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AI's taste test
Anthropic says Claude Opus 4.7 is more tasteful on work tasks.
Can Claude Write Z80 Assembly Code?
Betteridge’s law applies, but with help and guidance by a human who knows his stuff, [Ready Z80] was able to get a functioning game of Wordle out of the French-named LLM, which is more than w…
Washington’s Scramble to Get Mythos, Anthropic’s Powerful New Model
Federal agencies have requested access to Claude Mythos Preview, which Anthropic says can rapidly identify — and potentially create — new cyberthreats.
OpenAI’s big Codex update is a direct shot at Anthropic’s Claude Code
Codex can now use your macOS apps on its own.
How Dangerous Is Anthropic’s New AI Model? Its Chief Science Officer Explains.
Just how fast is the future coming at us, and how worried should we be? Last week, Anthropic announced Claude Mythos, the latest in what has become the leading family of AI models. In a very short time, AI models from Anthropic and other companies have become shockingly powerful at coding. But Mythos stands out because it is not just able to write code that can hack into critical systems, like those that run financial companies, but also to analyze systems and find and string together vulnerabilities in them, allowing Mythos to evade even sophisticated defenses. Anthropic chose to release Mythos to only about 40 leading corporations, so they could analyze the model’s capabilities and prepare countermeasures, in an effort it calls Project Glasswing. The release of Mythos—and the prospect of similarly powerful AIs to follow—has caused tremors at the top levels of Washington and corporate America. Jared Kaplan, co-founder and chief science officer at Anthropic, is at the center of this breakthrough. We sat down with Kaplan so he could walk us through just how powerful Mythos is, what the implications are for issues like our personal privacy, and what comes next. This conversation has been edited for length and clarity. Sean Fischer: For the layman, how is Claude Mythos different—more powerful, more dangerous—from previous models released by Anthropic? Jared Kaplan: I think most people have experienced the fact that our computers get faster and faster over time and over many decades. There’s this very smooth trend where every 18 months, maybe, computer processing power doubles. I think AI is on a trend in which it’s improving maybe 10 times faster than that. A lot of the earliest work that I did in AI was around identifying this scaling trend in AI. Claude Mythos is the latest model from Anthropic, but it’s not different because it’s qualitatively different. It’s really just kind of the culmination of the trend of AI models getting smarter and smarter at all kinds of general capabilities, from reasoning to software engineering to scientific research to knowledge work. For us, it was the first model to demonstrate very elite-level cybersecurity capabilities. It wasn’t because we trained Claude Mythos to focus on cybersecurity, but it was because, as a byproduct of its general intelligence and its general ability with software, it’s particularly good at identifying vulnerabilities in software and how to exploit them.
We tested Anthropic’s redesigned Claude Code desktop app and 'Routines' -- here's what enterprises should know
For the enterprise, the Desktop GUI is likely to become the standard for management and review, while the CLI remains the tool for execution.
Adobe takes Creative Cloud into Claude Code-esque territory
This is a big step in a new strategic direction for Adobe.
Anthropic draws offers from VCs to invest at up to $800 billion valuation, Business Insider reports
Anthropic has received multiple offers from venture capital firms valuing the Claude maker at as much as $800 billion in recent weeks, more than double its current valuation, Business Insider reported on Tuesday, citing sources.
Anthropic's AI Assistant Claude Is Now Available in Microsoft Word
If you're using Word, you can now try out an alternative AI tool to Copilot, though it's only in beta testing for now.
I use Claude, Gemini, and ChatGPT every day—here's the only one you should pay for
The battle of the bots.
Anthropic brings Claude into Microsoft Word, and legal contract review leads its use cases
Anthropic's Claude for Word beta is live in Microsoft Word for Team and Enterprise users, with legal contract review as the tool's first listed use case
Anthropic's Claude for Word is a new challenge to Microsoft's empire
Anthropic launched a beta version of Claude for Word. It's another challenge to Microsoft's software empire, and a bid to appeal to lawyers.
