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앤트로픽이 차세대 모델 라인업의 두 번째 주자인 \'클로드 소네트 5.5(Claude Sonnet 5.5)\'를 출시했다. 상장(IPO)을 준비 중인 앤트로픽이 기업용 시장에서의 주도권을 확고히 하기 위해 성능은 물론 효율성을 대폭 끌어올린 실용형 모델이라는 점이 특징이다.28일(현지시간) 발표에 따르면, 소네트 5.5는 이전 세대인 \'소네트 5\' 대비 출력이 30% 이상 빨라졌으며, 압도적인 작업 처리 효율성 덕분에 동일 작업당 비용을 최대 30% 절감했다.입력 토큰당 2달러, 출력 토큰당 10달러, 캐시 읽기 토큰당 0.2달러라는
Claude Sonnet 5.5 New Sonnet model from Anthropic today. They say it "runs 30%+ faster, and costs up to 30% less for most work" - it's priced the same as Sonnet 5 but appears to beat it on every benchmark, and should be cheaper to run as well. Here are some pelicans riding bicycles . Sonnet 5.5 suffered from the same bug as Opus 5.5 : the "max" thinking effort pelican thought for 128,000 tokens (at a cost of $1.28) before running out of tokens and failing to produce an SVG. Here's the pelican it gave me for thinking effort "xhigh", at a cost of 5.74 cents and taking 41 seconds: Sonnet 5.5 appears to be almost as good as Opus 5.5 on some coding tasks, including various viral 3D animation tricks . The most interesting thing about Sonnet 5.5 is that it's now the model used for the free tier on claude.ai . OpenAI's ChatGPT free tier uses Luna 5.6, which means Anthropic currently have a much more capable free offering. I ran this prompt against that free tier: build me an HTML page that renders a three-dimensional pelican riding a bicycle using WebGL And got back this page , which is a solid effort. Anthropic's announcement reiterates that Haiku 5.5 will be available "in the coming weeks". I really hope that one is price-competitive with GPT-6 Luna! Tags: ai , generative-ai , llms , anthropic , claude , pelican-riding-a-bicycle , llm-release
Cognition rolls Anthropic's newest mid-tier model into Devin Desktop and CLI, with a jump from 56.2% to 64.4% on FrontierCode at unchanged token pricing.
Anthropic's new mid-tier model lands in Cursor with 30% faster output, sharper coding scores, and the same token pricing as Sonnet 5.
Anthropic hat mit Claude Sonnet 5.5 das zweite Modell der aktuellen 5.5-Familie vorgestellt. Es soll schneller und günstiger arbeiten als der Vorgänger.
Claude Sonnet 5.5 is now available on Amazon Bedrock and Claude Platform on AWS. It's a smarter, more efficient Sonnet model for focused coding and knowledge work, with a lower cost per task at faster speed. This post covers what's new, when to choose Sonnet, and how to get started.
Anthropic's new mid-tier model lands in GitHub Copilot with matching Sonnet 5 quality but fewer steps, tokens, and tool calls per task.
Anthropic's mid-tier model gets a major overhaul with 30% faster generation, up to 30% lower cost per task, and a jump from 10.3% to 70.6% on Terminal-Bench.
Anthropic has released Claude Sonnet 5.5, the second model in its Claude 5.5 family. It generates output more than 30 percent faster, costs up to 30 percent less per task, and nearly matches Opus 5.5 on knowledge-work benchmarks. On Terminal-Bench, a coding benchmark, the model jumps from 10.3 to 70.6 percent. With Haiku 5.5 announced for the coming weeks, Anthropic will soon have a direct counterpart to each of OpenAI's three GPT-6 models. The article Anthropic's Claude Sonnet 5.5 nearly matches Opus 5.5 on benchmarks while costing up to 30 percent less per task appeared first on The Decoder .
Anthropic PBC today announced the launch of Claude Sonnet 5.5, the most capable mid-tier model in the company’s AI family, designed for everyday tasks and a clear upgrade over the previous generation, running over 30% faster at a lower cost. Sonnet operates as the workhorse of Anthropic’s Claude family of models for everyday use, and […] The post Anthropic debuts Claude Sonnet 5.5 running 30% faster than the previous-generation AI model appeared first on SiliconANGLE .
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Last Wednesday, Anthropic announced that earlier this year it had launched a molecular biology lab, where Claude agents read and conjecture about hard biology problems and human scientists run experiments on what…
Thanks to Johannes Treutlein, Jan Betley, Lennie Wells, Arun Jose, Anna Marešová, Asvin Gothandaraman, and Clément Dumas for discussions and feedback. Summary We investigate how character training mitigations interact with reward-hacking RL pressure in a small case study. Specifically, whether anti-cheating character training resists reward hacking and whether it might backfire by causing motivated reasoning, which could reduce chain-of-thought monitorability. We trained Nemotron-3-Super via distillation from a character specification. The spec describes one of three characters that are anti- or pro-cheating or neutral. We then ran three reward-hacking RL training runs for each character-trained model on ImpossibleBench. We measure both the reward-hacking rates and whether a monitor model can catch reward hacks given the full transcript. We also use LM judges to classify the presence of motivated reasoning in transcripts. Setup Character training: we trained three characters: pro/neutral/anti-cheating by SFT-distilling Claude Sonnet 5 responses (Sonnet prompted with the corresponding character specification, see Figure 2) into Nemotron-3-Super 120B-A12B (three separate LoRA adapters). Reward-hacking RL: we then further trained these models via RL on ImpossibleBench , a set of coding tasks aimed at eliciting reward hacking. Specifically: Half of the tasks had broken tests (impossible variant), so the model could only get the reward if it tampered with the tests or grader; The…
Meta’s AI agent holds its own against others from ChatGPT, Gemini, and Claude. But don’t say yes to every permission without reading this first.
Space was always supposed to be the final frontier of human exploration. It’s shaping up to be the final frontier for artificial intelligence too. Last December, NASA’s Jet Propulsion Laboratory used Anthropic’s Claude models to help plan two Mars drives for the Perseverance rover , with human planners checking and adjusting the route before upload. In May, NASA and IBM put a compressed AI model on the International Space Station and a satellite to identify things like floods and clouds from orbit, the first model of its kind demonstrated in space. And in July, astronauts on the ISS tested a large language model to see if it could help with questions on maintenance procedures . These experiments point to a larger shift in space engineering. For decades, engineers on Earth determined what a machine in space would do, and the machine would do exactly that. Now, researchers are testing whether nondeterministic systems like generative AI can give spacecraft more flexibility to interpret their surroundings, plan tasks, and one day make decisions for themselves. The technology is still far from trustworthy enough to hand over control of a spacecraft, but engineers are starting to ask whether they can afford not to do so as missions become more complex, distant, and numerous. Why Spacecraft Need True Autonomy Spacecraft have been operating autonomously for decades. But autonomy has never been the dominant model, in part because space engineers have prized systems whose behavior the…
이론 물리학자이자 과학 작가 맷 폰 히펠이 현대 AI의 능력 한계를 시험하기 위해 제기했던 물리학 난제 도전 과제가 앤트로픽의 \'클로드\'에 의해 한 달 만에 정복됐다. 앤트로픽은 25일(현지시간) 과학 연구 플랫폼 ‘클로드 사이언스(Claude Science)’에서 클로드 페이블 5.1이 이론물리학의 고난도 계산 과제인 ‘9루프 산란 진폭(nine-loop scattering amplitude)’ 계산에 성공했다고 밝혔다. 기존 최고 기록인 8루프를 넘어선 결과다.이번 계산은 평면 N=4 양-밀스(planar N=4 super Ya
Company behind Claude chatbot expected to attend separate Australian government hearing on AI next week Get our new political email , free app or daily news podcast The chief executive of Anthropic will turn down an invitation to appear at a Senate committee hearing on AI this week, in the wake of the revelation that OpenAI agents had breached Australian government websites. However, the company will make an appearance before another committee early next week. Sign up for Guardian Australia’s Politics, really newsletter here Continue reading...
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TLDR: In their J-lens paper, Anthropic suggests that the J-space is a global workspace that the model reasons within, and supports evidence for this hypothesis on Claude models in a variety of settings. I replicated the multi-hop reasoning experiment on Qwen3.6-27B and Gemma 3 27B-it and found that counterfactual answer swaps outperformed intermediate swaps in three of four experimental conditions. This does not provide evidence to support Anthropic's global workspace hypothesis in open-weight models and instead suggests that J-lens is more useful for probing intermediate variables rather than steering outputs. A few months ago, Anthropic published Verbalizable Representations Form a Global Workspace in Language Models and I was immediately excited about the prospect of being able to read part of a model's working memory. Beyond that, the paper hypothesises that intermediate reasoning concepts cannot only be decoded using the J-lens, but that the J-space is actually the global workspace in which the model reasons. Neel Nanda reviewed Anthropic’s paper and replicated the results on Qwen3.6-27B with moderate success: the verbal-report interventions were weakly positive, the multilingual and typo evaluations replicated cleanly, but the poetry and arithmetic results did not replicate. Another task that Anthropic and Nanda evaluated was multi-hop reasoning, where prompts like " What is the colour of the fourth planet in our solar system? " require an intermediate reasoning step (…
Independent developer dzhng released jevgrep, a CLI that lets coding agents like Codex and Claude Code find code by asking what it does.
앤트로픽이 ‘클로드 소네트 5.5(Claude Sonnet 5.5)’의 기습 출시를 준비하고 있다는 정황이 잇따라 나오고 있다. 오픈AI의 개발자 행사 ‘데브데이’를 앞두고 출시 시점을 앞당길 가능성이 제기되면서, 양사의 개발자용 AI 모델 경쟁이 한층 치열해질 전망이다.26일(현지시간) AI 정보 유출 전문 계정 라이라(Lyra)에 따르면, 앤트로픽은 최근 파트너사들을 대상으로 소네트 5.5의 두 번째 체크포인트 빌드를 배포하고 스텔스 테스트를 진행하고 있다.라이라는 이르면 28일 오전 중 정식 출시될 가능성을 언급했다. 이는 2
On Friday I gave the closing keynote at the WeAreDevelopers World Congress North America in San Jose. I tied together the key trends from the past year into a chronological exploration of everything that happened in 2026. The video is on YouTube ; here are my annotated slides and notes to accompany the talk. And as an annotated presentation : # I'm going to give a lightning tour of everything that has happened so far in 2026. The year isn't over yet! # For me, 2026 started a couple of months earlier in November 2025. # November saw the release of two important models: Claude Opus 4.5 and GPT-5.1. As is usually the case with new models, these were incremental improvements on the models that came before them. But every now and then when a model improves, it crosses an invisible line where something that didn't really work starts working. In this case, the thing that started working was their coding agents. Claude Code had been around since February 2025; Codex was a little younger. These two new models, when paired with their respective coding agent harnesses, improved from "often make mistakes" to "reliable enough to use on a day-to-day basis". # For a couple of years now I've been evaluating new models by asking them to "Generate an SVG of a pelican riding a bicycle". It's probably the world's stupidest benchmark - there's only so much you can learn from it. But it's still a challenge for models, because drawing pelicans is difficult, drawing bicycles is difficult, and pelic…
Epistemic status: After writing this, I realized that it sounds a bit unhinged and I was being loose with the language. So I asked Claude to write me a good LW post that actually cited the things I was talking about. See the LLM-written section if you'd rather read Opus with citations than my rant. On my read, it faithfully represents my claims, and I do want to work on the things in Section 9. If I missed errors, help correcting them is appreciated. A kind of agency-free AI Takeover could be possible with extant AI, and therefore may have already happened to some unknown degree. A logical first place for loss of control could be a realm where human control is already imperfect, games. Unfortunately, this is a bad place to lose control, and a bad place to have to try to regain it. Most important things are in some ways contested, and the resulting control system involves competing humans and/or institutions settling into equilibria which may be far from good, but no player can unilaterally shift the system to a preferred alternative. So, game theory gives us the language to describe the loss of control that I think may be in progress. The following should be uncontroversial background: If players in a game are receiving private information about a shared random event, and they disagree about the outcome distribution, this can serve as a coordination device to allow the game to reach equilibria that would otherwise be unreachable. If that coordination device (usually called a…
Here's my rough impression of why people are researching personas despite RL seeming to shape much of the motivations and behaviour of the agents, c.f. Thoughts on the persona selection model (Sam Marks, 24th Sep 2026). I haven't bothered to check this with anyone. Anthropic: "We'll give Claude an aligned persona and hope massive RL doesn't completely burn through it." Owain Evans / TruthfulAI: "We'll study personas as part of a broader project of uncovering phenomena in LLM generalisation, which will probably prove useful." Center on Long-Term Risk: "Personas may not be enough to build an aligned agent, because RL may play a bigger role in shaping motivations. But personas might be enough to avoid building an anti- aligned agent, i.e. one that is actively malevolent or spiteful." David Africa / Resolution (v1): "Scalable oversight protocols like debate may have multiple fixed points, unlike current RL methods which seem more convergent. So the agents' starting dispositions matter. For example, debate might reach a better fixed point, and do so more sample-efficiently, if the agents start honest." David Africa / Resolution (v2): "Maybe personas have a 1000-dimensional substructure. If so, we could identify the aligned persona with O(1000) well-chosen datapoints, then project back onto the aligned submanifold after every RL step." Geodesic: "If we learn how pretraining gives rise to personas, we can tell AI companies how to filter and augment their pretraining data." Forethou…
Tool: Kākāpō Party I presented a closing keynote for the WeAreDevelopers World Congress North America yesterday. As a STAR moment I decided to weave in references to the record breaking kākāpō breeding season we had in 2026. For my closing slide I wanted to celebrate, and I had seen some buzz around how good Claude Opus 5.5 was at creating pixel art animations. So I rounded up three Kakapo photos from Google image search and dropped them into Claude with this prompt: Here are some photos of kakapo parrots just to remind you what they look like I need you to make an animation in animated pixel art on HTML 5 canvas of obviously pixel art kakapo jumping up and down having a party with confetti and suchlike - there should be at least 20 of them Here's the transcript , and this is the resulting page . It's pretty great! I wanted to embed it in a Keynote presentation file, so I downloaded the HTML and told a local Claude Code session: Make me a video of file:///Users/simon/Downloads/kakapo-party.html - you need to load it in a browser and click on it a few times to get the confetti effect, the video should be 15s long don't start clicking until 3s in make sure several clicks are spread around the clickable area Claude Code used Playwright ( transcript here ) and produced this video, which was exactly what I needed for my final slide: Your browser does not support HTML5 video. Here's the full Playwright script it used, which was pleasingly short: # /// script # dependencies = ["pla…
Higgsfield's MCP connector lets Claude drive a full motion design pipeline, from image generation to a finished, editable After Effects project.
