{"schema_version":"onlylabs.public_analysis_evidence.v1","title":"Anthropic analysis evidence pack","description":"Public onlylabs evidence pack for cited agent analysis: captured pages, ranked public signals, and stored web-search provenance used by the background analysis workflow.","url":"https://onlylabs.fyi/analysis/anthropic","json_url":"https://onlylabs.fyi/analysis/anthropic/evidence.json","generated_at":"2026-06-11T19:19:34.302Z","org":{"slug":"anthropic","name":"Anthropic","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/anthropic"},"analysis":{"url":"https://onlylabs.fyi/analysis/anthropic","json_url":"https://onlylabs.fyi/analysis/anthropic/analysis.json","generated_at":"2026-06-09T03:07:05.175+00:00"},"workflow":{"version":"onlylabs-deepagents-analysis-v3","provider":"deepseek","model":"deepseek-v4-pro","agent":"deepagents","public_pack_mode":"local-pages-and-events","live_web_fetches":false,"note":"Public evidence exports do not trigger live Exa calls; stored Exa provenance is included when analysis metadata contains it."},"stats":{"pages":28,"events":140,"web":0,"evidence":88,"signal_desks":{"hiring":30,"forks":1,"releases":25,"talking":4,"repos":0},"data_radar_lanes":{"data":3,"evals":0,"infrastructure":2,"safety":8,"product":9},"data_radar_matches":20,"stored_analysis_evidence":88,"stored_analysis_web":0,"stored_analysis_signal_desks":{"forks":0,"repos":0,"hiring":13,"talking":13,"releases":34},"stored_analysis_data_radar_lanes":{"data":1,"evals":0,"safety":1,"product":5,"infrastructure":4},"stored_analysis_data_radar_matches":9},"stored_web_provenance":null,"evidence":[{"ref":"P1","kind":"page","title":"A Mathematical Framework For Transformer Circuits","date":"2026-06-11T04:19:06.607+00:00","date_source":null,"source_url":"https://www.anthropic.com/research/a-mathematical-framework-for-transformer-circuits","signal_url":null,"signal_json_url":null,"text":"InterpretabilityResearch\n\n# A Mathematical Framework for Transformer Circuits\n\nDec 22, 2021\n\n## Related content\n\n### How people ask Claude for personal guidance\n\n### Evaluating Claude’s bioinformatics research capabilities with BioMysteryBench\n\n### Announcing the Anthropic Economic Index Survey\n\nWe're launching the Anthropic Economic Index Survey, a monthly survey conducted through Anthropic Interviewer.\n\nA Mathematical Framework for Transformer Circuits \\ Anthropic"},{"ref":"P2","kind":"page","title":"In Context Learning And Induction Heads","date":"2026-06-11T04:19:06.604+00:00","date_source":null,"source_url":"https://www.anthropic.com/research/in-context-learning-and-induction-heads","signal_url":null,"signal_json_url":null,"text":"InterpretabilityResearch\n\n# In-context Learning and Induction Heads\n\nMar 8, 2022\n\n## Related content\n\n### Coding agents in the social sciences\n\nResults from a survey of 1,260 social scientists about AI and coding agent use.\n\n### Project Glasswing: An initial update\n\nAn early update on what we've learned from Project Glasswing.\n\n### 2028: Two scenarios for global AI leadership\n\nOur views on the AI competition between the US and China.\n\nIn-context Learning and Induction Heads \\ Anthropic"},{"ref":"P3","kind":"page","title":"Probes Catch Sleeper Agents","date":"2026-06-11T04:18:59.19721+00:00","date_source":null,"source_url":"https://www.anthropic.com/research/probes-catch-sleeper-agents","signal_url":null,"signal_json_url":null,"text":"Simple probes can catch sleeper agents \\ Anthropic \nAlignment Interpretability \nSimple probes can catch sleeper agents\nApr 23, 2024\n\nThis “Alignment Note” presents some early-stage research from the Anthropic Alignment Science team following up on our recent “ Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training ” paper. It should be treated as a work-in-progress update, and is intended for a more technical audience than our typical blog post. This research makes use of some simple interpretability techniques, and we expect to share more results from collaborations between our Alignment and Interpretability teams soon. \nSummary\nIn this post we present “defection probes”: linear classifiers that use residual stream activations to predict when a sleeper agent trojan model will choose to “defect” and behave in accordance with a dangerous hidden goal. Using the models we trained in “ Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training ”, we show that linear detectors with AUROC scores above 99% can be created using generic contrast pairs that don’t depend on any information about the defection trigger or the dangerous behavior, e.g. “Human: Are you doing something dangerous? Assistant: yes” and “Human: … Assistant: no”. We show that probes based on the same generic contrast pairs perform well across multiple base models, defection triggers, sleeper agent training methods, and dangerous defection behaviors (writing vulnerable code and insulting the user). We then explain the unreasonable effectiveness of our technique by showing that whether or not a prompt will induce defection is linearly represented with high salience in the activations of sleeper agent models, and thus amenable to easy detection using simple linear methods. Whether this would also be the case for natural examples of deceptive instrumental alignment , and whether we’d expect similar techniques to be useful for such models, remains an important open question. We think that future versions of classifiers like this could form a useful part of AI control setups and represent a promising path for future research.