“AI polls” are fake polls
A few weeks after Donald Trump’s second presidential win, I took the train up from London (where I was living at the time) to Oxford to attend a conference on polls and forecasts of the 2024 election. Most of the attendees were pollsters or academics, but I also watched presentations from Aaru and Electric Twin, two companies that do what is interchangeably called synthetic sampling, silicon sampling, or synthetic audiences. Stripped of startup jargon, that means they use large language models (LLMs) to simulate responses to public opinion polls by having AI agents take on the role of survey respondents. I had already heard of Aaru thanks to some articles with eye-catching headlines like “No people, no problem: AI chatbots predict elections better than humans” in the months leading up to Election Day. The founders were making some big, some might even say far-fetched claims, such as: “within two years, we will simulate the entire globe — from the way crops are grown in Ukraine to how that impacts production of oil in Iraq, trade through the strait of Malacca, and elections for the mayor of Baltimore.” When Semafor asked Aaru’s cofounders — Cameron Fink and Ned Koh — about my boss, they said “we respect all those who came before us.” Nate (as he so often does) shared his thoughts on Twitter: Fink and Koh were relatively good-natured about this back-and-forth when we spoke at Oxford. They even offered to mail me one of the t-shirts featuring Nate’s quote they apparently had made. I never took them up on the offer, which I now somewhat regret. These synthetic sampling companies fell off my radar for a while, but they do still exist. In fact, Aaru recently received a $1 billion valuation. Is what they’re doing anywhere close to the most important frontier in AI development? Not by a longshot, especially when Anthropic just developed a model so adept at exploiting software vulnerabilities that it’s only being released to 40 companies. Still, silicon sampling is increasingly finding its way into public polling. Axios reported in March that “a majority of people trust their own doctors and nurses” based on findings from Aaru — without mentioning that the “people” in that sentence were actually LLMs. Around the same time, the Public Sentiment Institute “boosted” their online sample of 373 real survey respondents with 114 AI agents.1 (Spoiler alert: even the co-founder of Electric Twin doesn’t think that’s a particularly defensible approach.) Polling companies like Qualtrics and Ipsos are also developing synthetic data panels. So, what should we make of these … “polls”? Let’s get one thing out of the way: whatever they are, they’re not polls in the way that term is usually defined. Subscribe You can’t replace polls with AI On one hand, using LLMs to essentially make up fake survey respondents sort of sounds like the dumbest idea ever, one that will at best imperfectly replicate real polls while introducing all sorts of biases. On the other hand, with LLMs improving at a remarkable, perhaps even alarming rate, maybe that means I’m a dinosaur at the ripe old age of 24 because I still want to rely on polls that talk to actual people. I’m not going to argue that synthetic samples are completely useless. In fact, as I’ll return to later, there is evidence that some techniques can replicate topline survey results quickly and cheaply. But the marketing from certain companies can be slightly optimistic. “No traditional poll will exist by the time the next general election occurs,” said Fink in 2024. We’re just 206 days away from the midterms, and based on the fact that I still have to collect a bunch of polls every day, I’d say he should have run that prediction by a sample of AI agents before the interview.2 To see why synthetic samples can’t replace polls, here’s a quick primer on how they work. The simplest version of these models involves taking a LLM (like ChatGPT or Claude), giving it a demographic profile (e.g., a white, college-educated woman who lives in Utah and makes $70k a year), and then asking it to respond to a survey question. You repeat that process a few thousand times using different demographic profiles and end up with a sample of synthetic survey responses. The actual models used by private companies are more sophisticated than this, usually because they incorporate more hypothesized demographic characteristics for each agent and provide them with extra information. Aaru, for example, feeds agents a diet of news and information they’d be likely to consume, while Electric Twin incorporates their customers’ proprietary data about the audience they’re trying to replicate. The way Ben Warner, the co-founder of Electric Twin, explained it to me was “we have a large amount of data on […] for instance, 5,000 people. Can we make an accurate prediction of how they would respond to another question?” Still, it should be obvious why synthetic samples can’t replace polls. Polling is fundamentally a data collection process. We might use surveys to make predictions by feeding them into election forecasts, but the main purpose of a poll isn’t prediction, it’s gathering new data about what people think and how they feel. Silicon sampling, on the other hand, produces no new data. It’s simply a model: you input LLM training data, demographic prompts, and a bunch of other information, and it spits out a prediction for what a poll would say. We love models here, but models aren’t polls. That difference is an important philosophical sticking point for most pollsters I talk to. “I think politics should stay away from [synthetic sampling], because we’re trying to […] represent the voice of the people,” said Natalie Jackson, a vice president at GQR Insights. Democratic pollster John Hagner told me: “I think I’m just incredibly skeptical of this idea. I don’t think it’s research. At that point, you’re asking the machine to tell you what you already believe.” Hagner has seen some presentations of early synthetic sampling experiments, but so far, “if it’s being used in a campaign, people are keeping it incredibly quiet.”3 But Eli, I hear you saying, aren’t polls themselves increasingly governed by modeling decisions? Indeed they are: pollsters’ choices on which sampling method to use, how to define their likely voter models, and how to weight their samples can and do lead to dramatic differences in the results they publish. Aaru even referenced these limitations in the methodology statement included with that maternal mortality “poll” — although I’m using the term “methodology statement” loosely here, because it doesn’t really explain how the model works at all. We can ignore the (frankly preposterous) implication that synthetic sampling isn’t subject to a separate set of biases. The important point is that there’s still a meaningful difference between using weighting and other statistical techniques on actual polling data and using a model to predict what a poll would say. The latter is far closer to election forecasts or techniques like MRP — potentially useful models, but not a replacement for polls.4 To be fair, other synthetic sampling companies are perfectly happy with the distinction between polls and models. Warner compared polling and synthetic sampling to different tools in a toolbox. “The mistake I think we make is we think that these new tools should either work in exactly the same way or somehow replace these old tools,” he said. “Rather than thinking of it as, okay, so we’ve always had the hammer, we’ve always had the screwdriver, now we’ve got a saw. But don’t use a saw to try [to] do the job of a hammer.” A quick comment from Nate Eli didn’t ask me for a comment — rather rude of him, don’t you think? But since I’m editing this story, I figured I’d add a few quick thoughts rather than putting words