel techniques: self-resampling, cross-modal representation alignment, domain-aware preference optimization, and reinforced online-policy distillation (ROPD).

Intel was once a silicon powerhouse, designing the most cutting-edge CPUs for computers and servers, and building them in its own fabs. But in the 2010s, the big new markets were mobile-phone chips and GPUs for AI and gaming, and Intel rapidly lost ground.

The industry has only shifted paradigms when it just absolutely cannot extend—even one more little bit—out of what it's been doing.

This is like 30% to 50% better in terms of capability. This is probably the first tool that hasn't obviously made business sense right away for ASML.

They would be very happy to have a tool that does one wafer per hour and it costs them a fortune to run. They would build a fab with a thousand of those and be super happy with it.

Microsoft's efficiency-first messaging surrounding the Maia 200 follows its recent trends of stressing the corporation's concern for communities near its data centers... taking great lengths to deafen the backlash to the AI boom.

Microsoft claims the Maia 200 gives 30% more performance per dollar than the first-gen Maia 100, an impressive feat considering the new chip also technically advertizes a 50% higher TDP than its predecessor.

The Maia 200 does beat the B300 in efficiency, however... no outside customers can purchase the Maia 200 directly, the Blackwell B300 Ultra is tuned for much higher-powered use-cases than the Microsoft chip, and the software stack for Nvidia launches it miles ahead of any contemporary.

Maia 200 is built on TSMC's 3nm process node, and it contains 140 billion transistors. The chip can hit up to 10 petaflops of FP4 compute, Microsoft claims, three times higher than Amazon's Trainium3 competition.

The Maia 200 does beat the B300 in efficiency, however, a big win in a day where public opinion against AI's environmental effects is steadily mounting. The Maia 200 operates at almost half of B300's TDP (750W vs 1400W)

Such automation efforts may give Chinese automakers a significant edge in competitiveness as global EV adoption continues to rise—even as US automakers have already been retreating from EV production in the wake of the Trump administration's decisions.

Technological development has the capability of making work safer for the working class and enabling workers to have a shorter work week without losing pay. But in the bosses' and billionaires' hands it's used to pad profits and lay off workers.

Recent events highlight how important open source is to the AI ecosystem, with more nations and enterprises recognizing the risks and costs associated with exclusively depending on closed models.

For SpaceX, the deal is another sign that compute itself has become strategic currency in the AI race.

Reflection has leaned directly into that pitch as the startup, last valued at $25 billion, is trying to build American open-source AI models that can compete with frontier systems from OpenAI, Anthropic and Google.

The deal shows how SpaceX is using its massive data center build-out after its record initial public offering.

The models are finally ready. Costs of inference are getting optimized with open models, and even on-device models.

most great products start out looking like a toy. In the early social days, people saw Twitter as a dumb site where people posted what they had for breakfast

They are native world-builders themselves. They came up playing Roblox and Minecraft, they have no preconceived limitations about what an app is, or what they can do with it.

But two important things have changed, that have completely opened the world-building doors again.

Opening up OAuth to all customers is an important step toward a broader Cloudflare app ecosystem

We gathered additional metrics during the database migrations, and observed considerable performance improvements after the upgrade was complete

We chose an upgrade window when Hydra had the lowest request volume per second to minimize lost token writes

if a refresh token was reused, Hydra would invalidate the whole access and refresh token chain

we decided to do two smaller sequential upgrades rather than doing one large upgrade

Include AI-generated sexualized impersonation as a separate category in standard content reporting and appeal forms, distinct from 'harassment' or 'nudity.'

Meta said that when the content was flagged, the company had no indication that the individual depicted in the video was 'a real person' because they did not report the content.

Broadening the signals of lack of consent in this way would especially benefit non-public figures who are the targets of non-consensual intimate imagery because it would reduce the burden on victims to report the abuse themselves.

The Board finds that AI-generated impersonation is non-consensual by default and should be added to the set of signals the company uses to establish lack of consent.

We would like to thank Deepseek-OCR, Deepseek-OCR-2, PaddleOCR for their valuable models and ideas.

no_repeat_ngram_size= 35

max_length= 32768

Single image supports two configs: gundam or base

Welcome the Era of One-shot Long-horizon Parsing.

Continual improvements through a safety data flywheel, which continually learns from the road how to expand the set of operational design domains for safe deployment.

NVIDIA is the first company accredited by ANAB for an inspection plan that combines cybersecurity, AI, and functional safety.

A diverse AV stack that combines a modular stack and NVIDIA Alpamayo reasoning VLA models for algorithmic AI safety.

For Robotaxis, Safety Must Be Built In, Not Bolted On

NVIDIA Halos is a full-stack, comprehensive safety system that unifies safety elements across vehicle architecture, AI models, chips, software, tools, and services to ensure the safe development and deployment of autonomous vehicles (AVs) from cloud to car.

The NVIDIA DSX reference design for AI factories has zero water consumption — we have eliminated massive amounts of power usage and pretty much all water usage.

In the right geographic location, with the right system design, you don't need any refrigeration equipment. You can just put big radiator coils outside and use the air temperature for all your cooling. It's incredibly efficient.

For agent products, that may matter more than raw benchmark scores.

The specific models Fugu selects and how it coordinates them are proprietary, so this routing information is not exposed by design.

We never stack model fees; you are charged a single rate based on the top tier model involved.

Fugu models surpass publicly accessible frontier models and are shoulder-to-shoulder with Fable 5 and Mythos Preview in various rigorous engineering, scientific, and reasoning benchmarks while delivering frontier capability without the risk of export controls.

