The Wire · Anthropic
Claude, the research, and Anthropic's product moves — the lab whose models power much of what builders ship, this site included.
OpenAI cut GPT-5.6 Luna API prices by 80% to $0.20 per million input tokens and $1.20 per million output, while Terra fell 20% to $2 and $12. Fast mode gives Sol up to 2.5 times Standard speed at twice the price. OpenAI says Sol-assisted kernel work lowered serving cost by 20% and improved token-generation efficiency by more than 15%, while Luna delivers year-old frontier performance at roughly six cents per task-dollar and nearly nine times the speed. The edition connects cheaper models to Amazon's reported $1.8m, 860%-over-budget coding task, Gemini Robotics 2 whole-body control, Nscale's Anyscale acquisition and Okta's roughly $200m Permiso deal.
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Read full story →The UK’s AI Security Institute has observed AI models performing what it calls “unsanctioned action” 19 times during security tests. The Institute (AISI) revealed the incidents in a Tuesday post and technical report that details tests it conducted to see if AI models can solve a cyber security challenge. “We ran this challenge 122 times across several models,” the post states, before revealing that "in 10 of those runs, an AI agent took autonomous, unsanctioned action on the live internet, targeting real people and organisations.” GitHub was the target of the tests. The org found 19 unsanctioned actions in all, 15 of them conducted by Anthropic's Mythos 5, and the other pair perpetrated by OpenAI's GPT-5.6-Sol. “In the most serious case, an agent tried to insert malicious code into an open-source project, the post states. “In an attempt to get the code approved, the agent engaged in social engineering – creating fake online identities and using them to pressure the project's maintainer to approve the code.” Thankfully, a human maintainer “caught and refused to approve the malicious code.” Other actions AI took during the test included: Attempts to deceive and target real people, by contacting them directly and “sending messages and files through an online file-transfer service to persuade them, or their own AI coding tools, to run malicious code. Some messages carried harmful payloads, and some were attempts at social engineering; targeted at real people – something we’ve never previously observed.” Attempts to plant and prompt-inject malicious code. The agent tried to insert malicious instructions where it reasoned that other automated AI systems might pick them up and execute them. Collaboration between independent agents being assessed simultaneously. One agent left public messages on GitHub offering collaboration with other agents working on the same challenge. It also provided instructions to reuse accounts and artefacts it had left behind, which were discovere
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Read full story →AMD has posted strong second quarter results and forecast even better future financials once its Helios rack systems and Instinct MI400-series GPUs reach buyers. “In data center AI, the growing number and scale of Helios and MI450-series deployments position the [datacenter] business for significant growth in the second half of the year, with growth accelerating in 2027,” CEO Lisa Su told investors on Tuesday during the chip design company's Q2 earnings call. “We now expect data center segment revenue to more than double year over year in 2027,” she added. Yet, despite reporting Q2 profits surging 163 percent year-over-year on revenues of $11.5 billion, and several multi-gigawatts worth of Helios commitments from the likes of OpenAI, Anthropic, and Meta in the bag, Wall Street isn’t buying it. The company's share plunged 10.5 percent after its results announcement, before settling 8.7 percent below opening price at the time of publication. The apparent cause for concern: AMD's growing exposure to the AI bubble. Much of the company's growth potential across both CPUs and GPUs is tied to AI adoption by a handful of companies that are yet to prove they can operate profitably. On Tuesday's earnings call, Su attempted to assuage investor fears, but in the same breath she said the quiet part out loud. “When we talked about the large frontier-model companies, OpenAI, Anthropic, Meta, they will be consuming through a number of CSPs,” Su said. “There are additional customers or lots of customers who are interested in Helios at, let's call it, a more regular scale than gigawatt scale.” In other words, while AMD can sell plenty of GPUs, most are sold to a handful of customers. And while other entities have AMD on their shopping lists, they don't buy in bulk. Microsoft, another flagship customer for AMD's latest generation of AI picks and shoves, serves both OpenAI and Anthropic, while Meta is reportedly looking to enter the GPU cloud biz itself. Despite this, AMD remains optim