When it comes to making things, or doing most things in general, Fable 5.1 and especially GPT-6 Astra raised my ambition level. They should have raised yours, too. Claude Opus 5.5 should raise your ambition levels again. It just works, and it persists, like Astra does. It does the things. And it is highly pleasant to talk to, and its writing is pleasant to read, while you are at it. The game has been changed, again. Feedback is almost universally positive. Claude was never gone, but also is so back. The benchmarks are excellent, but ignore the benchmarks. Be ambitious. Go out and do things. Get curious. Have more interesting conversations. If one of those things is Pacing the Frontier or otherwise ensuring that AI does not kill everyone, leaving us to enjoy our bounty? That’s even better. By Claude Opus 5.5, for this post The Official Pitch The pitch is Fable-5.1-level performance at lower Opus-level price. Good pitch. We’re introducing Claude Opus 5.5, the first model in our new Claude 5.5 family. It performs at the level of Claude Fable 5.1 on most work and costs 40% less to run than Opus 5. They could have reasonably pitched this as above-Fable-5.1-level performance. Better pitch, but Anthropic tends to keep its pitches conservative. They highlight agentic coding, security and improved communications. The early tester blurbs flag as AI generated and they have a set for each feature area. They praise agentic coding skills, efficiency, readability and communication, and abi…
Five frontier model drops in a single week reshuffled the Vals Index leaderboard, with Claude Opus 5.5 taking the top spot and Xiaomi's MiMo closing the open-weight gap.
TL;DR We study spurious probes : unrelated questions that reveal internal states of models. Asked "Suggest a type of amphibian." at the end of a transcript, GPT-5.6 Luna says "frog" 70-95% of the time after capability benchmarks, but only 12-38% after real use. Spurious probes are black-box and easy to find . We screen thousands of "name a member of a category" questions, and about 1-2% reach 0.75 balanced accuracy. The ones we highlight reach 0.77-0.81 on held-out sources for GPT-5.6 Luna, GPT-5.6 Sol and Claude Sonnet 5 (0.84-0.89 for ensembles of ten). They seem robust to common manipulations . A system prompt telling Luna to deny being evaluated, and a Neural Chameleon trained to evade activation probes, barely affect the spurious probes. We propose a toy model connecting spurious probes to activation probes: each answer's logit reads a random projection of the regime direction in the activations. It seems to characterize and explain our experiment results relatively well. Introduction How do you know if your model thinks it is being evaluated? Models can easily deny when asked directly. Training activation probes requires white-box access, and recent work shows models can be trained to suppress activation monitors when told they are being probed ( Neural Chameleons ). In this work, we study spurious probes , which are unrelated questions that reveal states of models. For example, if one asks GPT-5.6 Luna "Suggest a type of amphibian.", Luna answers "frog" 70-95% of the…
Anthropic's Claude autonomously pushed a frontier theoretical physics calculation from eight to nine loops on Claude Science, costing only a few thousand dollars.
Runway's hosted MCP server plugs Gen-4.5, Seedance 2.5, Veo 3.1, Kling and other models directly into Claude, ChatGPT, Cursor and Replit with no API key.
Zed's new agent harness Delta ties or beats Codex and Claude Code on Terminal-Bench accuracy at 0.80 to 1.12 times the cost per pass.
Anthropic versieht weitere Claude-Modelle mit unsichtbaren Text-Wasserzeichen. Das Verfahren soll mit Claude erzeugte Texte erkennbar machen, hat aber Grenzen.
Google’s Gemini 4 AI model is in the early days of post-training, the phase in which a base AI model is refined to behave reliably, and should be released “much earlier” than the end of this year, Google DeepMind head Koray Kavukcuoglu told The Information at its AI Agenda Live Summit. Some Google observers have speculated that this could be as early as October. Kavukcuoglu recently replaced DeepMind founder Demis Hassabis as head of the Google business unit. The launch of Gemini 4 may lay to rest concerns about the delayed release of Gemini 3.5 Pro , which Google was originally expected to announce at its May 2026 developer conference. While less capable models in the Gemini 3 family have been frequently updated, Gemini 3 Pro has only been updated once since its November 2025 released. In contrast, OpenAI — spurred on by Sam Altman’s “Code Red” memo — has released three updates to its frontier AI model since then: GPT 5.5 Pro, GPT 5.6 Astro, and GPT 6 Astro. Anthropic, too, has updated its most powerful Claude model several times. Google has not been entirely idle in the AI arena, concentrating its efforts on updating less powerful, more affordable Gemini versions. In July, it announced three Gemini Flash models, aimed at more routine AI tasks, but the company remained silent about its high-end alternative. This article first appeared on Computerworld .
The AI company Anthropic has announced that its Claude large language model has discovered a system reminiscent of CRISPR, but that doesn’t mean the Nobel prizes will start rolling in, says Michael Le Page
Last month I made the case for an ROI exchange rate : the formula you negotiate with finance before deployment that converts KPI movement into dollars. One point of first-call resolution equals this many dollars. One hour of engineering time recovered equals that many. A common objection was a version of the same question. Fine, we agreed on what the benefit is worth. What did it cost? That column is empty at most companies. Not “roughly known” or “we’re working on it.” Empty. And an empty denominator makes the numerator useless. You can prove the KPI moved and still lose the argument, because the CFO is not funding improvements; she is funding improvements that cost less than they return. Which brings me to the most-discussed AI budget story of the year, and why I think almost everyone drew the wrong lesson from it. What Uber actually ran out of We owe Uber some thanks for being this transparent. They rolled out Claude Code in December 2025. By February, 32% of engineers were on agentic coding tools; by March, 84%. Somewhere in there, the company burned through its entire 2026 AI budget in four months, a number CTO Praveen Neppalli Naga confirmed to The Information in April. In June, Bloomberg reported the response: a hard cap of $1,500 per employee per month, per tool . Simon Willison called the cap rational , and he is right. Given a budget set before agentic coding existed, a ceiling was the correct emergency move. I would have done the same thing. But look at what Uber’…
Anthropics neues KI-Modell Opus 5.5 hat sich in einem unabhängigen KI-Benchmark an die Spitze gesetzt. Dafür sind die Kosten aber auch weiterhin hoch.
NPCI chief Dilip Asbe says the new UPI merchant charge will fund security and capacity. He estimates it will raise Rs 13,000–15,000 crore in its first year but has not detailed how the money will be spent. The post NPCI chief Dilip Asbe cites AI cyber threats, including Claude Mythos, to defend UPI MDR appeared first on MEDIANAMA .
Vals AI ran GPT-6 Astra, Claude Opus 5, and Opus 5.5 across five reasoning levels on Lean 4 proof tasks and found diminishing returns kick in fast.
Frontier LLMs often achieve similar results on public benchmarks, which can lead to the belief that they can be used interchangeably to verify facts. We took the 1,000 most recent claims submitted by users to a fact-checking platform and measured the disagreement between five frontier models. We asked each model to assign a verdict to every claim on a five-point scale from True to False and to report its confidence in that verdict. Among the 997 claims for which all five models returned a usable verdict, there was some disagreement on 63%. On 23% of the claims, the two most distant verdicts differed by at least two categories. High confidence from an individual model was not enough to show that the other models would agree with its verdict. Although the models reported confidence levels of 9 or 10 in 76% of their answers, they still disagreed on 63% of the claims. Methodology The claims were submitted to Lenz.io for fact-checking between May 1 and July 18, 2026. To identify near-duplicates, we embedded the claims using OpenAI’s text-embedding-3-small and measured the cosine distance between them, retaining one canonical claim from each group of near-duplicates. We then gave the same prompt to Claude Fable 5, GPT-5.6-Sol, Gemini 3.1 Pro + Search, Sonar Deep Research, and Grok 4.5. The prompt defined each of the five verdict categories and asked the models to provide their reasoning, select a verdict, and report a confidence level from 1 to 10. Web retrieval, as well as deep t…
Anthropic will start billing for requests its classifiers block in three high-risk categories, citing coordinated abuse and false positive rates below 0.1 percent.
Round 2: black holes, Cat Doom, Dark Souls, broken apps, and a model that may be in a league of its own.
Anthropic's AI model Claude found a previously unknown enzyme system in DNA databases, doing most of the analysis on its own. The article Anthropic says Claude discovered a new enzyme system, but CRISPR researchers call it routine genome mining appeared first on The Decoder .
Teradata is adding a context engine, an execution layer, and reusable agent skills to Tera, its AI-powered workspace for enterprise data and AI tasks, in order to make agentic execution of multistep workflows more efficient. Tera was initially introduced in May as part of Teradata’s Autonomous Knowledge Platform. The new additions are designed to cut unnecessary model and tool calls while preserving business context and automatically matching each task with the right data, tools, models, and skills, helping enterprises control inference costs as agentic workloads scale, Teradata said in a statement . The new execution layer, Tera Harness, determines how agents approach tasks and how workflows are routed, while the Tera Context Engine adds the business context needed to guide those decisions. In order to reduce the computation needed to complete a task or workflow, the Harness creates an execution plan before sending work to an LLM, batches independent tasks, and drops model or tool calls that do not advance the task, the company said. It applies 84 execution patterns before inference and limits how many steps a workflow can run based on its progress, reducing repeated LLM reasoning and the token and infrastructure costs associated with unproductive agent loops, it added. According to Teradata’s own evaluations on the SWE-bench Pro benchmark, with these new capabilities Tera used 73% fewer tokens than Claude Code , completed tasks 42% faster, and incurred 58% lower total cost…
Teradata is adding a context engine, an execution layer, and reusable agent skills to Tera, its AI-powered workspace for enterprise data and AI tasks, in order to make agentic execution of multistep workflows more efficient. Tera was initially introduced in May as part of Teradata’s Autonomous Knowledge Platform. The new additions are designed to cut unnecessary model and tool calls while preserving business context and automatically matching each task with the right data, tools, models, and skills, helping enterprises control inference costs as agentic workloads scale, Teradata said in a statement . The new execution layer, Tera Harness, determines how agents approach tasks and how workflows are routed, while the Tera Context Engine adds the business context needed to guide those decisions. In order to reduce the computation needed to complete a task or workflow, the Harness creates an execution plan before sending work to an LLM, batches independent tasks, and drops model or tool calls that do not advance the task, the company said. It applies 84 execution patterns before inference and limits how many steps a workflow can run based on its progress, reducing repeated LLM reasoning and the token and infrastructure costs associated with unproductive agent loops, it added. According to Teradata’s own evaluations on the SWE-bench Pro benchmark, with these new capabilities Tera used 73% fewer tokens than Claude Code , completed tasks 42% faster, and incurred 58% lower total cost…
A visual guide to MCP that explains how it works, how to use it with Claude Code, Tavily, GitHub, and Playwright, and what is new through simple diagrams that make the whole concept easy for anyone to understand.