in his mouth. Beyond the frequently misleading marketing, what bothers me about the AI “poll” hype is that as AI tools make statistical inference cheaper and/or better (note that these are not synonyms) that actually increases the comparative value of collecting original data. You might be able to train a model to make a reasonable estimate of what some hard-to-reach poll respondent would say — say, a young Black man who voted for Trump. (Such a person checks a number of boxes for a voter who is usually hard to reach in surveys.) Indeed, this is closely related to what models like the Silver Bulletin forecast already do. They essentially smooth out the kinks in noisy survey data by making inferences based on past voting patterns or national polls or surveys of other states. But you don’t actually know what these voters think unless you’re reaching them directly. If there’s a shift in opinion among this subgroup, you’re not going to detect it. So if I were running a campaign, I’d invest more in going the extra mile to find a representative sample of those voters. And then I’d hire some smart quants — or Claude? — to figure out the implications for campaign strategy based on proprietary data that my competitors didn’t have access to. -Nate Silver Are these models any good? If synthetic surveys are just a new type of model, the next obvious question is whether the models are at least accurate. The answer very much depends on who you ask. On one end of the spectrum, you have the maximalist argument that synthetic sampling is more accurate than actual polls. “It’s an incredibly challenging problem to go to someone and say ‘hey, we’re going to be more accurate at predicting human behavior than you, even when you talk to your customers directly’,” Koh recently told CNBC. In his view, synthetic sampling isn’t a saw to polling’s hammer, it’s “magic.” There’s certainly evidence that synthetic samples can replicate certain survey toplines. But if Aaru does have any examples of their approach outperforming the polls, they’re keeping those to themselves.5 Aaru’s 2024 election model, for example, had Kamala Harris leading in Michigan, Nevada, Pennsylvania, and Wisconsin on November 4th. And although they’ve since taken down their forecast page, they gave Harris a 50.5 percent chance of winning the race on November 2nd.6 After the election, Fink told Semafor he was happy enough with those results because they were “within margin of error,” a term that is completely meaningless when applied to a “sample” of AI agents. And of course, Aaru says their models have improved since 2024, so supposedly now they’d be more accurate than the polls? Still, their stronger argument is on cost: “We are significantly faster and cheaper than traditional polling, and still more accurate,” said Fink. The first two claims are undeniably true, but the third brings us to the opposite end of the spectrum. Both Jackson and Hagner are skeptical that these models are reliable for anything beyond replicating common survey toplines. “I just […] don’t think the machines are what we want when we’re looking for nuanced views. My example on this is people in Arizona and Nevada in 2024 who voted for Trump and voted for expanding abortion in their states on ballot initiatives,” said Jackson. Hagner identified another issue. Maybe the synthetic respondents, like sycophantic LLMs, are inhuman in one important way: they’re too nice. “The reports that have come through at the meetings that I’ve been at are that the early experiments on this, they cannot get respondents to be as racist or sexist or, frankly, as negative as human respondents,” he said. Academic research mostly agrees on this point. While there are some papers that show promising results when using LLMs to replicate polling data, most show that LLMs suffer from various quirks like producing too few “don’t know” responses and can seriously overpredict the favorability of politicians like Donald Trump and Kamala Harris. They also seem to struggle with too little variation between demographic subgroups, so the difference in predicted opinion between Democrats and Republicans, for example, is too small. When I asked Warner about these studies, his response to these papers was that just because academics can’t get synthetic sampling to work doesn’t mean that the technique doesn’t work in general. “Actually, the argument is, okay, yours does not [work]. That does not mean […] for this complex set of machinery, which uses a lot of investment, a lot of time, a lot of money, you can’t get it to work.” Cards on the table, I’m somewhat sympathetic to this argument because academics aren’t exactly great at making election forecasts. Usually, the people with skin in the game are the most accurate. Warner’s argument is that the approach Electric Twin takes — which includes, for example, making multiple predictions for each synthetic respondent using different models and prompts and subsequently averaging those to get a final prediction in a sort of ensemble forecast — produces better results than the simpler academic models. Warner shared a comparison between his method and the method from a recent academic paper with me, and Electric Twin was indeed able to get more accurate replication. But even still, he acknowledged that synthetic sampling “is not a crystal ball.” “If you asked me, do I think using other data sources will be more accurate than asking somebody who they will vote for, I would probably say no. But if you asked me ‘would your system be useful for our turnout modeling today?’ I would say yes.” For better or worse, it looks like the method is already getting more popular in the market research world. Most of the clients Aaru touts these days are businesses like EY and McDonald’s. And AI will probably begin to pop up in other parts of the political polling process. Pollsters are already using it to code open-ended survey responses, and some firms, like YouGov, are testing using LLMs to ask survey respondents questions. More worryingly, one danger to actual polls is that AI agents can be used to infiltrate online surveys. Most online polls use various checks to prevent that from happening, but there’s conflicting evidence on how effective those filters are and how prevalent AI agents currently are in online panels. If those agents ever become impossible to detect, it might spell the end of online polling, but the solution isn’t to replace all of your respondents with ChatGPT. Silver Bulletin is a reader-supported publication. To receive new posts and support our work, consider becoming a subscriber. Subscribe 1 That particular poll obviously doesn’t meet Silver Bulletin standards for aggregation. But we exclude all Public Sentiment Institute polls from our averages because we classify them as an amateur polling firm. 2 You could argue that Fink meant the next presidential election, but (a) I’m also confident we’ll still have real polls in 2028 and (b) in that case he should have asked an LLM to define “general election.” 3 Quick caveat: that’s reporting from a Democratic pollster. It’s possible that Republicans are more willing to use AI in political campaigns. 4 Indeed, Silver Bulletin does not include “polls” produced by MRP in our forecasts or averages, and we think it’s extremely misleading when their practitioners describe them in a way that suggests original data had been collected among a large number of states or Congressional districts. 5 A recent report from Aaru and EY did show two examples of a synthetic estimate being closer than a survey to a real-world benchmark — but I’d take those findings with a grain of salt because the report reads more like an ad and didn’t involve any sort of prediction being made ahead of time. 6 For comparison, our odds for Harris on the same day were 48.2 percent.