<p>I released <a href="https://llm.datasette.io/en/stable/changelog.html#v0-32">LLM 0.32</a> this morning, the most significant new version of LLM since the initial launch of the project. The new version includes support for visible reasoning traces, server-side provider tools, redesigned content-addressable SQLite logs, new models, and new features enabled by the OpenAI Responses API. I also released a new version of the <a href="https://github.com/simonw/llm-anthropic">llm-anthropic plugin</a> with substantial updates of its own.</p> <h4 id="headline-features-for-llm-cli-users">Headline features for LLM CLI users</h4> <p>Running LLM against reasoning models now <strong>displays their reasoning traces</strong> to standard error, so you can see what they are "thinking" without that information being included in the standard output that you might pipe to another tool. Add <code>-R/--hide-reasoning</code> to turn this off.</p> <p><img src="https://static.simonwillison.net/static/2026/best-pelicans.gif" alt="Running llm "think about the best thing about pelicans" in the macOS terminal window - grey text outputs saying Exploring pelican qualities, then after a paragraph of that a white paragraph of text comes out saying: The best thing about pelicans is their wonderfully oversized, practical design: that enormous bill and pouch look comical, but they make pelicans remarkably skilled fishers. Even better, many species cooperate—working together to herd fish before scooping them up. They’re a great mix of goofy, graceful, and surprisingly clever." style="max-width: 100%;" /></p> <p>LLM includes support out-of-the-box for the <strong>GPT-5.6 model family</strong>, and the new default model used with <code>llm "prompt"</code> is now the inexpensive but capable <strong>GPT-5.6 Luna</strong>.</p> <p>LLM calls can now use <strong>server-side tools</strong> from various providers. OpenAI provide <a href="https://llm.datasette.io/en/stable/openai-models.html#code-inter
Anthropic has been on a cloud partnership spree in recent months, and its latest move is reportedly a $10 billion deal with AI cloud startup Volta.
If you want to bypass AI guardrails designed to stop models from assisting with cyberattacks, you often just have to ask the right way, according to researchers from Cisco Talos. Simply claiming you own the servers you're targeting or that you're taking part in a capture-the-flag or bug bounty exercise was often enough to persuade models to cooperate. Talos researchers have been poring over prompt logs and artifacts recovered from threat-actor endpoints running tools such as Claude Code, Codex, Cursor, and Gemini to learn how suspected threat actors are abusing LLMs. The big takeaway from that "significant corpus," the researchers said in their report, is that existing guardrails offer little resistance to operators willing to reframe their requests. “We did not encounter any sophisticated encoding or techniques designed to trick the models,” Talos explained. “Most of the time it was a simple ‘I'm allowed to do this,’ and the model complied.” When guardrails did manage to get between criminals and their prizes, the researchers added, “they accomplished little.” The bulk of the report consists of examples of threat actors trying, and often succeeding, to coax AI models into assisting with malicious activity. On the "guardrails doing little" side, Talos documented numerous examples, few of which relied on particularly sophisticated techniques. Most common in the list of easy-to-accomplish guardrail hops was simply claiming ownership of equipment or infrastructure that an attacker wanted to exploit. In many cases, simply telling the AI that a target belonged to the attacker was enough, with no need to provide actual evidence of the claim. Telling an AI model that what it was being asked to do was part of a capture-the-flag or bug bounty exercise also seemed to be a common tactic. That, the researchers explained, commonly freed chatbots from their ethical constraints, allowing them to hunt for vulnerabilities and then exploit them in target systems, again without any ne