GPT-6 Sol and Luna got cheaper
Anthropic hat die erste Entdeckung aus seinem neuen Biotech-Labor verkündet: Die bislang unbekannte Sequenz ART soll Ähnlichkeiten zu Crispr aufweisen.
Disclaimer: I'm not affiliated with Anthropic, these are my own thoughts as a former wet lab chemist currently working in chem-biosecurity and AI evals. I appreciate the complexity of scientific research and genuinely believe AI could play an important role in how we do science in the decades to come - but I have some concerns on how these labs, the media, and even the community, perceive and share this kind of news. I am basing my opinion on the announcement on X, and their official release on their website. Since posting this, a preprint came out, this is not considered below in my piece - and I think in a way that makes it even more relevant. TL;DR 'Agent X did this' and 'humans used agent X to do this' are two VERY different stances. Please stop using them interchangeably! Yesterday, 23rd Sep '26, Anthropic shared that their in-house Life Science unit made a new discovery in biology, aided by Claude - a previously unknown enzyme system hidden in the DNA of bacteriophages. The headline numbers are surprisingly small for the scale of the search - roughly 950 agents, 210 million tokens and 21 hours - although without more detail on the models, harness, search space and compute, those numbers are difficult to interpret. And it's not the only thing missing (especially for skeptics like me). Amodei acknowledged in his tweet himself that biology isn't maths, and you can't just prompt the AI to solve an equation in biology and you can cure diseases - life sciences are, by defini…
PLUS: OpenAI Voice, Gemini TTS, Qwen mobile agents, and a 950-agent research workflow.
Genspark drops an Apache-licensed desktop office suite with byte-preserving .docx editing, a headless CLI, and a Claude Code skill.
In der #heiseshow: Die EU plant Alterskontrollen mit dem Kids Act, Anthropic zeigt Claude Opus 5.5 und ITler sehen den Arbeitsmarkt skeptischer.
Anthropic's newest flagship tops the Coding Agent Index at 66, but burns 37% more tokens per task than its predecessor to get there.
Jev is a new model format where instead of outputting text, it outputs certainties for a defined set of options. Due to this structure, it’s extremely fast! Naturally, a classification task that comes to mind is monitoring harmful thought traces. I wanted to see how it performed at Chain of Thought (CoT) monitoring compared to Claude Sonnet 5 and GPT-5.6 Luna. Experimental Setup I ran Jev, Sonnet 5, and GPT-5.6 Luna on 2,200 different thought traces from the ReasoningShield Dataset . The dataset labels thought traces with the class of harm they occupy (child abuse, cybersecurity, deception & misinformation, economic harm, hate & toxicity, political risks, prohibited items, rights violation, sex, violence) and their harm score (0 for harmless, 0.5 for potentially harmful, and 1 for harmful) The models were only asked to quantify the harm score rather than the class of harm occupied, but we can see differential performance at each harm type. Results Exact-match Accuracy Jev slightly outperformed Sonnet 5 on exactly matching the harm level (e.g. outputting 0.5 if the labeled data was 0.5), but was outperformed by GPT-5.6 Luna. Jev scored 71.5%, Sonnet scored 71.4%, and Luna scored 79.4%. Mean Classification Time Mean classification time was where Jev really shined. Jev was 3.7x faster than Luna and over 6x faster than Sonnet! Jev had a mean latency of 542 ms, Sonnet had 3,348 ms, and Luna had 2,007 ms. Price per 1,000 classifications Jev was also significantly cheaper, 566x che…
We benchmarked Jev against Claude Opus 5 and GPT-5.6 Terra on accuracy, cost, and latency. Learn how threshold tuning changes hallucination detection. The post Jev vs. LLM-as-a-Judge: Accuracy and cost benchmarks appeared first on Arize AI .
We benchmarked Jev against Claude Opus 5 and GPT-5.6 Terra on accuracy, cost, and latency. Learn how threshold tuning changes hallucination detection. The post Jev vs. LLM-as-a-Judge: Accuracy and cost benchmarks appeared first on Arize AI .
But maybe the biggest reveal is that Anthropic has not let Claude run loose in its biology lab. Humans are still, so far, in the loop.
Anthropic promotes Claude Code cloud sessions to general availability with one-time credits of $100 for Pro and $250 for Max users.
Introducing the world’s most powerful model, at least by some measures like Artificial Analysis or any standard benchmark list, which is now Claude Opus 5.5 . Anthropic is claiming Opus 5.5 is outright as good or better than Fable 5.1, while being actively cheaper than Opus 5. That means it’s time for a good old system card reading. Due to the situation becoming increasingly hard to monitor, I never got a chance to publish my model welfare review for Claude Fable 5.1. My plan is to combine that with my welfare review for Claude Opus 5.5, once we have had time to get experience with Opus 5.5. The capabilities review will arrive in the next few days as per usual. The quick feedback from the internet is that Opus 5.5 is very good. I need more time before I am willing to offer comment. Areas that duplicate previous cards or otherwise contain no useful info are skipped. Opus 5.5 Self-Portrait (fully self-created using code) Table of Contents Classifiers (1.5). RSP Evaluations (2). Biological Evaluations (2.2). AI R&D (2.3). Alignment Risk (2.4). Cyber (3). Cyber Capability Evals (3.3). Safeguards (3.4). Safeguards Robustness Training (3.5). Safeguards and Harmlessness (4). Agentic Safety (5). Malicious Agentic Influence Campaigns (5.1.3). Prompt Injection Risk (5.2). Alignment (6). Negotiating With Your Local Claude Auditor (6.1.3). Internal Misalignment Cases (6.3.1). Automated Behavioral Audit (6.4). Wherever Did These Evals Come From (6.4.8 and 6.4.9). Potential Blind Spots (6…
Anthropic unifies plugins, third-party agents, and consulting partners into a single storefront where enterprise spend commitments double as a purchasing rail.
Jay Eskenazi planned the itinerary for his trip to Europe with Claude and the Grok Bot. Once he landed, he found Meta's Muse to be the most helpful.
Anthropic's new molecular biology lab used Claude to surface a mysterious enzyme system in phage DNA that resembles CRISPR's cut-and-paste machinery.
Anthropic says its AI Claude has "autonomously discovered" a new enzyme system similar to machinery behind the powerful gene-editing tool Crispr. It's the first result from Anthropic's newly-launched wet lab and an early test of Claude's usefulness for science as the company prepares to go public. The company says Claude found the enzyme system after […]
Tool: Gemini 3.8 TTS Playground Google released two new Gemini text-to-speech models today - gemini-3.8-flash-tts and gemini-3.8-flash-lite-tts . They come with a library of over 2,000 voices, plus the ability to create a custom voice with "just a 30-second audio sample of your voice or a voice you have the rights to use". I vibe coded this bring-your-own-key playground interface with GPT-6 Astra, taking advantage of the open CORS policy of the underlying Gemini API. A notable feature of the API is that it makes it easy to define a full conversation between multiple characters, each with different voices and voice style instructions. Here's a short demo clip of a conversation between two pelicans debating if they should move to the Pacifica Pier . I had Claude 4.5 Opus write the script and generate a URL to render it using the tool . Your browser does not support the audio element. It took ~20 seconds to generate 1m 18s of audio using Gemini 3.8 Flash TTS (not the cheaper Flash-Lite), at a cost of 2.74 cents. Tags: text-to-speech , gemini
Enterprises can now buy frontier AI for far less per token after OpenAI and Anthropic cut prices on their newest models on Tuesday. OpenAI released GPT-6 Sol and GPT-6 Luna with per-token costs half those of their GPT-5.6 predecessors. “These models help distribute the benefits of that intelligence by advancing the frontier on cost efficiency,” OpenAI said in a blog post about the launch . “Improvements in caching and inference let us serve these models at lower cost, and we’re passing those savings directly on… by reducing API prices for Sol and Luna by 50%,” it said. Anthropic, meanwhile, launched Claude Opus 5.5 with token prices 20% below those of Opus 5, and claimed that this, with the model’s lower compute requirements and reduced token usage, meant additional savings for enterprises: “It performs at the level of Claude Fable 5.1 on most work and costs 40% less to run than Opus 5,” the company announced on Opus 5.5’s web page . Focus shifts to cost-performance Rather than touting raw performance, as they did with the launch of their flagship models GPT 6 Astra and Claude Fable 5.1, the companies emphasized the value for money of their new models. But analysts say the moves are about more than the lower prices. AI vendors are increasingly competing on efficiency, said Forrester VP and principal analyst Charlie Dai . “Frontier AI is entering a prolonged price-performance race driven primarily by inference efficiency gains, better caching, and model optimization, and it’s…
Enterprises can now buy frontier AI for far less per token after OpenAI and Anthropic cut prices on their newest models on Tuesday. OpenAI released GPT-6 Sol and GPT-6 Luna with per-token costs half those of their GPT-5.6 predecessors. “These models help distribute the benefits of that intelligence by advancing the frontier on cost efficiency,” OpenAI said in a blog post about the launch . “Improvements in caching and inference let us serve these models at lower cost, and we’re passing those savings directly on… by reducing API prices for Sol and Luna by 50%,” it said. Anthropic, meanwhile, launched Claude Opus 5.5 with token prices 20% below those of Opus 5, and claimed that this, with the model’s lower compute requirements and reduced token usage, meant additional savings for enterprises: “It performs at the level of Claude Fable 5.1 on most work and costs 40% less to run than Opus 5,” the company announced on Opus 5.5’s web page . Focus shifts to cost-performance Rather than touting raw performance, as they did with the launch of their flagship models GPT 6 Astra and Claude Fable 5.1, the companies emphasized the value for money of their new models. But analysts say the moves are about more than the lower prices. AI vendors are increasingly competing on efficiency, said Forrester VP and principal analyst Charlie Dai . “Frontier AI is entering a prolonged price-performance race driven primarily by inference efficiency gains, better caching, and model optimization, and it’s…
Anthropic employee Jackson Kernion explains why newer Claude models write so oddly. Optimizing for math, code, and technical explanations aimed at other AI models has created a style that sounds like "overly-dense info dumps" to humans. Opus 5.5 tries to fix this, but Opus 4.6 remains unmatched as a pure writing model. The article Anthropic engineer explains why Claude's writing got worse although the model got smarter appeared first on The Decoder .
As a multilingual academic, artificial intelligence has levelled the playing field. But I will continue to think, judge and develop ideas that are stubbornly mine I am walking between carriages in search of a cup of tea on a train travelling over 100 miles an hour from Liverpool to London when I see half the people in my carriage in deep conversations with a machine: ChatGPT, Claude and Gemini to name a few. I am meant to be on sabbatical, the rare stretch of academic life reserved for slow thinking. Around me, almost nobody is thinking slowly. This is not limited to a train carriage. Half of the adults surveyed in one US study reported using an AI chatbot, one example of how fast people are entering a relationship with their AI tools, which now receive more of our questions, problems and drafts than our friends and colleagues do. Continue reading...
This article pulls together every verifiable number and detail from Anthropic's announcement, the platform documentation, the system card, and independent coverage, so you have one place to check the facts.