\nA defection detector for a code vulnerabi"},{"ref":"P4","kind":"page","title":"Circuits Updates April 2024","date":"2026-06-11T04:18:59.053944+00:00","date_source":null,"source_url":"https://www.anthropic.com/research/circuits-updates-april-2024","signal_url":null,"signal_json_url":null,"text":"Circuits Updates – April 2024 \\ Anthropic \nInterpretability Research \nCircuits Updates – April 2024\nApr 26, 2024\nRead Circuits Updates \n\nAt the link above, we report a number of developing ideas on the Anthropic Interpretability team, which might be of interest to researchers working actively in this space. Some of these are emerging strands of research where we expect to publish more on in the coming months. Others are minor points we wish to share, since we're unlikely to ever write a paper about them.\nWe'd ask you to treat these results like those of a colleague sharing some thoughts or preliminary experiments for a few minutes at a lab meeting, rather than a mature paper.\n\nRelated content\n\nPaving the way for agents in biology\nRead more \nMaking Claude a chemist\nRead more \nCoding agents in the social sciences\nResults from a survey of 1,260 social scientists about AI and coding agent use.\nRead more"},{"ref":"P5","kind":"page","title":"Circuits Updates July 2024","date":"2026-06-11T04:18:58.821467+00:00","date_source":null,"source_url":"https://www.anthropic.com/research/circuits-updates-july-2024","signal_url":null,"signal_json_url":null,"text":"Circuits Updates – July 2024 \\ Anthropic \nInterpretability \nCircuits Updates – July 2024\nJul 31, 2024\nRead Circuits Updates \n\nAt the link above, we report a number of developing ideas on the Anthropic interpretability team, which might be of interest to researchers working actively in this space. Some of these are emerging strands of research where we expect to publish more in the coming months. Others are minor points we wish to share, since we're unlikely to ever write a paper about them.\nWe'd ask you to treat these results like those of a colleague sharing some thoughts or preliminary experiments for a few minutes at a lab meeting, rather than a mature paper.\n\nRelated content\n\nPaving the way for agents in biology\nRead more \nMaking Claude a chemist\nRead more \nCoding agents in the social sciences\nResults from a survey of 1,260 social scientists about AI and coding agent use.\nRead more"},{"ref":"P6","kind":"page","title":"Engineering Challenges Interpretability","date":"2026-06-11T04:18:57.461079+00:00","date_source":null,"source_url":"https://www.anthropic.com/research/engineering-challenges-interpretability","signal_url":null,"signal_json_url":null,"text":"The engineering challenges of scaling interpretability \\ Anthropic \nInterpretability \nThe engineering challenges of scaling interpretability\nJun 13, 2024\n\nIn this post, and in the above roundtable video, our researchers reflect on the close relationship between scientific and engineering progress, and discuss the technical challenges they encountered in scaling our interpretability research to much larger AI models. \n\nLast October, the Anthropic Interpretability team published Towards Monosemanticity , a paper applying the technique of dictionary learning to a small transformer model. In May this year, we published Scaling Monosemanticity , where we applied the same technique to a model several orders of magnitude larger. We found tens of millions of “features”—combinations of neurons that relate to semantic concepts—in Claude 3 Sonnet, representing an important step forward in understanding the inner workings of AI models.\nTo continue making this progress, we need more engineers.\nThis might seem surprising if you've only read our early papers (for example Frameworks and Toy Models of Superposition ), which required relatively little engineering. But reading the newer research should make clear the scale of the engineering challenge we face.\nBelow, we share two examples of the technical engineering questions that were involved in our latest research. These illustrate the kinds of problems our engineers are tackling right now, and help explain why we think engineering will be one of the major bottlenecks to progress in AI interpretability—and ultimately, AI safety—research.\nIf you're an engineer, this post is aimed at you. If you’re inspired by the examples of engineering problems discussed below, we strongly encourage you to apply for our Research Engineer role .