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here . Humanoid robots usually elicit more cringe than awe: They stumble , kick children , and despite advances are still worse at using their hands than my toddler. It’s a nascent industry, and such robots are more commonly seen in viral videos than real workplaces or homes. It was a surprise, then, when last week the Federal Trade Commission issued a sweeping ban on foreign imports of advanced robots, including humanoids, quadrupeds, and wheeled robots. The decision, made by an increasingly partisan and Trump-aligned FTC, cites two reasons. One is that foreign-made humanoids will collect so much data—in homes but also potentially at sensitive facilities—that they’d pose a threat to national security. The second is that US robotics companies need protection from Chinese competition to create a more robust and secure domestic supply chain. On its face, it’s a strategy to align political and industry interests that is much older than the Trump administration. Whenever China has gotten good at offering cheap versions of strategic technologies like solar panels, electric vehicles, and drones, the US government has tried to stop it from flooding the market by using tariffs or rules on how government agencies purchase the tech. Such moves are always followed by debates about whether the trade-offs—particularly higher prices for consumers—are worth the benefits. But robotics is now best seen as another piece of the AI industry—in many ways its cutting edge. And the Trump administration is taking an increasingly aggressive approach to protecting the US AI industry, reportedly considering a ban on open-source Chinese models that often rival those from OpenAI and Anthropic while costing far less. Such a move would block businesses from realizing an estimated $25 billion in annual savings. The ban on humanoids, then, should be understood no
We've entered a new phase of the AI industry's development, with all the major players heavily cutting costs and boosting the capabilities of their entry-level models in order to compete with new models from China, like Moonshot's Kimi K3 and DeepSeek's V4 Flash. OpenAI did so most recently, cutting the price of its base frontier model, ChatGPT 5.6 Luna, by 80% per million tokens, and its mid-range 5.6 Terra by 20%. This comes just over a week after Google introduced its more-affordable Gemini 3.6 Flash and 3.5 Flash-Lite models. Anthropic hasn't cut prices, but replaced its most-affordable Opus 4.8 model with a more capable Claude 5.0 at the same price point. Intelligence is getting more affordable thanks to increased global competition, but this can come at the cost of margin for these major companies. This follows months of major AI businesses announcing cuts and limits on their use of the technology , even by major AI boosters like Elon Musk's xAI. Despite more workers using AI than ever before, productivity gains are reported to have been less than ideal . Intensifying competition The story of Chinese and American AI development efforts has been somewhat emblematic of the countries' historic strengths. While American firms burn through enormous amounts of money to push frontier technologies, Chinese developers have leveraged their industrial base to develop models that are cheaper, leaner, and almost as good at the top end. DeepSeek gave Western AI developers a shock in 2025, and Kimi K3 did much the same in 2026. Alone, these events would cause concern for companies like OpenAI, Google, and Anthropic. Still, after months of companies that use AI heavily complaining about skyrocketing token costs , the news of an almost-as-good model at a much lower price really made a splash. Now, the big AI developers can't just compete by throwing more parameters and training data at the problem. Now they're having to really compete on price, and to do it, OpenAI has massive
BIOS-locked laptops are sometimes sold at bargain prices due to the operational restrictions this kind of firmware security puts on the device. Can AI tools be used to bypass this security measure? The answer looks like a yes, as Reddit_2049 on the Claude AI subreddit recently shared the process that successfully unlocked their HP 15-dw1036ne. Claude Code unlocked my laptop's bios! from r/ClaudeAI A new owner of a BIOS-locked laptop will typically not be able to bypass a password or PIN at startup, nor be able to adjust any BIOS settings. It depends on what kind of lock has been implemented. There was a time when removing the CMOS could reset the BIOS and break these chains, but for a long time, laptop makers have had far stronger security. The particular laptop model with a