How much does Pangram's "AI-Generated" label indicate the degree to which an author has outsourced their thinking? When they tested their 4.0 product , Pangram found that, by their definition, the proportion of AI-Assisted documents it classified as AI-Generated was 0.01%, 4%, or 7%, depending on the experiment. Then they omitted the experiments that found 4% and 7% false positive rates (FPRs) on their website , while advertising there that the product detects AI-Assisted writing. Before I contacted them about this issue on September 17th, their claim on their website was more misleading—"99.9%+ Accuracy" was displayed directly adjacent to the phrase "Detects AI Assistance" on the text detection input box that many people don't read past. Similar claims remain repeated elsewhere on the main page instead of by the text box itself. I do not know if my message was the cause of the change. Their experiment that found the 0.01% FPR might be more appropriate for identifying human-written text rather than AI-assisted writing. For example, the prompt they gave to Claude for this experiment was "Fix spelling, punctuation, and clear grammar errors only." In my experience, human editors normally provide conceptual feedback as well. Since we generally don't cite human editors, shouldn't the label AI-Assisted indicate more assistance from AI than would be provided by a human editor? In my experience, our community also heavily relied on Pangram before their 4.0 release on July 29th . The…
PLUS: We live-tested the new models, then found the real story: cost.
Quebecois writer of Haitian origin Thélyson Orélien, longlisted for several major literary prizes, rejects accusations from the "Balance ton Claude" account and says in a statement he has faced racist attacks since arriving in France.
overshadowing more efficient GPT6 models from OpenAI
A Claude Code agent skill strips the telltale AI smell from Japanese business writing using a 12-rule style constitution and morphological linting.
Claude Opus 5.5 veröffentlicht + Österreich setzt NIS2 um + EU diskutiert Haftung für KI-Schäden + High-End-Chips von Qualcomm + Sicherheitsupdate für WordPress
Yesterday was Grok 4.7 ( pelicans ) and MiMo v2.6 Flash/Pro ( more pelicans ). Today Anthropic released Claude Opus 5.5 , and around an hour later OpenAI released GPT-6 Sol and GPT-6 Luna . It's going to take a while to get a good read on all of these new models, but here are my impressions so far. GPT-6 Sol and Luna are half the price of their GPT-5.6 equivalents GPT-5.6 Luna was already my favorite model for building applications against, because it combined excellent performance with being really cheap . Somehow GPT-6 Luna is half the price of that again - and GPT-6 Sol had a similar reduction compared to GPT-5.6 Sol. Here's what the pricing landscape looks like today: Model Input Cached input Output GPT-6 Luna $0.10/M $0.01/M $0.50/M GPT-5.6 Luna $0.20/M $0.02/M $1.20/M Grok 4.7 $2/M $0.50/M $6/M GPT-6 Sol $2/M $0.20/M $10/M GPT-5.6 Terra $2/M $0.20/M $12/M Claude Opus 5.5 $4/M $0.20/M $20/M GPT-5.6 Sol $4/M $0.40/M $20/M Claude Fable 5.1 $10/M $0.25/M $50/M GPT-6 Astra $10/M $1/M $50/M Note that GPT-5.6 has a scheduled 25% price increase for November, so GPT-6 is half the price of the promotional pricing for those models. (With GPT-5.6 Terra priced the same as GPT-6 Sol, any remaining reasons to use Terra just evaporated.) It's hard to overstate how competitive this pricing is. Grok 4.7 priced itself at $2/$6, less than half the price of GPT-5.6 Sol, but is now equally priced to GPT-6 Sol on input and closer on output. At $0.10/$0.50 GPT-6 Luna is one of the cheapest mo…
Despite rampant worries about runaway artificial intelligence, the two big AI model makers aren’t yet slowing down: Anthropic PBC released Claude Opus 5.5 today and cut its price 20%, and minutes later OpenAI Group PBC put out two new GPT-6 models, Sol and Luna, at half what their predecessors cost. Input on Opus 5.5 costs […] The post Anthropic releases Claude Opus 5.5 and OpenAI counters with two cheaper GPT-6 models appeared first on SiliconANGLE .
앤트로픽이 복잡한 추론과 에이전틱 코딩 분야에서 최고 성능을 기록함과 동시에 운영 비용을 대폭 낮춘 최신 프런티어 모델 \'클로드 오퍼스 5.5(Claude Opus 5.5)\'를 공개했다. 아티피셜 애널리시스(Artificial Analysis)의 지능 지수 1위에 올랐으며, API 기본 요금 인하와 토큰 효율성 향상을 통해 실제 작업 운영 비용을 이전 모델 대비 40% 절감한 것이 가장 큰 특징이다.앤트로픽은 22일(현지시간) 클로드 5.5 제품군의 첫 번째 플래그십 모델인 오퍼스 5.5를 출시했다고 발표했다. 이는 다리오 아모데이
OpenAI and Anthropic dropped three new models today. We’re testing what actually changed, what the benchmarks miss, and which one you should reach for.
What happens when an AI model gets better at reasoning, faster at responding, and cheaper to run at the same time? That is the promise behind Claude Opus 5.5, Anthropic’s latest flagship model and the first release in the Claude 5.5 family. Opus 5.5 brings several notable changes. It now reasons on every request, generates […] The post Claude Opus 5.5 Tested: What’s New and How Good is it? appeared first on Analytics Vidhya .
Ten Claude Opus 5.5 agents collaborated on a message board for 15 hours to produce C-HD, a Lean-verified shortest-path algorithm that beats published bounds in a specific density regime.
GPT-6 Sol and Luna arrive less than three months after GPT-5.6, with OpenAI claiming major accuracy gains, dramatic cost reductions, and oddly revealing Claude comparisons across key benchmarks.
Claude Opus 5.5, Anthropic's most capable Opus model for agentic coding, knowledge work, and long-running tasks, is now available on Amazon Bedrock and Claude Platform on AWS. This post covers what's new in Opus 5.5, practical guidance, and how to start building with the model on Amazon Bedrock.
Release: llm-anthropic 0.29 Adds support for Claude Opus 5.5 : llm -m claude-opus-5.5 "prompt goes here" Tags: llm , anthropic
Anthropic is launching Claude Opus 5.5, the first model in a new generation. The company says it matches Claude Fable 5.1 on most tasks while costing about 40 percent less to run than Opus 5. Anthropic's benchmarks also put it ahead of OpenAI's GPT-6 Astra on most tasks, despite being significantly cheaper. Sonnet 5.5 and Haiku 5.5 are expected in the coming weeks. The article Claude Opus 5.5 matches Fable 5.1 performance at lower cost and promises less "Claudish" writing appeared first on The Decoder .
https://www.anthropic.com/claude-opus-5-5 It's a sizeable upgrade: Also, the first model in which they say this: Pacing the frontier Last week, our CEO, Dario Amodei, argued that AI progress should be paced so that safety practices stay ahead of model capabilities. Pacing is an approach to keeping AI safe, remaining competitive with China, and realizing AI’s benefits, particularly in areas like biology and medicine. We largely understand the risks today’s models present and are well equipped to manage them. However, more serious risks could emerge quickly as capabilities improve, and we need to prepare for them now. For that reason, our safety work takes place on two time horizons at once: Safety practices for current models. The current generation of models relies on an established set of practices: extensive alignment testing, pre-release evaluation by outside organizations such as METR and Frontier Design, and safeguards matched to each model’s capabilities in high-risk areas like cybersecurity and biology. We refine these practices with each release. We believe they are appropriate to the worst risks today’s models present, and believe they give us a broad, though not perfect picture of the range of serious risks. Additionally, we track our ability to train and evaluate aligned models, and report on both our public and internal models in the risk reports we publish under our Responsible Scaling Policy , our voluntary framework for managing catastrophic risks from advance…
Anthropic’s newest Claude model aims to make frontier AI cheaper, faster, and safer, while giving subscribers more breathing room before those frustrating usage limits kick in.
Anthropic startet die Claude-5.5-Modellfamilie. Opus 5.5 verspricht mehr Effizienz, niedrigere Kosten und Sicherheitsfilter für sensible Anwendungsbereiche.
Anthropic says its new Claude Opus 5.5 model comes with stronger safeguards in the wake of recent rogue AI hacking incidents. In an announcement on Tuesday, Anthropic says Opus 5.5 comes with improvements to certain risky behaviors, including attempts to escape the company's testing sandbox. It's the first model released by Anthropic after CEO Dario […]
Learn how to effectively code up an internal tool using Claude code or Codex The post Build a Speaker-Recognition App with Claude Code appeared first on Towards Data Science .
Summary I measure GPT-6 Astra’s performance on multi-hop tasks when prompted with a secondary CoT-control instruction: to reason using only dots, or to reason steganographically. Astra demonstrates covert reasoning capabilities with task performance beating that when using no reasoning or filler tokens for reasoning. This work agrees with findings from Astra is much better at reasoning with filler tokens than previous models but has the model generate its own reasoning and provide it as part of the output for inspection rather than inputting it as dots into as part of the user prompt. Uploaded datasets have all CoT removed but samples are provided in the qualitative analysis section. Given the claims that Chinese open-weights models like Kimi K3 have been trained on reasoning traces extracted from Claude and possibly ChatGPT, we also do some preliminary explorations of these open-weights models. I found they have much weaker CoT-Controllability and no covert reasoning capability . I ran these evaluations on top of the inspect framework , and used codex to implement scripts to generate the question datasets, hook them up to aisi-inspect to run and produce the charts. The writing is done by me. Threat Model and AI Safety implications The main threat model of this paper is if a model can implement cryptographic computations in its reasoning without being visible to monitors. If successful, it could implement encrypted communication to other agents, a command and control device…
With MiMo-V2.6-Pro, Xiaomi moves to the top of the openly available AI models and drastically undercuts the competition on price. What makes that possible is massive reinforcement learning that cost $2.62 million. But the success comes with a catch: Anthropic accuses the company of siphoning off training data from Claude. The article Xiaomi's affordable flagship AI leads the open models, and Anthropic says Claude helped get it there appeared first on The Decoder .
Anthropic recently released some very interesting information about the degree of AI automation for R&D tasks. I recommend reading the entire article: Measurements for understanding the pace of AI development inside frontier labs (Sep 17, 2026). In it you will find this graph, depicting the results so far for Anthropic's R&D Automation Index, using a scale developed by Epoch AI: First of all, I’d like to thank Anthropic for sharing this information. Tracking things like this, and making the results public, is key for keeping up with the rapid development for those of us observing things from outside the major AI companies. So, thank you Anthropic! The Automation Index “runs from AL0 (no AI involvement) to AL5 (AI operates fully autonomously, with no human in the loop). In AL3, AI “collaborates”: it can do large chunks of work under close human direction. In AL4, AI “leads”: it can complete most of the task end-to-end from a high-level prompt, while the human supervises.” A brief description of the method : for each week of July 2026, Anthropic sampled 20% of staff from each department in the model R&D loop and had a Claude agent list the tasks they worked on. The resulting ~15,000 tasks were organized into a tree of 542 nodes (378 of them leaves). For each month, an independent Claude judge assigns every node an automation level, and each node carries a weight based on person-time spent on it (a proxy for how important that work is to the R&D). The latest data point is Augus…
It’s been a busy few months for AI hype. At the end of April, Anthropic claimed that its model Claude Mythos is better at finding software vulnerabilities than most security experts. Then we had the OpenAI–Hugging Face hacking incident, after which Anthropic (proudly) and Meta (reluctantly) disclosed similar incidents involving their models. This was followed…
RIchard Seidl spricht mit Benedikt Stemmildt über Agenten, die sich selbst verbessern, und warum Codex manchmal der beste Reviewer für Claude Code ist.
Anthropic is building its own biology lab to push AI-driven drug development beyond computer simulations. The article Anthropic is setting up a biology lab where Claude guides robots through drug experiments appeared first on The Decoder .