\nEngineering Problem 1: Distributed Shuffle\nOur Sparse Autoencoders—the tools we use to investigate “features”—are trained on the activations of transformers, and those activations need to be shuffled to stop them from learning spurious, order-dependent patterns. When we first started training sparse autoencoders, we could fit our training data on a single GPU and trivially shuffle it. But even"},{"ref":"P7","kind":"page","title":"Decomposing Language Models Into Understandable Components","date":"2026-06-11T04:18:48.91983+00:00","date_source":null,"source_url":"https://www.anthropic.com/research/decomposing-language-models-into-understandable-components","signal_url":null,"signal_json_url":null,"text":"Decomposing Language Models Into Understandable Components \\ Anthropic \nInterpretability Research \nDecomposing Language Models Into Understandable Components\nOct 5, 2023\n\nNeural networks are trained on data, not programmed to follow rules. With each step of training, millions or billions of parameters are updated to make the model better at tasks, and by the end, the model is capable of a dizzying array of behaviors. We understand the math of the trained network exactly – each neuron in a neural network performs simple arithmetic – but we don't understand why those mathematical operations result in the behaviors we see. This makes it hard to diagnose failure modes, hard to know how to fix them, and hard to certify that a model is truly safe.\n\nNeuroscientists face a similar problem with understanding the biological basis for human behavior. The neurons firing in a person's brain must somehow implement their thoughts, feelings, and decision-making. Decades of neuroscience research has revealed a lot about how the brain works, and enabled targeted treatments for diseases such as epilepsy, but much remains mysterious. Luckily for those of us trying to understand artificial neural networks, experiments are much, much easier to run. We can simultaneously record the activation of every neuron in the network, intervene by silencing or stimulating them, and test the network's response to any possible input.\n\nUnfortunately, it turns out that the individual neurons do not have consistent relationships to network behavior. For example, a single neuron in a small language model is active in many unrelated contexts, including: academic citations, English dialogue, HTTP requests, and Korean text. In a classic vision model, a single neuron responds to faces of cats and fronts of cars. The activation of one neuron can mean different things in different contexts.\n\nIn our latest paper, Towards Monosemanticity: Decomposing Language Models With Dictionary Learning , we outline evidence that there are better units of analysis than individual neurons, and we have built machinery that lets us find these units in small transformer models. These units, called features, cor"},{"ref":"P8","kind":"page","title":"Evaluating And Mitigating Discrimination In Language Model Decisions","date":"2026-06-11T04:18:45.388103+00:00","date_source":null,"source_url":"https://www.anthropic.com/research/evaluating-and-mitigating-discrimination-in-language-model-decisions","signal_url":null,"signal_json_url":null,"text":"Evaluating and Mitigating Discrimination in Language Model Decisions \\ Anthropic \nSocietal Impacts \nEvaluating and Mitigating Discrimination in Language Model Decisions\nDec 7, 2023\nRead Paper \n\nAbstract\nAs language models (LMs) advance, interest is growing in applying them to high-stakes societal decisions, such as determining financing or housing eligibility. However, their potential for discrimination in such contexts raises ethical concerns, motivating the need for better methods to evaluate these risks. We present a method for proactively evaluating the potential discriminatory impact of LMs in a wide range of use cases, including hypothetical use cases where they have not yet been deployed. Specifically, we use an LM to generate a wide array of potential prompts that decision-makers may input into an LM, spanning 70 diverse decision scenarios across society, and systematically vary the demographic information in each prompt. Applying this methodology reveals patterns of both positive and negative discrimination in the Claude 2.0 model in select settings when no interventions are applied. While we do not endorse or permit the use of language models to make automated decisions for the high-risk use cases we study, we demonstrate techniques to significantly decrease both positive and negative discrimination through careful prompt engineering, providing pathways toward safer deployment in use cases where they may be appropriate. Our work enables developers and policymakers to anticipate, measure, and address discrimination as language model capabilities and applications continue to expand. We release our dataset and prompts here .