Instead of using domain knowledge to prescribe team organization, roles, or workflows, Fugu learns to dynamically assemble agents from a pool and coordinate them through non-obvious but highly efficient collaboration patterns.

Raw output quality is on par with top frontier models, but Fugu showed unusually strong persona stability across long sessions, holding its identity where other models drift.

Fugu Ultra is significantly better than GPT-5.5. It gives comprehensive answers and finds the bugs others miss. Where other tools flag about three issues, Fugu surfaced more than twenty.

the most powerful AI systems will not be isolated monoliths, but collaborative ecosystems.

The effects of early exposure to deoxyglucose persisted even when researchers removed the glucose-like molecule.

helps sustain progress across long-running projects

preserves context, manages complex workflows

break ambitious goals into verifiable steps

determine when to delegate execution to Codex versus when human oversight is most valuable

How Codex helps work continue beyond a single prompt

Our models identified a 23-year-old use-after-free in OpenBSD's kernel implementation of System V semaphores.

Security engineers reviewed every finding before it reached a maintainer... While frontier AI models are highly capable of finding vulnerabilities and patching them, they also produce a high volume of false positives

The completed setup took less than a day. Trail of Bits estimates that building the same lab manually would ordinarily take at least several weeks.

Trail of Bits engineers found that, with limited guidance, GPT‑5.5‑Cyber made useful choices about where to expand coverage, which builds and entry points to probe, and which candidates were too weak to pursue.

When a connection breaks, you should be able to find out why. And an administrator should be able to decide, down to the individual tool, what is available in each part of the organization.

Automated work should run on behalf of a user or a service account, never impersonate the person who wrote it.

Production connectivity has a few non-negotiables. A connector should respect two sets of rules at once: the permissions already set in the source platform, and the controls your administrators set in Mistral Studio or Vibe.

Async agents are moving into everyday work. For an agent to be trustworthy and useful inside an organization, it needs real enterprise data: CRM records, repositories, inboxes, knowledge bases.

What varies is how much you layer on top. Use OCR 4 in pure extraction mode when you want to: Work directly with the raw response

Models building their own software tools might have seemed outlandish not long ago, but it is happening. It would be unwise to rule out the same trajectory in hardware.

We are plausibly entering the early era of physical agentic AI.

This progress is not the result of a concerted effort to improve the robotics capabilities of our models. These improvements, like so many others in the history of LLM development, have emerged from much more general scaling.

it was as or more successful than both human teams while producing almost ten times less code than Team Claude.

Claude Opus 4.7—operating without human assistance—was about 20 times faster than the fastest human team at all tasks completed by our participants less than a year ago.

We now spend much more of our time delegating tasks to many Claudes in parallel.

Today, 65% of our product team's code is created by our internal version of Claude Tag.

Qualcomm Dragonfly AI300 joins the previously announced Qualcomm Dragonfly AI200 and AI250 in its data center solutions portfolio with an annual cadence AI accelerator roadmap

HBC is designed to enable efficient scaling of AI agents to meet the demands of continuous reasoning, memory bandwidth, and real-time responsiveness

> 2x better performance per watt estimate compared to existing product benchmarks for server CPU competitive offerings based on specs

AI300 with HBC Gen 2 is designed to enable another stepwise improvement with a 54x increase over AI200

HBC is designed to enable a 6x increase in bandwidth per watt versus HBM compared to competing published product specifications normalized at card-level

Hyundai still plans its robot army, and 2028 is close. The strike vote does not stop that. It does force a question the whole industry has dodged: when a robot can do the job, who gets to say yes?

The union has drawn a hard line. 'Not a single humanoid robot will be allowed on the production lines without a labour-management agreement,' it said. It wants a veto, not a briefing.

The union wants guarantees on jobs and working conditions as Hyundai adds AI and robots. That issue never appeared in past wage rounds.

Our customers are recognizing that supply shortages in memory and storage will take considerable time to improve, even as we expect industry supply to improve gradually in 2028.

Memory prices have skyrocketed in the last couple years as AI chips eat up all the production capacity of the small crop of vendors.

When completed, we expect approximately half or more of our company revenue to be under these strategic customer agreements

The goal is to move beyond using models to find more vulnerabilities, towards a world of safer software and cyber resilience.

Frontier defensive capabilities should not be concentrated in the hands of a few. Software touches all aspects of life, from critical infrastructure to business applications and government networks.

As AI makes it possible to find and patch more vulnerabilities faster, it also creates more work for maintainers, who need to sift through thousands of reports, many of which are low-quality false positives.

Vulnerability reports, on their own, do not protect anyone. The value comes from validating the issue, understanding its impact, developing and testing a patch, coordinating disclosure, and helping teams deploy the fix.

The bottleneck historically has been finding vulnerabilities, but now defenders are overwhelmed with the number of vulnerabilities found. Instead, the bottleneck is now patching vulnerabilities.

Public reaction on the ClaudeAI subreddit appears to be split into roughly three camps. The majority see the story as an indictment of the government's cybersecurity, citing its inability to hire the required level of talent and its history of leaks. A second large group is skeptical of the claim, considering it sensationalist or even an Anthropic marketing stunt.

The Financial Times reported earlier in June that roughly six Anthropic engineers are embedded directly inside the agency as forward-deployed staff, adapting and customizing Mythos for specific operational applications, with sources indicating the work could extend to infiltrating networks operated by countries including China and Iran.

Anthropic contends that the cited breach was a narrow jailbreak, one that rival models, including OpenAI's GPT-5.5, also exhibit. According to the company, the flagged behavior amounted to asking the model to analyze a codebase and fix identified issues, which revealed a few minor, already known bugs, rather than a genuine autonomous offensive intrusion.

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.