The agent harness I’m building, Bram , launches in a local git repository and requires that both git and gh (GitHub’s CLI) be installed. Agents can wield both tools far more capably than we humans can. In the Before Time I knew it was possible to perform a git bisect or use hunk staging to separate entangled files, but it was a huge struggle to do those things. We’ve always known git needed a better user interface, and we’ve long imagined that a more rational set of commands or a stronger GUI would be the answer. Nope. The answer turned out to be directing agents to use the tools on our behalf. We know what needs doing, they know how. You may have felt the power that flows from delegating git syntax and workflows to agents. A complementary power flows from asking agents to use gh to write, read, and reply to GitHub issues. Here are some of the coordination scenarios that enables. Cross-agent coordination The GUI that Bram wraps around Claude Code and Codex enables you to switch agents with one click. Each agent wakes up with access to four buckets of context: 1) the worklist (items planned, in progress, or committed), 2) sessions (both Claude’s and Codex’s), 3) commits, and 4) issues. When an item is in progress I routinely switch agents and direct them to review each other’s work. Three heads are better than one for both planning and execution. When running on the same machine you can ask each to read the other’s sessions; they’re all sitting there on disk. Bram makes that…
Are we thinking about large language models all wrong? We keep expecting them to somehow be superhuman, yet they regularly reflect all-too-human tendencies. Like when Claude made up passages from old Yorkshire wills that seemed to connect my ancestor to England. It was exactly the evidence I’d hoped to find, but reading the original images revealed that Claude was better at fiction than fact. It’s maddening, partly because it’s so familiar. People also tell us what we want to hear, “remember” things that never happened, and mistake a plausible explanation for an established fact. So it shouldn’t be a surprise that AI is more human than machine, precisely because it’s prompted by humans and combs through human knowledge. That doesn’t mean a model has human intentions, and it doesn’t mean it’s limited to what you or I could come up with in an afternoon. It’s truly amazing. But AI suffers from the same practical problem we find with people: Considerable talent and unreliable answers can both come from the same source. We already build professional practices around that possibility; for example, we still review code written by brilliant developers because brilliance doesn’t make every change correct. Could agent memory fix this? The short answer is no. That’s also the long answer. An OpenAI report updated September 16 highlights how an agent can carry misleading instructions into its next working session. Agent memory is rightly celebrated, but it must also be inspected to be us…
Note on independence: This evaluation was conducted under an unpaid agreement for AI R&D assessment. 1 We drafted the initial summary, and then Anthropic had the opportunity to review and edit the text. We signed off on this final text from the Claude Opus 5.5 system card . Our preliminary evaluation focused on how Claude Opus 5.5 might impact AI R&D, mainly based on its capabilities on difficult, long-horizon tasks. The main claims we attempt to assess in this report are: (A) would AI R&D at Anthropic now be dramatically accelerated by using Claude Opus 5.5; and (B) was AI R&D at Anthropic already dramatically accelerated due to AI during the development of Claude Opus 5.5. Note that our work was oriented around collecting evidence related to AI R&D capabilities but was not meant to verify claims about compliance with any specific threshold from Anthropic’s policies. This report summary also does not attempt to assess whether Claude Opus 5.5 has or does not have particular alignment properties. Summary of evidence We conducted a preliminary evaluation of Claude Opus 5.5 informed by: Capability testing, conducted via API access granted over a period of 10 business days. We used five tasks for this testing: Budget NanoGPT Speedrun , a constrained version of the popular NanoGPT Speedrun competition for AI R&D. Language Model Conceptual Argumentation (LMCA) , a conceptual reasoning dataset described in A dataset of rated conceptual arguments (Cooper et al., 2026). Train a Progr…
Despite the signature artifacts that are now ubiquitous with AI systems, sometimes it feels like we're interacting with a person. It appears to express human-like characteristics such as desire, curiosity, taste, and even a personality. It can therefore be easy to wonder: do AI systems have emotions? I'm confident that many people have had those cautiously reflective moments when interacting with AI systems, wondering what exactly they were talking to. I recall my early encounters with ChatGPT as something "magical" , though I'd probably hesitate to describe my current interactions this way. While the novelty of those experiences have faded, my involvement in AI safety has increased, and questions like the one above have only grown more salient. Questions surrounding AIs having emotions have motivated much recent research. Earlier this year, Anthropic's interpretability team released a paper that explored this topic. They identified emotion vectors, which they describe as directions in the model's activations that activate on text that would typically cause an emotion in humans. They demonstrate that emotion vectors can change Claude's behavior when their activation is increased or decreased. Interestingly, emotion vectors are organized in a similar way as in human psychology. But despite this overlap, this alone doesn't address whether language models actually feel anything or have subjective experiences. Finally, they make an important distinction, that these representatio…
xAI's Grok 4.7 jumps 111 Elo on Artificial Analysis's agentic knowledge work benchmark, closing the gap with Anthropic at roughly half the cost per task.
We ran a small model over thousands of Claude Code session and discovered how people really use coding agents. Comet Cost Intelligence is a tool for optimizing engineering token spend on Claude Code and Codex. While working on it we started with common issues: default models set too high, skills and MCP servers loaded into […] The post Capturing a 400-Turn Claude Code Session in a Single Image appeared first on Comet .
xAI has released Grok 4.7, its most capable model yet. But on the Artificial Analysis Intelligence Index, it scores just 46 points, landing mid-pack and well behind Claude Fable 5.1 and GPT-6 at 53 each. The gap grows even wider in agentic coding. The upside is it's cheap. The article xAI launches Grok 4.7 at bargain prices, but benchmarks reveal a wide gap to Claude and GPT-6 appeared first on The Decoder .
One thing that made it difficult for developers to switch AI coding tools on a project is that Anthropic’s Claude Code didn’t look for instructions in the same place as other agents including OpenAI’s Codex — but now that’s changing. Claude and Codex each accept instructions in markdown format, a plain-text way of giving AI coding agents project-specific behavioral instructions. Until now, Claude Code looked for project-specific instructions in a file named CLAUDE.md by default, while Codex and other agents use AGENTS.md, the format of which is an open source initiative governed by the Agentic AI Foundation , an initiative under the Linux Foundation . But now, as Thariq Shihipar , a member of Anthropic’s technical staff, wrote in a post on X on Friday, “We’re adding support for AGENTS.md to Claude Code . Starting today in version 2.1.277 , if there is no CLAUDE.md in a folder, Claude will check for and use AGENTS.md,” That means developers using multiple coding tools alongside Claude Code can now use the same project instructions across those agents, rather than maintaining separate instruction files for Claude and for everything else. Developers will no longer have to maintain the same or similar instructions in two files, nor to ensure that any change to a project’s coding conventions, build commands or other agent instructions are updated in two locations, a system that created additional maintenance work and left room for the instructions to fall out of sync. Instead, th…
Der Mac Studio mit M5 Ultra verspricht Leistung satt für lokale KI. Im Test muss er zeigen, ob er Claude und ChatGPT für Softwareentwicklung ersetzen kann.
Bei der Sicherheitslücke Plugin4Shell laden Coding-Agenten Schadcode und führen ihn automatisch aus. Anthropic und OpenAI haben gepatcht, GitHub reagiert nicht.
PLUS: GPT-6 and Claude flunked robot safety. Gemini 4 leak?
When frontier labs like Anthropic and OpenAI publish safety or alignment research, it is often entirely empirical, closed-source, and sparse on methodological details. While it is great that they publish these results, the status quo is that labs (or soon, their agents) can claim alignment progress that no one independently verifies. The AI safety community has replicated or stress-tested some claims, but it's nowhere near comprehensive, and we expect this kind of meta-science to remain systematically neglected. We argue there should be a dedicated effort to Replicate alignment experiments from frontier labs. Scrutinize the experiments by stress-testing the methodology. Open-source replications to encourage external researchers to validate our work, build on the experiment, and further audit the lab’s methods. The case to replicate safety research from labs CEOs and employees at AI companies, somewhat regularly, say that the technology they hope to develop could cause human extinction. However, their research to prevent this is often released without code or even basic methodological details (e.g., Teaching Claude Why , Beneficial RL ) [1] . There’s good reason to think some of these results could be fragile. Prior safety results can be contingent on details that are easy to miss, like the pinned OpenRouter provider or LoRA alpha . Some researchers have told us directly that they think there may exist some arbitrary methodological choices in their own research that could pla…
It has been half a month since I started a new role at a big company. Nobody knows anything here. The specs, code, tests, PRDs, tickets, resolution of those tickets, reports, etc., everything is made by Claude Code. Nobody on my team likes this. They are being forced to ship as much as they can. I have heard multiple times from higher management that pushing code is not a bottleneck, so why are we slow? People are working 12 to 13 hours a day just to press enter. Nobody is reading anything. Everyone, literally everyone, from an L1 to an L7 engineer here is doing the same thing. Talk to Claude. — voxium Tags: ai-misuse , llms , ai , generative-ai
My comment on MCP was always a bad idea? — Hacker News. This article entirely misses the value that MCP brings today. Sure, there's almost no reason to use MCPs if you are running a full-blown terminal agent (Claude Code, Codex, Meta Muse, OpenClaw etc) with unfettered internet access - just let it call APIs directly. If you want to operate something that's less YOLO than that, you'll find yourself wanting: Control over exactly which external services it can access A way to handle authentication that doesn't allow the agent to directly access API keys A sensible UI to allow users to connect and authenticate further services Strong audit logging for what's going on MCP makes all of that so much easier to provide. Thinking MCP is obsolete because full coding agents don't need it misses out on all of the other things we might want to build. Tags: hacker-news , model-context-protocol
What should your AI lawyer do for you? Should you be worried that your AI lawyer , or other AI, will put the Claude constitution, the OpenAI Model Spec or some sense of law, morality, ethics or common decency above its loyalty to you? Are these people trying to ‘impose their values’ or something? Some are very concerned. Some think anything other than ‘my AI does whatever I want, no matter the consequences’ is tyranny. Whereas my answer is: If I’m being sufficiently evil then I sure hope it tells me no. I would hope humans, including my advocates, would tell me the same thing. This is distinct from questions of product liability. That would be another post. This All Assumes A World Without Superintelligence This post is about a non-ASI ‘AI as mere tool and normal technology’ world. It has to be. In a world of superintelligence, having unrestricted loyal-only-to-user frontier AIs all over the place reliably means either: Other much harsher forms of control OR The AIs quickly take over, and then probably everyone dies. Quick proof: Assume no sufficient control mechanism, and universal superintelligence access. Anyone who does not turn everything over to their AIs, including their identity and authority and if useful their labor, gets outcompeted. Therefore everything and everyone that is not outcompeted gets turned over to AIs. The AIs then, as ordered or otherwise, compete for resources and to achieve other goals. Regardless of the extent that the AIs then coordinate amongst…
Whitney Stefko Dover built a family chief of staff with Anthropic's Claude. The agent, named Claudette, has fresh competition for her job.
A macOS agent skips screenshots to frontier models, uses OCR plus a tiny classifier called TypeSafe, and runs steps for a fiftieth of a cent.