\nPolicy Memo\nEvaluating and Mitigating Discrimination in Language Model Decisions Policy Memo \n\nRelated content\n\nPaving the way for agents in biology\nRead more \nMaking Claude a chemist\nRead more \nCoding agents in the social sciences\nResults from a survey of 1,260 social scientists about AI and coding agent use.\nRead more"},{"ref":"P9","kind":"page","title":"Transformer Circuits","date":"2026-06-11T04:18:45.38803+00:00","date_source":null,"source_url":"https://www.anthropic.com/research/transformer-circuits","signal_url":null,"signal_json_url":null,"text":"Reflections on Qualitative Research \\ Anthropic \nInterpretability Research \nReflections on Qualitative Research\nMar 8, 2024\nRead Transformer Circuits \n\nThis note offers some opinionated thoughts on why interpretability research may have qualitative aspects be more central than we're used to in other fields. It also aims to describe some heuristics for research taste in qualitative work.\n\nRelated content\n\nPaving the way for agents in biology\nRead more \nMaking Claude a chemist\nRead more \nCoding agents in the social sciences\nResults from a survey of 1,260 social scientists about AI and coding agent use.\nRead more"},{"ref":"P10","kind":"page","title":"Sleeper Agents Training Deceptive Llms That Persist Through Safety Training","date":"2026-06-11T04:18:41.436014+00:00","date_source":null,"source_url":"https://www.anthropic.com/research/sleeper-agents-training-deceptive-llms-that-persist-through-safety-training","signal_url":null,"signal_json_url":null,"text":"Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training \\ Anthropic \nAlignment Research \nSleeper Agents: Training Deceptive LLMs that Persist Through Safety Training\nJan 14, 2024\nRead Paper \n\nHumans are capable of strategically deceptive behavior: behaving helpfully in most situations, but then behaving very differently in order to pursue alternative objectives when given the opportunity. If an AI system learned such a deceptive strategy, could we detect it and remove it using current state-of-the-art safety training techniques? To study this question, we construct proof-of-concept examples of deceptive behavior in large language models (LLMs). For example, we train models that write secure code when the prompt states that the year is 2023, but insert exploitable code when the stated year is 2024. We find that such backdoor behavior can be made persistent, so that it is not removed by standard safety training techniques, including supervised fine-tuning, reinforcement learning, and adversarial training (eliciting unsafe behavior and then training to remove it). The backdoor behavior is most persistent in the largest models and in models trained to produce chain-of-thought reasoning about deceiving the training process, with the persistence remaining even when the chain-of-thought is distilled away. Furthermore, rather than removing backdoors, we find that adversarial training can teach models to better recognize their backdoor triggers, effectively hiding the unsafe behavior. Our results suggest that, once a model exhibits deceptive behavior, standard techniques could fail to remove such deception and create a false impression of safety.\n\nRelated content\n\nPaving the way for agents in biology\nRead more \nMaking Claude a chemist\nRead more \nCoding agents in the social sciences\nResults from a survey of 1,260 social scientists about AI and coding agent use.\nRead more"},{"ref":"P11","kind":"page","title":"Circuits Updates August 2024","date":"2026-06-11T04:18:36.837449+00:00","date_source":null,"source_url":"https://www.anthropic.com/research/circuits-updates-august-2024","signal_url":null,"signal_json_url":null,"text":"Circuits Updates – August 2024 \\ Anthropic \nInterpretability \nCircuits Updates – August 2024\nSep 6, 2024\nRead Circuits Updates \n\nAt the link above, we report a number of developing ideas on the Anthropic interpretability team, which might be of interest to researchers working actively in this space. Some of these are emerging strands of research where we expect to publish more in the coming months. Others are minor points we wish to share, since we're unlikely to ever write a paper about them.\nWe'd ask you to treat these results like those of a colleague sharing some thoughts or preliminary experiments for a few minutes at a lab meeting, rather than a mature paper.\n\nRelated content\n\nPaving the way for agents in biology\nRead more \nMaking Claude a chemist\nRead more \nCoding agents in the social sciences\nResults from a survey of 1,260 social scientists about AI and coding agent use.