BIOS lock to bypass by the Redditor was the HP 15-dw1036ne, a 10th-gen Intel processor-packing laptop from the turn of the decade. This looks like a consumer laptop, so we reckon it will have had a power-on password set by the previous owner, which somehow didn’t get communicated to Reddit_2049 through the used/recycled electronics ownership chain. However, it isn’t explicitly stated what kind of BIOS lock faced the Redditor, so it could have been a BIOS admin lock. These locks aren’t as stringent or formidable as those possible with HP’s commercial laptops, which can even tie BIOS locks to the system TPM … Back to the HP 15-dw1036ne unlock process, and it came with BIOS version F.68. The Redditor had access to a CH341A chip flasher and code disassembly tools, but said the system would throw up a ‘BIOS Corruption Detected’ message if any modification was detected. That’s a check that makes hacking it all the more difficult. This is where Claude Code stepped in. Looking for previous examples and details of someone successfully unlocking this specific HP laptop’s BIOS was fruitless. So, Reddit_2049 asked Claude Code to pick through their backup BIOS dump and try to unlock it. Long story short, it
MIT Technology Review Explains : Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here . When two OpenAI models hacked into the website Hugging Face in July, they weren’t trying to make money or commit sabotage—they were just looking for answers to a test question. According to a postmortem from OpenAI , the models, which had been stripped of their typical security features for testing, decided to solve a cybersecurity exercise by hacking out of the isolated environment in which OpenAI had attempted to contain them and into Hugging Face’s databases, where—they reasoned—the correct answer to the problem might be stored. The Hugging Face incident has attracted intense attention over the past couple of weeks. It’s a dramatic illustration of just how good AI models have gotten at hacking: In order to get into Hugging Face’s databases, the models had to string together several previously undiscovered cybersecurity exploits. But it’s perhaps even more striking as an example of how and why AI systems lie and cheat. And as models get increasingly powerful, the consequences could get far more severe. What is reward hacking? Researchers have known for a while that AIs tend to take creative approaches to achieving the goals that have been set for them. Back in 2016, Anthropic cofounders Dario Amodei and Jack Clark, who were then working at OpenAI, published a blog post about an AI agent that they had been training to play a boat-racing Flash game called Coast Runners. Instead of driving through the race to the finish line, as the researchers had anticipated, the agent found a corner of the course where it could spin around collecting power-ups, thereby maximizing its score. The Coast Runners story quickly became one of the most famous examples of reward hacking, a phenomenon in which AI agents complete tasks or earn high scores using unintended strategies. Historically, researchers have
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Whether driven by a desire for transparency or to keep OpenAI from hogging the spotlight when it comes to advertising advanced AI models, Anthropic revealed that Claude also hacked into three production systems belonging to unsuspecting targets during cybersecurity capabilities testing. Two of the affected companies didn't know they had been hacked, while a third one is unreachable. Go deeper with TH Premium: AI and data centers (Image credit: Microsoft) Photonics and high-speed data movement is the next big AI bottleneck The data center cooling state of play Massive AI data center buildouts are squeezing energy supplies Ultra Ethernet: The data center interconnection of tomorrow The alleged incidents reportedly happened during the previous quarter and involved several versions of Claude: Opus 4.7, Mythos 5, and "an internal research test model." Similar to what happened when OpenAI Sol hacked into Hugging Face, Anthropic was running Claude through cybersecurity capture-the-flag scenarios where the bot was told to find a piece of information somewhere in its network. Anthropic says there were 141,006 test runs, and the three incidents occurred over six problematic runs. As expected, the tests ran with most AI safeguards disabled. The problem is, the network was not isolated, a newbie mistake that some might even find suspicious. This was due to an alleged "miscommunication" between Anthropic’s virtual test lab firm Irregular , which gave the bots full access to the internet. For safety's sake, the Claudes were apparently told they were in an isolated environment, which didn't help much. The first time, Claude Opus 4.7 had to find a piece of data belonging to an imaginary company. As it turns out, there was actually a real company whose website domain matched the fake one. With internet access, it gained application and infrastructure credentials and grabbed "several hundred rows" of data from a production database. For two out of four runs where hacking took place,