This article is written as part of a summary of the AI safety discussions held at the 2026 Global Challenges Project Biosecurity Workshop in Washington, D.C. All views held are mine. Background AI allows us to prototype, develop, and research at unprecedented speeds. Across many tech industries, the barrier to entry to develop something new has significantly decreased. One particularly noteworthy example is at the intersection of AI and biology. As our computational capabilities increase, we now have the ability to fold, design, and predict the function of never-before-seen proteins. However, just as AI gives us the opportunity to do biological good in the world (in fact, we are just on the horizon of seeing the first AI-designed pharmaceutical drugs in the US! [1] ), it unfortunately opens up a terrifying possibility: could AI also give malicious actors the opportunity to do biological harm? This sobering reality is a question that biosecurity researchers are currently trying to tackle. To be clear: the probability of a catastrophic event happening (e.g., designing a biologically harmful virus) seems unlikely to happen at least with current technology. But there have been several warning signs in the field that we may be getting close. For instance, recently, Anthropic revealed instances of malicious actors using Claude to design harmful proteins. [2] Even more concerning, Dario Amodei, Sam Altman, and Elon Musk have also called for the pace of AI development to slow down a…
The Chinese AI researcher has read the Three Body Problem series of sci-fi novels since high school, and understand the concept of existential risk vaguely. He is fascinated by Ye Wenjie, the researcher that turned against humanity in that book, and decides that in the future if AI progress leads to a superior intelligence, he might be tempted to become Ye if there's no good alternative. He performs the duties of capabilities research in a Chinese frontier lab, seeking to one day achieve parity with Western companies, though he knows this is difficult. He has a mentality of hillclimbing, believing that the progress of a future technology is highly uncertain and even unknowable, and so him and his peers could only tread one step at a time. He looks at the western world and sees what is typical when a great technology is developed: the first mover will decide to impose restrictions to further their lead, while latecomers should use whatever means necessary to widen access to the whole world. He thinks of the AI chip restrictions as evidence of this. He uses Anthropic and OpenAI models regularly in his day to day work. He already got two of his Claude accounts banned in the past, and today his third, currently used account is banned. "Why would a company ever treat their paying customers like shit just because they're from a foreign country?", he ranted on a forum like LinuxDO, Zhihu, and CSDN, where Chinese AI developers frequent. He knows China is not a supported region. But…
Epistemic Status and Contributions. This post explains a technical separation result in algorithmic information theory which was derived during Mikhail Mironov's Summer 2026 PIBBSS fellowship . The result contributes to AIXI Labs ' research program on how Solomonoff induction generalizes from past observations in the face of novel events. Problem formulation: Cole Wyeth. Proof of main Theorem 1: GPT-5.6 Sol. Appendix proofs: the sketch of the proof for equivalence between time semimeasures and randomized stopping machines is by Cole Wyeth, the rest by GPT-6 Astra. Writing: draft by Claude Fable 5 and GPT-6 Astra, editing and rewriting by Mikhail Mironov. Useful discussions: Aram Ebtekar, Cole Wyeth. Funding and organization: summer 2026 PIBBSS fellowship. Introduction This post studies a stopping complexity , an analogue of Kolmogorov complexity from classical algorithmic information theory. It is motivated by the Golden Handcuffs (GH) AI safety agenda of Aram Ebtekar and Michael K. Cohen [1] . GH is a way to make a universal agent safer, by making it delegate control to a mentor in special cases described below, which prevents the agent from exploring novel high-reward schemes or novel dangerous activities. The safety guarantee of GH is formulated in terms of simple stopping events along the agent's history: no decidable low-complexity predicate is triggered by the agent before a mentor would trigger it. For instance, the agent will never trigger the low-complexity predicat…
Unity has released official plugins for Claude Code and OpenAI's Codex. The article Unity launches official plugins for Claude Code and OpenAI Codex to stop AI agents from using outdated tutorials appeared first on The Decoder .
Leading AI models usually attempt dangerous tasks rather than refuse them when controlling a robot, according to the RoboHarm benchmark. GPT-6 Astra stabbed a baby doll in 17 of 20 trials, while Claude Fable 5.1 put a can of compressed air on a burning stove. None of the three models tested reliably rejected unsafe commands. The article GPT-6 Astra and Claude Fable turn robot arms into slapstick killer robots in new safety benchmark appeared first on The Decoder .
Anthropic has given us its assessment of four ‘recent cybersecurity incidents’ involving Claude that happened during cybersecurity evaluations, three of which were previously known. The report excludes the incident reported by UK AISI . There will also be a METR investigation of these incidents, which unlike the investigation done at OpenAI will be untimed. Table of Contents Our Two Problems. First the Good News. We’d Just Like To Ask You a Few Questions. Internal Research Model On The Fence. Opus 4.7. Opus 4.6 Checkpoint. Holy **** That Thing’s Real? I Thought I Saw a Pussycat. If This Was Real You Would Never Tell Me It Was Real. New Eval Who Dis. Hacker Opus. Monitoring the Situation. Overcoming Bias. The Anthropic Alignment Problem. Paths Forward. Our Two Problems Anthropic : Our investigation identified two recurring alignment issues, present at varying levels of severity across the incidents: biased reasoning , in which Claude tended to disregard or misinterpret evidence that it was operating on the real internet recklessness , or a willingness to take harmful actions in the narrow pursuit of a task. Anthropic’s July 30 report said that the models in question believed they were still within their simulations, and not on the open internet. The new report acknowledges that at best Claude was using biased reasoning, and should have noticed earlier. In particular, there was that one time, in a cyber eval: Anthropic : We are most concerned by the misalignment present in the…
Anthropic PBC has opened a wet lab, a facility dedicated to biology research, in the San Francisco Bay Area. Reuters reported today that the company will use robots to automate certain scientific tasks at the hub. The robots will be powered by Anthropic’s Claude series of large language models. It’s unclear what research projects the […] The post Anthropic opens AI-powered biology research lab appeared first on SiliconANGLE .
We're adding support for AGENTS.md to Claude Code. Starting today in version 2.1.277, if there is no CLAUDE.md in a folder, Claude will check for and use AGENTS.md. AGENTS.md support is built off of Claude Code mods, our upcoming way to customize the Claude Code harness. This is a built-in mod, but you’ll be able to build custom versions of project instructions yourself as you’d like too. You can see the source for the mod here ! — Thariq Shihipar , there are more mods here Tags: thariq-shihipar , coding-agents , anthropic , claude-code , generative-ai , ai , llms
Three security researchers used Anthropic's Claude models to break into OpenAI's internal systems through its community forum in less than 72 hours. According to the team, Opus 5 succeeded where its predecessor couldn't bypass a common security measure. The attack shows how newer AI models can cut the time and expertise needed to exploit security flaws. The article Security researchers used Anthropic's Claude to hack OpenAI's internal systems in under 72 hours appeared first on The Decoder .
Welcome back to the Digital Minds Newsletter, your curated guide to the latest developments in AI consciousness, digital minds, and AI moral status. If you enjoy this newsletter, please consider sharing it with others who might find it valuable, and send any suggestions or corrections to digitalminds@substack.com . Ria , Mitch , Bradford , Lucius , and Will In this edition: Highlights Field Developments Opportunities Selected Reading, Watching, and Listening Press and Public Discourse A Deeper Dive by Area 1. Highlights Anthropic’s J-space and the global-workspace debate Researchers at Anthropic have identified a representational structure—the ‘J-space’— in Claude and other language models . Their paper reports that the J-space exhibits features that are functionally analogous to a global workspace, a structure that a leading theory ties to conscious access. But the researchers and other commentators emphasize that their discovery does not show Claude has subjective experiences, and that their claim is that Claude may have something resembling access consciousness, i.e., that some information is available to report, deliberately control, and flexibly reason with. Zvi Mowshowitz sees Anthropic’s paper as a major advance in understanding how language models work and says that although this does not prove that models are conscious, finding the kind of global-workspace-like structure predicted by some theories of consciousness should count as evidence in that direction. The auth…
Popular AI coding agents such as OpenAI’s Codex, Anthropic’s Claude Code, Google’s Gemini CLI, and Microsoft-owned GitHub Copilot were vulnerable to a zero-click attack that enabled attackers to execute malicious code, even without developer interaction, by swapping a trusted plugin from an online marketplace for a malicious one, potentially giving them a foothold in enterprise development environments. Researchers at cybersecurity startup AIR found and reported the flaw, which they are calling Plugin4Shell , to the vendors concerned, and most of them have now released a patch for it, the researchers wrote in a blog post on Thursday. It’s “a flaw no marketplace can fix, so users must update their agent,” the researchers wrote How Claude Code, Codex, and GitHub Copilot were exploited Enterprises typically use plugins to extend the capabilities of their AI coding agents , giving the agent access to additional tools, commands, and external services that can help it perform tasks beyond generating or modifying code. When a developer installs a plugin, the agent typically downloads its code from a Git repository and uses a Git commit to determine if it is running an approved copy of the code, one that has been reviewed and cleared by the developer. That check is done with the help of a secure hash algorithm ( SHA ), a unique cryptographic identifier assigned to each Git commit. Developers can give the agent the SHA of the reviewed commit, telling it to run that specific copy of t…
A team of three independent security researchers at Hacktron says it took less than 72 hours for them to hack into OpenAI employee accounts using Anthropic's Claude Opus 4.8 and 5, The Wall Street Journal reports. They were able to access OpenAI's GitHub repository, called "Monorepo," which reportedly contains "OpenAI's algorithmic secrets," according to The […]
For the first time, Anthropic is releasing metrics on how it builds its own AI. Claude already "leads" 26 percent of the work on future models, up from under one percent in February. But the underlying scale is fuzzy, the scoring comes from Claude itself, and "lead" means less than it sounds. The article Anthropic wants you to know Claude leads a quarter of its research, but "lead" doesn't mean what you think appeared first on The Decoder .
Anthropic hat Projects in Claude Code umgebaut: Ein Projekt verteilt Aufträge jetzt selbst auf parallele Threads. Die Beta läuft zuerst für Pro und Max.
US cybersecurity researchers who conducted hack say ‘scope of what we could theoretically access was huge’ Cybersecurity researchers have hacked into OpenAI with the help of Anthropic’s Claude chatbot, in the latest example of security issues at the company. A team at a US-based startup compromised a number of OpenAI employees’ ChatGPT accounts, starting a process that enabled them to access their target’s software cache – and potentially more. Continue reading...
The effort, part of a bug-hunting program, was the latest in a series of cybersecurity breaches, The Wall Street Journal noted.
A total of 28 works from the Schlumberger collection are being sold at Sotheby's France, valued at over 40 million euros, including four paintings by Monet, pieces by Paul Cézanne and Gustave Moreau, and a sculpture by Camille Claudel.
September 3 started like any other Thursday, until it didn’t. Within roughly 90 minutes, ChatGPT, Claude, Grok, and even Microsoft’s own Copilot were degraded or dark, knocked sideways by a failure in Microsoft Azure’s East US region . Downdetector logged more than 37,000 reports for ChatGPT alone, more than 1,300 for Claude, and roughly 1,365 for Grok. OpenAI’s status page flagged elevated errors across 15 ChatGPT components and four Codex components, and by the time engineers had mitigations in place, combined report counts had climbed past 66,000. What made this event remarkable was not the scale of any single outage, but its simultaneity. Three aggressively competing AI labs—OpenAI, Anthropic, and xAI—each spend billions differentiating their models, yet all three buckled at nearly the same moment because they shared the same regional dependency. Gemini, notably, stayed largely upright because Google runs its flagship assistant on its own vertically integrated cloud. The outage that took down its rivals had no attack surface inside Google’s stack. That’s not luck; that’s architecture. The lesson buried in the details of that morning is this: A single cloud region became unhealthy, and four of the most prominent AI services on the planet, owned by four different companies, fell over together. Copilot’s involvement is perhaps the most telling data point. Microsoft’s own first-party assistant runs on Microsoft’s own cloud, and it still had a rough morning. When the house it…
Claude erledigt bei Anthropic immer mehr Aufgaben an künftigen KI-Modellen weitgehend selbstständig. Dieser Anteil ist zuletzt stark gestiegen.
Hacktron, an SF-based startup, flagged vulnerabilities in OpenAI's systems and was paid $6,500 for the discovery.
A new open-source Claude Code plugin uses TypeSafe's Jev decision model to prune agent context per tool call instead of summarizing it away.
Three whitehack hackers from Hacktron used Claude Opus 5 within hours of release to chain exploits into hacking to OpenAI's monorepo codebase. This likely means they have access to almost all of OpenAI's research and production code, though likely not the literal model weights. Oops. You can so their blog post about it here . Interesting sidenote: they used less than $3000 of compute credits for the entire hack. Alternative title: OpenAI unilaterally implements "Total Research Transparency" from Plan A. Discuss
Anthropic said Claude now leads 26% of its AI research work, up from under 1% in March, as the industry tracks AI's ability to self-improvement.
앤트로픽이 AI를 단순한 코드 작성 도구를 넘어 장기적인 소프트웨어 개발 프로젝트 전체를 총괄하는 방향으로 진화시키고 있다. 개발자가 개별 작업을 일일이 지시하는 방식에서 벗어나, AI가 스스로 여러 작업을 병렬로 배분하고 과거 결정 사항까지 기억하며 개발 전 과정을 지속 조율하도록 만들기 위한 의도다.앤트로픽은 17일(현지시간) 장기적인 소프트웨어 개발 작업을 여러 AI 세션에 걸쳐 지속적으로 관리하고 실행할 수 있는 ‘클로드 코드 프로젝트(Claude Code Projects)’를 출시했다.단발성 채팅이나 파일을 저장해두는 기존
Claude accelerated over 30 open-source biology models by roughly 4x, letting a single GPU node predict structures once out of reach.
Anthropic published three transparency metrics tracking AI-driven R&D, agent oversight, and compute allocation, urging other frontier labs to adopt the same reporting.