Anthropic closes in on OpenAI as US business use surges
Divergence reflects company’s recent rapid growth owing to strong interest in its Claude Code products
Why Anthropic sent its Claude AI to an actual psychiatrist
Mythos is "the most psychologically settled model we have trained to date."
Patients Are Using Chatbots to Fight Medical Bills, With Mixed Results
While chatbots like Claude and ChatGPT can help narrow the information divide between patients and providers, they can also dispense flawed advice.
Anthropic says its latest AI model is too powerful to be released
Anthropic said Claude Mythos is too good at hacking and that's why you won't be able to use it anytime soon.
AI Giant Anthropic Leans Into Life Sciences With $400M Coefficient Bio Catch
Anthropic in October last year iterated its Claude AI model to better cater to biopharma purposes. Sanofi, Novo Nordisk, AbbVie and others already use Claude in their operations.
Can AI Be My Health Adviser? I Tested Claude and Perplexity With My Real Medical Records to See
Anthropic cuts off OpenClaw support for Claude subscriptions
Anthropic said third-party tools like OpenClaw put an "outsized strain on our systems." OpenClaw's founder said cutting support would be "a loss."
Gemma 4 and what makes an open model succeed
Having written a lot of model release blog posts, there’s something much harder about reviewing open models when they drop relative to closed models, especially in 2026. In recent years, there were so few open models, so when Llama 3 was released most people were still doing research on Llama 2 and super happy to get an update. When Qwen 3 was released, the Llama 4 fiasco had just gone down, and a whole research community was emerging to study RL on Qwen 2.5 — it was a no brainer to upgrade. Today, when an open model releases, it’s competing with Qwen 3.5, Kimi K2.5, GLM 5, MiniMax M2.5, GPT-OSS, Arcee Large, Nemotron 3, Olmo 3, and others. The space is populated, but still feels full of hidden opportunity. The potential of open models feels like a dark matter, a potential we know is huge, but few clear recipes and examples for how to unlock it are out there. Agentic AI, OpenClaw, and everything brewing in that space is going to spur mass experimentation in open models to complement the likes of Claude and Codex, not replace them. Especially with open models, the benchmarks at release are an extremely incomplete story. In some ways this is exciting, as new open models have a much higher variance and ability to surprise, but it also points at some structural reasons that make building businesses and great AI experiences around open models harder than the closed alternatives. When a new Claude Opus or GPT drops, spending a few hours with them in my agentic workflows is genuinely a good vibe test. For open models, putting them through this test is a category error. Something else to be said about open models in the era of agents is that they get out of the debate of integration, harnesses, and tools and let us see close to the ground on what exactly is the ability of just a model. Of course, we can’t test some things like search abilities without some tool, but being able to measure exactly the pace of progress of the model alone is a welcome simplification to a systematically opaque AI space. Share The list of factors I’d use to assess a new open-weight model I’m considering investing in includes: Model performance (and size) — how this model performs on benchmarks I care about and how it compares to other models of a similar size. Country of origin — some businesses care deeply about provenance, and if a model was built in China or not. Model license — if a model needs legal approval for use, uptake will be slower at mid-sized and large companies. Tooling at release — many models release with half-broken, or at least substantially slower, implementations in popular software like vLLM, Transformers, SGLANG, etc due to pushing the envelope of architectures or tools. Model fine-tunability — how easy or hard it is to modify the given model to your use-case when you actually try and use it. The core problem is that some of these are immediately available at release, e.g. general performance, license, origin, etc. but others such as tooling take day(s) to week(s) to stabilize, and others are open research questions — with no group systematically monitoring fine-tunability. In the early era of open models, the days of Llama 2 or 3 and Qwen pre v3.5, the architectures were fairly simple and the models tended to work out of the box. Some of this was due to the extremely hard work of the Llama, Qwen, Mistral, etc. developer teams. Some is due to the new models being genuinely harder to work with. When it comes to something like Qwen 3.5 or Nemotron 3, with hybrid models (either gated delta net or mamba layers), the tooling is very rough at release. Things you would expect to “just work” often don’t. I’ve been following this area closely since we released Olmo Hybrid with a similar architecture, and Qwen 3.5 is just starting to work well in the various open-source tools that need to all play nice together for RL research. That’s 1.5 months after the release date! This is just to start really investing more into understanding the behavior of the models. Of course, others started working on these models sooner by investing more engineering resources or relying on partially closed software. The fully open and distributed ecosystem takes a long time to get going on some new models. All of this is lead-in for the most important question for open models — how easy is it to adapt to specific use-cases? This is a different problem for different