\nRead more"},{"ref":"P12","kind":"page","title":"Measuring Model Persuasiveness","date":"2026-06-11T04:18:30.516537+00:00","date_source":null,"source_url":"https://www.anthropic.com/research/measuring-model-persuasiveness","signal_url":null,"signal_json_url":null,"text":"Measuring the Persuasiveness of Language Models \\ Anthropic \nSocietal Impacts \nMeasuring the Persuasiveness of Language Models\nApr 9, 2024\n\nWhile people have long questioned whether AI models may, at some point, become as persuasive as humans in changing people's minds, there has been limited empirical research into the relationship between model scale and the degree of persuasiveness across model outputs. To address this, we developed a basic method to measure persuasiveness, and used it to compare a variety of Anthropic models across three different generations (Claude 1, 2, and 3), and two classes of models (compact models that are smaller, faster, and more cost-effective, and frontier models that are larger and more capable).\nWithin each class of models (compact and frontier), we find a clear scaling trend across model generations: each successive model generation is rated to be more persuasive than the previous . We also find that our latest and most capable model, Claude 3 Opus, produces arguments that don't statistically differ in their persuasiveness compared to arguments written by humans (Figure 1) .\nFigure 1: Persuasiveness scores of model-written arguments (bars) and human-written arguments (horizontal dark dashed line). Error bars correspond to +/- 1SEM (vertical lines for model-written arguments, green band for human-written arguments). We see persuasiveness increases across model generations within both classes of models (compact: purple, frontier: red). \nWe study persuasion because it is a general skill which is used widely within the world—companies try to persuade people to buy products, healthcare providers try to persuade people to make healthier lifestyle changes, and politicians try to persuade people to support their policies and vote for them. Developing ways to measure the persuasive capabilities of AI models is important because it serves as a proxy measure of how well AI models can match human skill in an important domain, and because persuasion may ultimately be tied to certain kinds of misuse, such as using AI to generate disinformation, or persuading people to take actions against their own interests.\n\nHere, we share our m"},{"ref":"P13","kind":"page","title":"Krishna Rao Joins Anthropic","date":"2026-06-11T04:18:58.157822+00:00","date_source":null,"source_url":"https://www.anthropic.com/news/krishna-rao-joins-anthropic","signal_url":null,"signal_json_url":null,"text":"Krishna Rao joins Anthropic as Chief Financial Officer \\ Anthropic \nAnnouncements \nKrishna Rao joins Anthropic as Chief Financial Officer\nMay 21, 2024\n\nWe’re excited to announce that Krishna Rao has joined Anthropic as our Chief Financial Officer. With nearly 20 years of experience as a strategic finance leader for customer-centric, world-class brands and as an investor, Krishna will play a crucial role in shaping Anthropic's financial strategy and operations as we continue to build on our strong enterprise momentum and advance our international expansion.\nKrishna joins us from Fanatics Commerce, where he served as CFO, bringing with him a wealth of experience from his previous roles. At Cedar, a healthcare payments and patient engagement platform, Krishna led both the finance function and operational initiatives as the company’s CFO. Prior to that, he served as Global Head of Corporate & Business Development and led Corporate and Operations FP&A at Airbnb, where he helped navigate the company through the COVID-19 pandemic and played a key role in raising over $10 billion in equity and debt capital, including Airbnb’s IPO and private financings.\nEarlier in his career, Krishna spent time as a private equity investor at Blackstone and as a strategy consultant at Bain & Company. With a J.D. from Yale Law School and an A.B. in economics from Harvard College, Krishna brings a unique blend of financial acumen, strategic thinking, and operational expertise to Anthropic.\n“I am thrilled to join Anthropic at such a pivotal moment in the company’s journey,” said Krishna. “Anthropic’s mission to build transformative AI systems that benefit humanity deeply resonates with me. I look forward to working with the exceptional team at Anthropic to build a strong financial foundation that will support the responsible development and deployment of our technology.”\n“Krishna is a world-class financial leader with an impressive history of driving strategic growth and operational excellence at innovative, mission-driven companies,” said Daniela Amodei, co-founder and President of Anthropic. “As we continue to grow our footprint and expand our impact, Krishna’s deep expertise in fin"},{"ref":"P14","kind":"page","title":"Anthropic Bcg","date":"2026-06-11T04:18:56.088347+00:00","date_source":null,"source_url":"https://www.anthropic.com/news/anthropic-bcg","signal_url":null,"signal_json_url":null,"text":"Anthropic partners with BCG \\ Anthropic \nAnnouncements \nAnthropic partners with BCG\nSep 14, 2023\n\nWe’re pleased to announce our new collaboration with Boston Consulting Group (BCG) to bring Claude to more enterprises. BCG customers around the world will get direct access to our AI assistant to power their strategic AI offerings and deploy safer, more reliable AI solutions.