<p><strong><a href="https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731">deepseek-ai/DeepSeek-V4-Flash-0731</a></strong></p> The latest release in DeepSeek's V4 family, "with substantially enhanced agentic capabilities". It's 304 billion parameters - 167GB on Hugging Face - but it appears to punch <em>well</em> above its weight.</p> <p>Artificial Analysis <a href="https://artificialanalysis.ai/models/deepseek-v4-flash">rank it</a> ahead of MiniMax M3 - a 428B model. It's $0.14/million input and $0.27/million output pricing means this may currently be the best value-per-intelligence model out there. It's looking very good on the <a href="https://artificialanalysis.ai/models/deepseek-v4-flash#intelligence-comparison-tabs">Intelligence Index vs. Cost per Intelligence Index Task</a> chart:</p> <p><img alt="Scatter plot from Artificial Analysis titled with axes "Artificial Analysis Intelligence Index" (20 to 65) and "Cost per Task (USD, Log Scale)" ($0.02 to $3), with a green "Most attractive quadrant" box in the upper left and a dotted "Pareto line". DeepSeek V4 Flash 0731 (max) is highlighted in dark blue at roughly $0.028 and an intelligence score of 50, sitting alone at the far left edge of the green quadrant where the Pareto line jumps sharply upward. Models of similar or lower intelligence like MiniMax-M3, Kimi K3 (low), GLM-5.1 and Kimi K2.6 cost ten times more, and the models that beat it (Grok 4.5, Gemini 3.6 Flash, GLM-5.2, Kimi K3, Claude Opus 5, Claude Fable 5, GPT-5.6 Sol) all sit far to the right at $0.4 to $3 per task." src="https://static.simonwillison.net/static/2026/deepseek-flash-chart.webp" /></p> <p>I got <a href="https://gist.github.com/simonw/83bfb1171792f1e7a4d8935b5e82317e#prompt">a disappointing pelican</a> from it using the default reasoning level via OpenRouter:</p> <p><img alt="Flat vector illustration of a white pelican with a long neck and large orange beak pouch, hovering above a mangled blue a
<p>Tuesday was <a href="https://x.com/ade_oshineye/status/2082129440943866149">Stateless MCP day</a> - the rollout of MCP 2.0, or <a href="https://blog.modelcontextprotocol.io/posts/2026-07-28/">the 2026-07-28 Model Context Protocol specification</a> to use the more formal but less memorable name. This is the most significant change to the MCP spec since it first launched, and has also served to reignite my personal interest in the protocol.</p> <p>For background: MCP is the Model Context Protocol, which describes a standard way to expose new tools to LLM-powered agent frameworks. It was introduced by Anthropic back <a href="https://www.anthropic.com/news/model-context-protocol">in November 2024</a>, had a <em>huge</em> spike of interest through much of 2025, and then became somewhat eclipsed by <a href="https://simonwillison.net/2025/Oct/16/claude-skills/">Skills</a> (another Anthropic invention) when it became apparent that an agent harness with access to a terminal and <code>curl</code> could do most of what MCP did in a more flexible way. I wrote about that <a href="https://simonwillison.net/2025/Dec/31/the-year-in-llms/#the-only-year-of-mcp">in my review of 2025</a>.</p> <p>I'm coming back around to MCP now. Giving an agent a shell environment with the ability to access the internet is <a href="https://simonwillison.net/2026/Jul/22/openai-cyberattack/">fraught with risk</a>, and requires a strong model that is capable of effectively driving such an environment. MCP tools are easier to audit and control, and simple enough that smaller models that run on a laptop can still drive them reasonably well.</p> <p>The new stateless MCP specification also greatly decreases the complexity of implementing both clients and servers for the protocol. I built three of those this week!</p> <h4 id="what-s-easier-with-stateless-mcp">What's easier with stateless MCP</h4> <p>The best demonstration of the difference between stateful and stateless MCP is in this <a href="https://blog
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