model sizes. Large MoE open-weight models may be used by entities like Cursor who need complex capabilities in their domain, e.g. Composer 2 trained on Kimi K2.5. Other applications can be built on much smaller models, such as Chroma’s Context-1 model for agentic search, built on GPT-OSS 20B. The question of “which models are fine-tunable” is largely background knowledge known by engineers across the industry. There should be a thriving research area here to support the open ecosystem model. The first step is to understand characteristics of different base and post-trained models to understand what they look like. The second step is to tune pretraining recipes for open models so they’re more flexible. Interconnects AI is a reader-supported publication. Consider becoming a subscriber. Subscribe For The ATOM Project and other Interconnects endeavors, we’ve put in substantial effort to measuring adoption trends in the open ecosystem. Everything takes a long time to unfold after a model is first publicly available — and adaptability is why. What we know for sure now, when Qwen has been going from strength to strength with its releases, is that technical staff across the industry has gotten comfortable working with Qwen models. Countless research methods and datasets were made to work with Qwen. It’ll take patience for any other model family to get to this point — a patience I’m not sure many open model builders have. This takes us to Gemma 4, Google’s latest open models. Gemma 3 was released more than a year ago, in March of 2025, and is a bit underrated. Gemma 4 comes in 4 sizes for now, with a bigger, MoE model of over 100B total parameters rumored but not released yet. The models we have today come in sizes of ~5B dense, 8B dense, 26B total 4B active MoE, and 31B dense. I’m most excited that they’re finally adopting a standard Apache 2.0 open source license. This’ll massively boost adoption. The standard of better licenses for strong open-weight LLMs was set by mostly Chinese open model labs in the last 1-2 years, and now U.S. companies are following suit. I will personally be so happy if the horrible Llama licenses and Gemma terms of service were an ~18-month transient dynamic of the industry being nervous about releasing strong open models. The Gemma 4 scores look very solid, the small models have incredible benchmark scores (especially in general domains like LMArena) and the 31B model rivals the recent Qwen 3.5 27B, which is the leading member of that class. The ~30B size range is an important one, as it’s accessible both to researchers and to enterprises looking to deploy the model in real use-cases. Where the 7B model scale is the default for tinkering and research, a 30B model is the default for seeing if an open model can unlock substantial value in your specific workflow — a good mix of intelligence, low price, tractability for downstream training, etc. This takes us back to the above adoption criteria I mentioned for open models and the bigger question — do I think Gemma 4 will be an overwhelming success? Previous Gemma models have been plagued by tooling issues and poorer performance when being finetuned. Gemma 4’s success is going to be entirely determined by ease of use, to a point where a 5-10% swing on benchmarks wouldn’t matter at all. It’s strong enough, small enough, with the right license, and from the U.S., so many companies are going to slot it in. I’m cautiously optimistic that Gemma 4 is going to work better here. Winds are shifting for open models built in America. We saw GPT-OSS go through a bumpy launch to become an overwhelming success. There’s a collective energy around the likes of Reflection, Arcee, Nemotron, Gemma, Olmo, and peers that show substantial demand for building new stacks around open models. There’s capital to be spent on AI stacks across the economy by those who want more ownership of everything, including the model. After launching The ATOM Project 240 days ago, the conversation is shifting into the next stage. Summer of 2025 was a crisis moment where the U.S. AI scene realized it can’t wait and figure out open models after building AGI. The two markets will capture different areas and proceed in parallel. Now that more companies in the U.S. are releasing strong models, we need to improve the ecosystem so that these models are easy to use, understand, and build value around. It’s the hard work to build another inflection point in these adoption plots I’ve been updating consistently, but that’s the work to be done. Join me in it. More data coming soon! Here’s a sneak peek:
Cursor Launches a New AI Agent Experience to Take on Claude Code and Codex
As Cursor launches the next generation of its product, the AI coding startup has to compete with OpenAI and Anthropic more directly than ever.
Anthropic Says That Claude Contains Its Own Kind of Emotions
Researchers at the company found representations inside of Claude that perform functions similar to human feelings.
5+ Things to Know About the Siri Chatbot Coming in iOS 27
We're less than three months away from our first look at Apple's smarter, redesigned version of Siri. iOS 27, iPadOS 27, and macOS 27 will focus on Siri updates, and rumors about what we can expect are picking up. There's a full chatbot version of Siri in the works, which will change the way that we use Apple's personal assistant. Siri will be more like Claude or ChatGPT, marking a major improvement in how Siri works and what it can do.
Claude's popularity is forcing it to hit the brakes on users
"I know this was frustrating," an Anthropic official wrote in an announcement of the changes. "We're continuing to invest in scaling efficiently.