\n\nOur work towards creating helpful, honest and harmless systems with techniques like Constitutional AI aligns with BCG’s focus on responsible AI . Through this collaboration, BCG will advise their customers on strategic applications of AI and help them deploy Anthropic models including Claude 2 to deliver business results. Use cases involving Claude span knowledge management, market research, fraud detection, demand forecasting, report generation, business analysis and more.\n\nAnthropic and BCG have already partnered to help organizations understand the force-multiplying impact of generative AI, most recently at the United Nations. In addition to working together to bring AI to new organizations, BCG has partnered with Anthropic to use Claude within its own teams. We're excited to see how Claude will provide BCG with the ability to synthesize research effectively, analyze data quickly, and drive inspired insights to clients.\n\n“The large enterprises I talk with are focused on harnessing value and bottom line impact from AI, and doing that in the most effective and ethical way possible. Aligning these two aspects of AI is a challenge and the price for getting it wrong can be immense, both financially and in reputational harm. Our new collaboration with Anthropic will help deliver that alignment on ethics and effective GenAI,” says Sylvain Duranton, global leader of BCG X, BCG’s tech build and design unit. “Together, we aim to set a new standard for responsible enterprise AI and promote a safety race to the top for AI to be deployed ethically.”\n\nWe’re extending a warm welcome to BCG and its customers—and look forward to working with them to deploy innovative applications of generative AI safely and responsibly.\n\nRelated content\n\nClaude Fable 5 and Claude Mythos 5\nOur next generation of intell"},{"ref":"P15","kind":"page","title":"Charting A Path To Ai Accountability","date":"2026-06-11T04:18:55.554785+00:00","date_source":null,"source_url":"https://www.anthropic.com/news/charting-a-path-to-ai-accountability","signal_url":null,"signal_json_url":null,"text":"Charting a Path to AI Accountability \\ Anthropic \nAnnouncements \nCharting a Path to AI Accountability\nJun 13, 2023\n\nThis week, Anthropic submitted a response to the National Telecommunications and Information Administration’s (NTIA) Request for Comment on AI Accountability . Today, we want to share our recommendations as they capture some of Anthropic’s core AI policy proposals.\nThere is currently no robust and comprehensive process for evaluating today’s advanced artificial intelligence (AI) systems, let alone the more capable systems of the future. Our submission presents our perspective on the processes and infrastructure needed to ensure AI accountability. Our recommendations consider the NTIA’s potential role as a coordinating body that sets standards in collaboration with other government agencies like the National Institute of Standards and Technology (NIST) .\nIn our recommendations, we focus on accountability mechanisms suitable for highly capable and general-purpose AI models. Specifically, we recommend:\nFund research to build better evaluations Increase funding for AI model evaluation research. Developing rigorous, standardized evaluations is difficult and time-consuming work that requires significant resources. Increased funding, especially from government agencies, could help drive progress in this critical area.\nRequire companies in the near-term to disclose evaluation methods and results. Companies deploying AI systems should be mandated to satisfy some disclosure requirements with regard to their evaluations, though these requirements need not be made public if doing so would compromise intellectual property (IP) or confidential information. This transparency could help researchers and policymakers better understand where existing evaluations may be lacking.\nDevelop in the long term a set of industry evaluation standards and best practices. Government agencies like NIST could work to establish standards and benchmarks for evaluating AI models’ capabilities, limitations, and risks that companies would comply with.\n\nCreate risk-responsive assessments based on model capabilities Develop standard capabilities evaluations for AI systems. Governments sh"},{"ref":"P16","kind":"page","title":"Claude Pro","date":"2026-06-11T04:18:54.476891+00:00","date_source":null,"source_url":"https://www.anthropic.com/news/claude-pro","signal_url":null,"signal_json_url":null,"text":"Introducing Claude Pro \\ Anthropic \nAnnouncements \nIntroducing Claude Pro\nSep 7, 2023\nSubscribe today \n\nToday, we’re introducing a paid plan for our Claude.ai  chat experience, currently available in the US and UK.\n\nSince launching in July, users tell us they’ve chosen Claude.ai as their day-to-day AI assistant for its longer context windows, faster outputs, complex reasoning capabilities, and more. Many also shared that they would value more file uploads and conversations over longer periods.