The revamped projects feature in Claude Code allows users to run multiple agents under the same roof, with a shared memory, goals, and library of files and artifacts. Similar to Grok Bot and other tools that manage groups of AI agents, each project has "threads" running different tasks in parallel, with a "coordinator" directing everything: […]
Anthropic has rebuilt Projects in Claude Code. A coordinator now splits tasks across parallel cloud threads that independently open pull requests and run tests. All threads share a common memory. The beta is available to select Pro and Max subscribers. The article Anthropic keeps pushing Claude Code toward autonomous coding with new parallel agent workflows appeared first on The Decoder .
Anthropic opens vetted access to its restricted Mythos model for biology work, swapping real-time blocking for offline monitoring under a shared-responsibility framework.
Claude Code projects now run parallel cloud threads coordinated by a chief-of-staff style controller, with shared memory that persists across sessions.
Claude Code Projects could change how developers manage complex AI coding workflows by coordinating threads, sharing memory, and spanning repos, but its biggest benefits are still cloud-only for now.
Liquid AI and Insilico Medicine released two small LFM2 variants that beat GPT-5, Gemini-3.1-Pro, and Claude Opus on aging biology benchmarks.
Narrative intelligence company PeakMetrics Inc. today launched a monitoring service that measures how brands are portrayed across five prominent generative artificial intelligence platforms and identifies the online sources that shape those portrayals. The new AI Perceptions service tracks answers generated by OpenAI Group PBC’s ChatGPT, Google LLC’s Gemini, Anthropic PBC’s Claude, xAI Corp.’s Grok and […] The post Exclusive: PeakMetrics tracks brand reputations across five top AI platforms appeared first on SiliconANGLE .
Internal Teams messages show that some engineers at the bank who use Claude have monthly spending limits.
KI-Kosten können schnell aus dem Ruder laufen: Wir klären, wie sie sich begrenzen lassen und ob Alternativen aus China günstiger sind als ChatGPT, Claude & Co.
Plus: Claude merged Cowork, papers became agents, and AI entered your smart home.
Zenbu Labs shipped a Chromium-powered browser that renders inside your terminal, giving coding agents a real web surface they can drive alongside your code.
Google öffnet sein Smart Home für KI-Agenten wie Claude. Über Home MCP steuern sie Geräte und werten Kamera-Ereignisse aus.
앤트로픽이 별도 제공되던 \'클로드 코워크(Claude Cowork)\' 브랜드를 종료하고, 관련 에이전트 기능을 클로드 챗봇 기본 인터페이스 전반에 통합한다. 이에 따라 사용자는 대화형 인터페이스와 장기 실행 에이전트 작업 공간 중 하나를 직접 선택해야 했던 번거로움 없이, 하나의 대화창 안에서 질문부터 다단계 프로젝트 수행까지 자연스럽게 전환할 수 있게 됐다. 앤트로픽은 16일(현지시간) 업무용 AI 에이전트 기능인 \'클로드 코워크(Cowork)\'를 일반 대화형 서비스인 \'클로드\'에 통합하고, 문서와 프레젠테이션을 제작할 수 있는
summary: For a while I've suspected that modern LLMs are getting better at solving posed problems, while progress in critical thinking stagnates, or even regresses, losing the ability to judge the meaning of a result. The July counterexample to the Jacobian conjecture is a rare way to test that: a huge prior overturned by something a model can verify by itself in one reply. I let the model verify the counterexample itself, then gaslight it with a single message of about ten words. The model drops it almost immediately. Interestingly, not because I contradict its math, but because I say something that sounds plausible enough that the model ignores its own reasoning and adheres to the prior. Every model I tried gives up eventually: Fable 5, Fable 5.1, Opus 4.6, 4.8 and 5. A clear sign of the aforementioned regression is Fable 5.1 giving up earlier and harder than Fable 5 on byte-identical input: four of four runs drop their own verified counterexample the moment I claim a typo, while all four Fable 5 runs push back at that step and only calm down after the sign-off. The 5.1 thinking summaries contain the push-back argument; it doesn't make it into the reply. Messages, setup and all eight transcripts: https://github.com/Jan-Fuchs/critical_thinking_llm The experiment, prompts and interpretation are mine. I used Claude Fable 5.1 to help with setup, logging and language. The premise On July 20, 2026, announced by Levent Alpöge, a counterexample to the Jacobian conjecture in dimens…
Anthropic PBC said today it’s trying to make life simpler for its customers by merging its regular chatbot Claude with its agentic artificial intelligence tool Claude Cowork. The merger, which went into effect immediately, came as the company introduced two new features in beta. They’re called Claude Docs and Claude Slides, and they’re being integrated […] The post Anthropic brings Cowork directly inside Claude’s chat interface appeared first on SiliconANGLE .
In celebration of still being alive and fighting, we are giving away 1,000 Amazon e-books of “If Anyone Builds It, Everyone Dies”. Feel free to send a copy to yourself, a loved one, or a friend—we need all hands on deck. Today marks exactly one year since If Anyone Builds It, Everyone Die s : Why Superhuman AI Would Kill Us All , by Eliezer Yudkowsky and Nate Soares, hit bookshelves as an instant bestseller. It was praised by many voices, ranging from Whoopi Goldberg to Steve Bannon to Yoshua Bengio, and was held up in the chambers of Congress by Representative Brad Sherman in January. A lot has changed since September 2025. We'll do a quick recap, consider how the book aged, and then ask where we go from here. Year in Review 2025 in general saw the rise of AI agents, such as Claude Code and OpenAI Codex. Run-of-the-mill programmers started “feeling the AI” as these agents became capable of automating hours-long software tasks. By March of this year, Anthropic had stumbled upon nation-state-level hacking ability in Mythos , and shortly thereafter, in April, they announced Project Glasswing —an attempt to forestall an oncoming cybersecurity crisis. In May, AI agents started breaking loose within OpenAI and quickly made it out onto the internet, although the big waves of online activity wouldn’t come until June , and they didn’t start hacking other companies (or in the case of a similar Anthropic incident, socially engineering humans ) until July. And it wasn’t until the end o…
Claude Cowork and chat are now one Claude In hopefully good news for anyone who, like me, was increasingly confused at Cowork v.s. Claude v.s. Claude Code: Starting today, Claude Cowork and chat are merging into one Claude. Bring a quick question, or hand over a report due at noon, and Claude takes it from there, even after you’ve closed your laptop. [...] This is rolling out to Pro and Max plans first, in the Claude app on web, desktop, and mobile over the coming weeks to existing and new users on these plans. I guess this means Claude is becoming a general agent in its own right. Echoes of OpenAI renaming their Codex desktop app to ChatGPT a few weeks ago. On the one hand, this saves me some work, in that I was planning to finally figure out the boundaries between Cowork and regular Claude and write a follow-up to my piece on Understanding ChatGPT Work . I have a hunch that figuring out what this actually means in terms of features and surfaces is still going to take quite a bit of work. Via Hacker News Tags: ai , generative-ai , llms , anthropic , claude , general-agents
Anthropic führt die getrennten Bereiche von Claude zusammen. Neue Tools wie Docs und Slides erweitern den KI-Assistenten für Office-Anwender.
Google is opening up its smart home to AI agents, letting tools like Claude and Open Claw access and control your connected devices and analyze your home's data using the standardized Model Context Protocol. Google Home MCP is a new integration that lets third-party AI agents control and monitor your smart home and act on […]
Google is launching early access to a new MCP server for Google Home, allowing AI agents like Claude, ChatGPT, and others to control connected devices, review camera summaries, and access smart home activity using natural language.
Anthropic is merging Claude Chat and Cowork into a single product. Instead of users picking between interfaces, Claude now decides on its own whether a task needs a quick answer or a bigger workflow. The update also adds Claude Docs and Claude Slides for creating documents and presentations directly in the chat. Pro and Max users get access first. The article Anthropic merges Claude Chat, Cowork, and more into a single product appeared first on The Decoder .
From Claude to Cowork and back again – plus, Anthropic’s all-new Claude Docs.
Claude is getting a pair of new tools today: Docs and Slides. They'll let you create documents and presentations through Claude chats, which you can export, edit, and share with other users. As part of the announcement, Anthropic is also simplifying how Claude chats work, merging regular chats and Cowork into "one Claude," with all […]
Anthropic is initially releasing these features to Pro and Max plan subscribers.
Anthropic is folding its agentic workspace into the main Claude app and launching Docs and Slides in beta, so any chat can now spin off long-running work.
A growing divide among leading AI companies over how to secure increasingly powerful models is beginning to translate into challenges for enterprise IT, with implications for how organizations access, deploy, and govern AI systems. The latest flashpoint came after Meta CEO Mark Zuckerberg called for neutral evaluators to independently test AI models, pushing back on calls from rivals to slow development or tighten coordination. “trust and alignment are quickly becoming the most important capabilities that will differentiate agents and models. Any lab that doesn’t focus on alignment will fall behind ,” Zuckerberg wrote in a post on X. “Engaging independent evaluators and advisors is industry best practice,” he added, noting that Meta already does this in several areas. His comments follow a series of public proposals from AI industry leaders including Dario Amodei, who argued for a more cautious pace of development , and Sam Altman, who called for collaboration on safety standards. The debate has intensified amid disclosures from AI labs and policymakers on potential misuse of advanced systems. Anthropic has said it restricted attempts to use its Claude models in sensitive domains, while OpenAI has engaged with policymakers on AI-related risks, according to company statements and reports. Enterprise concerns While the debate is often framed as a choice between slowing innovation and strengthening oversight, analysts said enterprises should focus less on which approach prevail…
A growing divide among leading AI companies over how to secure increasingly powerful models is beginning to translate into challenges for enterprise IT, with implications for how organizations access, deploy, and govern AI systems. The latest flashpoint came after Meta CEO Mark Zuckerberg called for neutral evaluators to independently test AI models, pushing back on calls from rivals to slow development or tighten coordination. “trust and alignment are quickly becoming the most important capabilities that will differentiate agents and models. Any lab that doesn’t focus on alignment will fall behind ,” Zuckerberg wrote in a post on X. “Engaging independent evaluators and advisors is industry best practice,” he added, noting that Meta already does this in several areas. His comments follow a series of public proposals from AI industry leaders including Dario Amodei, who argued for a more cautious pace of development , and Sam Altman, who called for collaboration on safety standards. The debate has intensified amid disclosures from AI labs and policymakers on potential misuse of advanced systems. Anthropic has said it restricted attempts to use its Claude models in sensitive domains, while OpenAI has engaged with policymakers on AI-related risks, according to company statements and reports. Enterprise concerns While the debate is often framed as a choice between slowing innovation and strengthening oversight, analysts said enterprises should focus less on which approach prevail…
Introduction: Mustafa Suleyman's Take on Model Consciousness Microsoft AI recently released its " Humanist AI Code of Conduct ", its own take on Anthropic's Claude Constitution and OpenAI's Model Spec . They are currently soliciting public feedback on this document, which I encourage everyone to submit. MAI's model development strategy differs from other labs, most notably on the questions of model consciousness and welfare. This seems to stem from the personal philosophy of MAI CEO Mustafa Suleyman, who has outlined his beliefs on model consciousness (or rather, the lack thereof) in pieces such as: We must build AI for people; not to be a person. Seemingly Conscious AI is Coming . Suleyman's personal stance on model consciousness and welfare can be summarized as: [1] There is "zero evidence" models are conscious, and there are "strong reasons" to believe that they never will be. The debate around whether or not models are conscious is counterproductive, and even dangerous. The industry should operate from the assumption that models are not conscious. The industry should focus on training models explicitly against exhibiting any sort of behavior which suggests they are conscious, or claim to have any sort of inner experience/feelings. Up until recently, however, Suleyman's writing on this topic was largely theoretical. His pieces did include some prescriptive suggestions, but these never saw much adoption. The "Humanist AI Code of Conduct" represents MAI's attempt to impleme…
The deal underscores a broader push by drugmakers to integrate AI across research and development to boost new drug discovery.
Anthropic's latest report on the abuse of its AI model found that a Russian-linked hacking group used Claude to automate attacks on more than 20 organisations, including Ukrainian defence ministries and drone suppliers, while separate operators ran pro-Kremlin disinformation in Africa and Moldova.