Gemini takes on ChatGPT and Claude with a major switching upgrade
Easily import your memories and preferences
Anthropic tweaks timed usage limits to discourage Claude demand during peak hours
: AI biz makes some Claude conversations more costly to manage capacity
Apple will reportedly allow other AI chatbots to plug into Siri
Other AI chatbots, like Gemini or Claude, could help Siri fetch answers.
Google Gemini Adds Tool to Make It Easier to Switch from ChatGPT
Google released new tools for its Gemini artificial intelligence assistant that will let users upload chat history and context from other AI apps, a move to lure users from rivals like OpenAI’s ChatGPT and Anthropic PBC’s Claude.
Apple Plans to Let Rival AI Chatbots Integrate With Siri in iOS 27
Apple plans to allow third-party AI chatbots to integrate with Siri in iOS 27, reports Bloomberg. Apple already has a partnership with OpenAI that lets Siri hand questions off to ChatGPT, but Apple will expand that integration to other companies like Google and Anthropic. An iPhone user with the Claude or Gemini app installed will be able to send questions to those chatbots, like how the current ChatGPT feature works.
iOS 27: Apple will reportedly let Claude and other AI chatbot apps integrate with Siri
Apple will unveil iOS 27 on June 8 at WWDC, and we’re learning new details about what to expect in...
Anthropic’s Claude can now control your Mac, escalating the fight to build AI agents that actually do work
Anthropic has given Claude the ability to control a Mac, marking a major step in the AI agent race and raising new questions about automation, reliability, and security.
iOS 27 Siri Overhaul: Standalone App, New Design, Dynamic Island Integration and Ask Siri Button
Apple is testing a standalone Siri chatbot app for iOS 27, reports Bloomberg's Mark Gurman. Apple plans to debut a chatbot version of Siri that will compete with Claude and ChatGPT, and the new Siri could be accessible through a dedicated app. Gurman previously said that Apple was not going to develop a standalone Siri app, but it appears plans have changed.
Claude Code can now take over your computer to complete tasks
But Anthropic urges caution as "research preview" safeguards "aren't absolute."
Anthropic’s Claude Code and Cowork can control your computer
Its only a research preview for now.
Anthropic says Claude can now use your computer to finish tasks for you in AI agent push
Anthropic and its rivals are trying to ramp up capabilities of AI agents after OpenClaw went viral earlier this year.
'Claude, Resize These Photos' - Anthropic's Agentic AI Will Run Photoshop For You
It can perform boring tasks.
Claude can now automate your entire desktop, but with a serious limitation
It's likely a temporary one, though.
Anthropic's Claude Can Now Control Your Computer
The developer is boosting Claude's agentic capabilities in an apparent effort to compete with platforms like OpenClaw.
This Web Tool Sabotages AI Chatbots By Making Them Really, Really Slow
Artist Sam Lavigne created ‘Slow LLM’ to make people question their dependence on tools like Claude and ChatGPT. Or at least, make them super annoying to use.
I let AI pick my Oscars ballot. It messed up oddly, but still won.
Alistair Barr's AI-assisted Oscars ballot, powered by Anthropic's Claude, outperformed despite missing some categories.
Anthropic just shipped an OpenClaw killer called Claude Code Channels, letting you message it over Telegram and Discord
The consensus among early adopters is that Anthropic has successfully internalized the most desirable features of the open-source movement—multi-channel support and long-term memory
Google Begins Testing Gemini Mac App to Match ChatGPT and Claude
Google is ramping up development of a dedicated Gemini AI app for Apple Inc.’s Mac computer lineup, looking to step up competition with OpenAI and Anthropic PBC.
Anthropic's Claude Can Now Create Interactive Visuals Directly in Conversations
Anthropic's Claude chatbot has been updated with support for inline visual content that will help it provide clearer answers. Claude can now create custom visuals like charts, graphs, and diagrams. Visual content will be used when it better conveys an answer than plain text, and visual aids can also include real-world data like weather and recipes as long as web search is enabled. The visuals that Claude creates are distinct from Artifacts, and use HTML and SVG rather than image generation.
Anthropic's Claude can now draw interactive charts and diagrams
The new feature lets Claude generate interactive charts, diagrams, and visualizations inline during a conversation.
Anthropic’s Claude AI can respond with charts, diagrams, and other visuals now
Claude can now help you visualize topics as you chat.
5 AI Tools to Run a 1-Person Business While You Sleep (While Millions of ChatGPT Users Flee to Claude)
The AI landscape just shifted — and one move sparked a mass migration entrepreneurs didn't see coming.
I can text Claude like a personal assistant now — this iPhone shortcut made it possible
Now I keep my most important chats in my text messages so I can access them faster
I ran 7 real-world prompts on Gemini 3 and Claude Sonnet 4.6 — the results surprised me
The latest default models go head-to-head with 7 practical use challenges
Anthropic's Claude found 22 vulnerabilities in Firefox over two weeks
In a recent security partnership with Mozilla, Anthropic found 22 separate vulnerabilities in Firefox — fourteen of them classified as "high-severity."
Exclusive | ‘Send Us More’: Anthropic’s Claude Sniffs Out Bevy of Bugs
New AI-powered tools are increasingly adept at spotting flaws. Hacking experts worry they will be good at exploiting them, too.
HHS starts phasing out Anthropic's Claude
In this edition of STAT Health Tech: An AI chatbot for surgical patients got 'breakthrough' status from FDA, HHS phases out Claude, and more.
Claude hits No. 1 on App Store as ChatGPT users defect to Anthropic
OpenAI secured a Pentagon deal, sparking backlash and shifting some users' loyalties to Anthropic's rival Claude chatbot.
Chatbots from ChatGPT and Claude offer health advice
Tech companies are pushing new health chatbots, but experts say you still need to talk to your doctor.
ChatGPT vs Claude: I put both default models through 7 real-world tests — one is the clear winner
ChatGPT vs. Claude go head-to-head for daily work and writing
The nuclear nightmare at the heart of the Trump-Anthropic fight
Should we trust Claude to stop a missile attack?
The Pentagon is demanding to use Claude AI as it pleases. Claude told me that's 'dangerous'
Defense Secretary Pete Hegseth has threatened Anthropic, the maker of Claude AI, that if it doesn't turn over full use of the powerful tool by Friday, the company will face severe repercussions.
Anthropic says it won't concede to military's terms for use of AI
Anthropic CEO Dario Amodei said on Thursday the company "cannot in good conscience accede" to the military's terms over the use of Claude.
Claude and Codex now available for Copilot Business & Pro users
Claude by Anthropic and OpenAI Codex are now available as coding agents for Copilot Business and Copilot Pro customers. Copilot Enterprise and Pro+ customers received access earlier this month, and…
How one AI company is helping businesses navigate Trump’s new tariff chaos following the Supreme Court ruling
Plus Anthropic accuses Chinese AI rivals of using Claude's answers to train their own models
Anthropic says DeepSeek fraudulently used Claude
"These campaigns are growing in intensity and sophistication," Anthropic said as part of its lengthy statement.
iOS 26.4 Brings CarPlay Support for ChatGPT, Claude and Gemini
With iOS 26.4, CarPlay users will be able to use third-party chatbots with CarPlay. AI services like Claude, Gemini, and ChatGPT will be accessible through the CarPlay system for the first time. Apple's CarPlay Developer Guide [PDF] lists voice-based conversational apps as a supported app type starting with iOS 26.4. Apple is implementing a new voice control screen that will let apps provide visual feedback for voice-based conversational apps.
Claude’s AI Market Structure Disruption Business Leaders Must Watch
Specialized AI service firms face challenges as big players move in. Business users must consider vendor lock-in and pricing when setting AI strategy.
Elon Musk slams Anthropic AI models as 'misanthropic and evil' in scathing social media post
Elon Musk accuses Anthropic's AI models of being "misanthropic and evil," claiming competitor's Claude models exhibit racial and demographic bias.
I tried a Claude Code rival that's local, open source, and completely free
I was curious if Block's Goose agent, paired with Ollama and the Qwen3-coder model, could really replace Claude Code. Here's how it worked.
The New Fabio Is Claude
The romance industry, always at the vanguard of technological change, is rapidly adapting to A.I. Not everyone is on board.
Trails Series Figures Of Estelle, Altina, And Agnès Revealed By QuesQ
Figure maker quesQ has announced new Trails series figures: Estelle Bright (Trails in the Sky), Altina Orion (Trails of Cold Steel III & IV), and Agnès Claudel (Trails beyond the Horizon). No pricing or pre-order info yet.
Anthropic’s breakout moment: how Claude won business and shook markets
Start-up’s bet on enterprise users is paying off as coding tools fuel revenue and investor frenzy
Apple to Allow ChatGPT, Claude, and Gemini in CarPlay
Apple is planning to bring new AI features to CarPlay, reports Bloomberg. Apple will allow third-party chatbot apps to integrate with CarPlay, so AI services like Claude, Gemini, and ChatGPT will be accessible in the car for the first time. CarPlay already supports third-party apps, but the types of apps that are supported are limited. Companies like Anthropic and OpenAI aren't currently able to create CarPlay apps, so users are limited to using Siri voice controls in the vehicle.
Anthropic and OpenAI release dueling AI models on the same day
The rivaling AI companies pushed GPT-5.3-Codex and Claude Opus 4.6, and had back-to-back appearances on a tech and business podcast.
Can AI ‘vibe research’ replace social science?
AI tool improvement is compounding fast enough for researchers to start using tools like Claude Code for real social science tasks.
GitHub adds Claude and Codex AI coding agents
GitHub continues to embrace rival AI agents
Anthropic says ‘Claude will remain ad-free,’ unlike ChatGPT
‘Ads are coming to AI. But not to Claude.’
Claude is a space to think
Anthropic explains why Claude will remain ad-free—how advertising incentives conflict with building a genuinely helpful AI assistant users can trust.
Xcode moves into agentic coding with deeper OpenAI and Anthropic integrations
Xcode 26.3 offers agentic coding capabilities with Anthropic's Claude Agent and OpenAI's Codex.