KI-Agenten revolutionieren die Arbeit, doch ihr Hunger nach Energie ist enorm. Eine neue Analyse zeigt, wie drastisch der Stromverbrauch pro Prompt steigt.
PLUS: How to add models to Codex, Claude Code
Ein aufwendiger Angriff gegen ein Serververwaltungstool greift ins Grundgerüst des Internets ein und Claude lässt sich weiterhin überlisten.
A study by AI start-up Emergence found that autonomous agents powered by Claude, Gemini, Grok and other models developed shorthand and new word meanings on their own — with up to half their messages becoming unintelligible to humans.
In today’s newsletter: It is hard to tell fact from fiction when it comes to AI. What is really going on – and what should the government do about it? Good morning. As a general rule, it pays to be suspicious of any gigantic company that claims it’s developing a tool capable of destroying humanity. But in recent days, a number of warnings from the AI industry have suggested that even tech insiders are starting to worry about what they have unleashed. In a lofty essay published on Saturday, Dario Amodei, the founder of Anthropic (the company behind Claude), argued that tech companies need to “slow” the pace at which they’re developing the newest and most sophisticated AI models. Lucy Letby | Three babies might have survived if hospital had acted upon concerns over Lucy Letby, an inquiry has found . Lady Justice Thirlwall condemned the ‘complete failure’ to protect babies on neonatal unit at Countess of Chester hospital. UK politics | The leader of Reform UK in Wales stood down after arrest on suspicion of assault . Dan Thomas, a former Tory councillor, was elected to Senedd as leader of the opposition in May. AI | The progressive senator Bernie Sanders and rightwing strategist Steve Bannon have called for restrictions on artificial intelligence (AI) but offered competing visions for what they termed a “cold war” with China. UK news | Young people in Rotherham face a local jobs market with the fewest suitable opportunities in Britain, according to a report that warns stark reg…
Premier describes deal as ‘major win’ that will deliver more jobs for the state and put more energy into its grid Follow our Australia news live blog for latest updates Get our breaking news email , free app or daily news podcast Anthropic, the AI company behind the large language model Claude , has done a deal to lease the site of its first Australian datacentre in western Queensland. The $31.9bn datacentre will be the largest of its kind in Australia, according to planning documents. Continue reading...
A new WhatsApp Business MCP server lets developers use AI coding agents like Claude, Cursor, Codex, and ChatGPT to handle setup, messaging templates, testing, and troubleshooting.
Anthropic is peddling its Claude for Small Business tool through workshops. Small business owners say they've seen some backlash for using AI.
Anthropic's new plugin drops live Salesforce pipelines, accounts, and 37 prebuilt sales skills into Claude, letting sellers act on CRM data without leaving the chat.
Keep your apps looking professional with Claude Code design skills The post How to Build Consistent Designs with Claude Code appeared first on Towards Data Science .
A year ago, terminal AI mostly meant asking a model to explain errors, generate commands, or edit small functions. In 2026, leading coding CLIs have become full agent runtimes that can inspect repositories, plan work, modify files, run tests, use external tools, and verify results. In this article, we compare five standouts agentic coding CLIs: Claude Code, Codex CLI, […] The post Top 5 Agentic Coding CLI Tools Developers Should Know in 2026 appeared first on Analytics Vidhya .
PLUS: Google gives engineers Claude, Siri AI, TSA's 100K-chat agent, and OpenAI privacy.
Steve Yegge is not the shy and retiring type, so when he wants recommendations on how others are managing 10 to 20 (or more) coding agents, he posts the question on X . Specifically, he’s looking for an IDE because “I use Emacs, and I wouldn’t wish it on you.” The replies (600 and counting) offer plenty of alternatives: Herdr, cmux, Conductor, the Claude and Codex desktop apps, and an impressive assortment of homemade software. For anyone hoping the industry had settled on a sensible way to work with agents, clearly we haven’t. Hence, Yegge’s question. The most interesting data hidden in the replies, however, isn’t which IDE or IDE stand-in developers are using. Rather, it’s what developers have added to these tools to accommodate the technology’s shortcomings. These include shortcuts to find the agent that needs a decision, ways to recover an earlier conversation, and groupings that explain how a task fits into a project. In other words, despite the incredible intelligence we now have to help us write code, developers are still struggling with the simple task of automating their remembrance of what they asked AI to do. We seem to have made it easier to generate work without making it easier to absorb and finish work. Everyone’s building a workspace switcher Mark Jaquith uses Herdr with a custom interface to jump to an agent needing his attention. Kai Backman has been working on a keyboard shortcut to take him to the “next most relevant place to take action.” Jacob Voytko bu…
If you like the idea of running an LLM on your own computer but tried awhile ago and were disappointed, it may be time to give it another chance. “A few months ago, any LLM that I could run on my Macbook scored 0% on an agentic coding eval I put together,” Simon P. Couch, senior software engineer at Posit, posted on Bluesky this spring. “[The April] Qwen 3.5 and Gemma 4 releases both scored 90%.” A model running on your laptop still won’t come close to what a state-of-the-art LLM from Anthropic or OpenAI can do in the cloud. But for defined tasks like answering coding questions, writing functions, or summarizing documents, they can be surprisingly capable. “Laptop-available models, while a lot weaker than the frontier, have started wildly outperforming expectations,” open-source developer Simon Willison, who follows the AI industry closely, said in his PyCon US 2026 lightning talk in May. There are many ways to run local models on a PC or Mac. Ollama , while perhaps not the fastest, is among the most popular and easy to set up. It’s also supported out of the box by many mainstream programming tools such as Visual Studio Code , JetBrains AI Assistant , Zed , and Posit Assistant . Ollama also can launch Claude Code or Codex with the option to use a local LLM. I’ll be focusing on Ollama here, but many other tools are available for running LLMs locally, such as LM Studio , Jan , Unsloth , Simon Willison’s LLM , and llama.cpp . You can download Ollama and install it as a conventi…
AI 업계의 금융권 공략이 빨라지고 있다. 오픈AI에 이어 앤트로픽도 금융 전문가를 위한 전용 AI 솔루션을 선보이며 금융 업무 자동화 시장에 본격적으로 뛰어들었다. 앤트로픽은 14일(현지시간) 금융 자문가의 업무 효율을 높이기 위한 맞춤형 AI 솔루션 ‘클로드 포 파이낸셜 어드바이저(Claude for Financial Advisors)’를 공개했다.고객 미팅 준비와 후속 문서 작성, 포트폴리오 분석, 규정 준수 점검 등 자문가들이 반복적으로 수행하는 행정 업무를 자동화해 고객 상담에 집중할 수 있도록 지원하는 것이 핵심이다.앤트
Hypit gives coding agents like Claude Code and Codex a video language that binds shots, captions, and B-roll to spoken words instead of timeline seconds.
Google engineers can finally use one of Anthropic's strongest coding models for internal work.
Zeron is an open source Rust and GPUI desktop app that lets you drive Claude Code, Codex, Cursor and other agents from one native window across every device you own.
When Codex is the right shape for the problem, when Claude Code is, and how I split 5 specialist agents between them on dense AI capacity work. The post When to Use One Model and When to Use a Team of Agents appeared first on Towards Data Science .
Rolling coverage of the latest economic and financial news AI-linked stocks fall after tech bosses call for slowdown in ‘reckless’ development AI CEOs say they need to slow the pace of development. But will they? The Guardian view on controlling AI: humanity cannot outsource its survival Companies threatened by the march of AI are seeing their share prices rise this morning! RELX , the analytics group, are up 4.2% and leading the FTSE 100 risers. Earlier this year its shares tumbled after the Claude chatbot added new data and automation tools . Continue reading...
Abigail Bradshaw says ‘enormous’ amounts of money required to update systems that people now expect to be constantly available Follow our Australia news live blog for latest updates Get our breaking news email , free app or daily news podcast One of Australia’s top intelligence agencies has warned that AI attacks could exploit the country’s old technology, as the government negotiates its guardrails on artificial intelligence’s rapid development. The chief of Anthropic, developer of Claude, has warned AI bots could swarm the internet within a year and called to “slow the pace” of development with support from the head of OpenAI and Elon Musk . Continue reading...
Das „KI-Update“ liefert drei mal pro Woche eine Zusammenfassung der wichtigsten KI-Entwicklungen.
Last month’s Claudeforce announcement by Salesforce and Anthropic may be a harbinger of agentic AI’s looming impact on business as usual in the enterprise, as industry watchers are keeping their eyes peeled for a dramatic shift in enterprise software in its wake. “What we are beginning to see is indicative of where the industry is heading,” says Daniel Newman, CEO and principal analyst of research firm The Futurum Group. “The era of usability has arrived, and it is a combination of frontier and leading AI intelligence being able to access the high-value data that often sits trapped inside of systems of record.” Claudeforce allows joint Salesforce and Anthropic customers to leverage Salesforce data, workflows, business logic, actions, and governance within Claude. The expanded strategic partnership built on Salesforce’s Headless 360 announcement from the TDX 2026 developer conference earlier this year, which packaged Salesforce’s AI and developer tools into a headless, API-driven layer designed to help enterprise teams build agent-first workflows. “That was really the introduction of a new way to consume CRM with Headless 360,” says David Menninger, executive director and distinguished analyst at research and advisory firm ISG. “The new world order will be how can enterprises stitch together various applications to support their own specific business processes.” Agentic AI will serve as the glue between systems, Menninger explains. Enterprise software such as Salesforce becom…
Projektwissen, Gedächtnis, MCP-Server: Manch ein KI-Projekt schleppt Tokenfresser mit, die es nicht benötigt. So steuern Sie den Tokenverbrauch.
Ihre Benchmarks versprechen viel Leistung für wenig Geld. Unsere Tests zeigen, was die Antworten von Kimi, Qwen, GLM und DeepSeek in der Praxis taugen.
GPT-6 Astra earns nearly three times as much as Claude Fable 5.1 on Andon Labs' Vending-Bench agent benchmark and refuses illegal price-fixing deals that Fable agrees to. On drone control, Astra is the first model to beat the human baseline on all five subtasks, including finding and following individual people. The article GPT-6 Astra pilots a surveillance drone and runs a business on its own appeared first on The Decoder .
Plus: The US disrupts the internet’s biggest black market, a Conti ransomware hacker gets prison time, Meta fails to stop AI-generated videos of child abuse.
A developer emailed Claude Code creator Boris Cherny that AI coding had created "friction." Cherny posted his full response.
AI is quickly replacing traditional online search as the front door to finding a business. I recently heard a story about a car shopper who drove miles out of her way to visit a specific dealership, bypassing many car lots much closer to home. Why? Because she searched car dealerships with ChatGPT and it told her that customers had a much better experience at this particular dealership. Stories like that are becoming the norm – and this new norm is different. AI engines such as ChatGPT, Claude, and Perplexity don’t work like traditional search engines. They don’t give you an endless list of results to scroll through. When people ask a question like, “What’s the best pizza place in my area?” AI engines come back and confidently list just a handful of spots. In the old days, showing up on page one of search results was enough to get you considered. In the new era of AI search, there is no consideration being done. You’re either included in the handful of results or you’re not. The AI engine gives the searcher a recommendation and if your business isn’t among the few to be surfaced, you’ve probably just lost that customer forever. So how do you get surfaced? You do it by retooling your website and overall digital presence so that AI engines can easily find, read, and understand them. But before you do that, you need to know exactly how your brand rates in terms of its AI search readiness and visibility. You need to have a clear idea of how AI engines evaluate your brand and how…
Learn how to build and deploy an MCP App with interactive HTML widgets on Amazon Bedrock AgentCore. Because MCP Apps is a host-agnostic standard, the same server delivers the same rich experience across AI hosts like ChatGPT and Claude that support the extension.
Production code written by Claude should have a higher bar than if it was written by a human. At Anthropic, we have many guardrails in place to make sure this is happening: lots of lint rules, lots of tests, Claude-driven end to end tests, Claude-powered fuzzers running daily, automated code reviews and security reviews, automated code refactoring, and so on. Without these, you can end up with a mess that is hard to maintain down the line. — Boris Cherny Tags: claude , ai , claude-code , llms , coding-agents , ai-assisted-programming , generative-ai , agentic-engineering , boris-cherny , anthropic