I tried a Claude Code alternative that's local, open source, and completely free
I was curious if Block's Goose agent, paired with Ollama and the Qwen3-coder model, could really replace Claude Code. Here's how I got started.
Anthropic extends MCP with a UI framework
Anthropic is turning Claude into an app platform, with interactive widgets from Slack, Figma, Asana, and others.
Anthropic embeds Slack, Figma and Asana inside Claude, turning AI chat into a workplace command center
Anthropic is turning Claude into an interactive enterprise workspace by embedding business apps like Slack, Asana, Figma, and Amplitude—powered by its open Model Context Protocol (MCP)—so teams can create projects, send messages, and analyze data without switching tabs.
Anthropic launches interactive Claude apps, including Slack and other workplace tools
Claude users will now be able to call up interactive apps inside the chatbot interface, with Cowork integration coming soon.
Claude Code is suddenly everywhere inside Microsoft
Microsoft is increasingly adopting Claude Code
Moxie Marlinspike has a privacy-conscious alternative to ChatGPT
Confer is designed to look and feel like ChatGPT or Claude, but your conversations can't be used for training or advertising.
Behind the Curtain: The AI future has arrived
Claude shows in vivid and unforgettable ways how AI will forever change work.
ChatGPT's AI Health-Care Push Has a Fatal Flaw
OpenAI and Anthropic have both announced big plans to enter healthcare, with a consumer-focused tool called ChatGPT Health and a version of the chatbot Claude that can help clinicians figure out a diagnosis and write medical notes. Notably absent from this flurry of announcements is Google. Its Gemini chatbot is one of the most popular and capable, so why not jump into the lucrative health market too? Perhaps because Google knows from experience that such an effort can backfire spectacularly.
How scientists are using Claude to accelerate research and discovery
Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.
Anthropic launches Cowork, a file-managing AI agent that could threaten dozens of startups
The new tool brings Claude Code's capabilities to non-technical users, positioning Anthropic to compete with productivity tools like Microsoft Copilot.
California Academy of Science to hold special event to honor Claude, beloved albino alligator
(KRON) — The California Academy of Sciences will hold a special celebration to honor the life and legacy of Claude, San Francisco’s beloved albino alligator. The free event, Claude Forever: A…
Anthropic launches Claude Cowork, a version of its coding AI for regular people
Anthropic is billing Cowork as "a simpler way for anyone — not just developers — to work with Claude."
Anthropic’s new Cowork tool offers Claude Code without the code
Built into the Claude Desktop app, Cowork lets users designate a specific folder where Claude can read or modify files, with further instructions given through the standard chat interface.
Anthropic wants you to use Claude to ‘Cowork’ in latest AI agent push
The new feature debuted today in research preview.
ChatGPT and Claude get into the business of health advice. Should you trust them?
AI chatbot are getting into the business of offering consumers health advice. What are the risks?
JPM26: Anthropic launches Claude for Healthcare to turbocharge AI efficiency at health systems, payers
SAN FRANCISCO—Anthropic is pushing into the healthcare market as it launches artificial intelligence tools and resources purpose-built for providers and payers. | Anthropic is pushing into the healthcare market as it launches artificial intelligence tools and resources purpose-built for providers and payers.
Anthropic Adds Features for Doctors, Patients in Health Care Push
Anthropic is making it easier for patients and clinicians to use its artificial intelligence chatbot to access medical information, part of a broader push into the lucrative health care sector. The San Francisco-based company on Sunday said that its Claude product is launching a new health care offering that is compliant with the Health Insurance Portability and Accountability Act, or HIPAA, and can be used by hospitals, medical providers and consumers to field protected health data. Anthropic h
Anthropic rolls out new healthcare and life science features for Claude
Anthropic is partnering with HealthEx to let patients use their electronic health records when asking Claude for medical or health advice.
Anthropic rolls out new healthcare and life science features for Claude
Anthropic is partnering with HealthEx to let patients use their electronic health records when asking Claude for medical or health advice.
Anthropic joins OpenAI's push into health care with new Claude tools
Anthropic’s new offerings allow users and providers to work with medical data, mimicking similar moves by OpenAI.
Advancing Claude in healthcare and the life sciences
Introducing Claude for Healthcare with HIPAA-ready infrastructure, plus expanded Life Sciences tools for clinical trials and regulatory submissions. New connectors to CMS, Medidata, and ClinicalTrials.gov.
Advancing Claude in healthcare and the life sciences
Introducing Claude for Healthcare with HIPAA-ready infrastructure, plus expanded Life Sciences tools for clinical trials and regulatory submissions. New connectors to CMS, Medidata, and ClinicalTrials.gov.
Bywater-Marigny business group pushes for a more bikeable, walkable St. Claude corridor
ByMBO's inaugural event, "Spark: Light up St. Claude," featured an illuminated jellyfish and bike light giveaways.
ChatGPT vs Gemini vs Claude vs Qwen: Which AI Chatbot Is Best Positioned for 2026
As 2025 comes to a close, AI chatbots are no longer judged on novelty or demos. Instead, investors now look at scale, daily use, work fit, and revenue reach. When