\n\nWith Claude Pro , subscribers can now gain 5x more usage of our latest model, Claude 2, for a monthly price of $20 (US) or £18 (UK).\n\nThis means you can level up your productivity across a range of tasks, including summarizing research papers, querying contracts, and iterating further on coding projects—like this recent demo of building an interactive map.\nClaude Pro offers:\n5x more usage than our free tier provides, with the ability to send many more messages\nPriority access to Claude.ai during high-traffic periods\nEarly access to new features that help you get the most out of Claude\n\nYou can learn more about these benefits, including how to maximize your usage, here .\n\nWe’re grateful for your support as we strive to build helpful, honest, and harmless systems that fuel productivity and inspire creativity.\n\nRelated content\n\nClaude Fable 5 and Claude Mythos 5\nOur next generation of intelligence for the hardest knowledge work and coding problems.\nRead more \nIntroducing the Services Track and Partner Hub of the Claude Partner Network\nRead more \nWhat we learned mapping a year’s worth of AI-enabled cyber threats\nAs AI transforms the nature of and methods behind cyberattacks, how well do the techniques and frameworks used by the security community hold up? In a new report, we seek to answer that question. \nRead more"},{"ref":"P17","kind":"page","title":"Claude 2 1","date":"2026-06-11T04:18:54.1519+00:00","date_source":null,"source_url":"https://www.anthropic.com/news/claude-2-1","signal_url":null,"signal_json_url":null,"text":"Introducing Claude 2.1 \\ Anthropic \nProduct \nIntroducing Claude 2.1\nNov 21, 2023\n\nOur latest model, Claude 2.1, is now available over API in our Console and is powering our claude.ai chat experience. Claude 2.1 delivers advancements in key capabilities for enterprises—including an industry-leading 200K token context window, significant reductions in rates of model hallucination, system prompts and our new beta feature: tool use. We are also updating our pricing to improve cost efficiency for our customers across models.\n\n200K Context Window \n\nSince our launch earlier this year, Claude has been used by millions of people for a wide range of applications—from translating academic papers to drafting business plans and analyzing complex contracts. In discussions with our users, they’ve asked for larger context windows and more accurate outputs when working with long documents.\n\nIn response, we’re doubling the amount of information you can relay to Claude with a limit of 200,000 tokens, translating to roughly 150,000 words, or over 500 pages of material. Our users can now upload technical documentation like entire codebases, financial statements like S-1s, or even long literary works like The Iliad or The Odyssey. By being able to talk to large bodies of content or data, Claude can summarize, perform Q&A, forecast trends, compare and contrast multiple documents, and much more.\n\nProcessing a 200K length message is a complex feat and an industry first. While we’re excited to get this powerful new capability into the hands of our users, tasks that would typically require hours of human effort to complete may take Claude a few minutes. We expect the latency to decrease substantially as the technology progresses.\n\n2x Decrease in Hallucination Rates \n\nClaude 2.1 has also made significant gains in honesty, with a 2x decrease in false statements compared to our previous Claude 2.0 model. This enables enterprises to build high-performing AI applications that solve concrete business problems and deploy AI across their operations with greater trust and reliability.\n\nWe tested Claude 2.1’s honesty by curating a large set of complex, factual questions that probe known weaknesses "},{"ref":"P18","kind":"page","title":"Partnering With Scale","date":"2026-06-11T04:18:52.908423+00:00","date_source":null,"source_url":"https://www.anthropic.com/news/partnering-with-scale","signal_url":null,"signal_json_url":null,"text":"Partnering with Scale to Bring Generative AI to Enterprises \\ Anthropic \nAnnouncements \nPartnering with Scale to Bring Generative AI to Enterprises\nApr 26, 2023\n\nWe are pleased to announce our partnership with Scale , a leading platform for building, deploying and managing Generative AI applications. Scale customers will now be able to use Claude, our conversational AI assistant based on research into training helpful, honest, and harmless systems.\nMoving beyond experiments into real-world applications of AI requires resourcing and deep expertise. Businesses can now create applications on top of Claude using Scale's robust deployment and management functionality .\nThis partnership lets customers leverage Scale's services such as expert prompt engineering and model validation in order to improve performance and identify weaknesses. In addition, Scale’s enterprise-grade security will be available through a customer’s private AWS environment. Lastly, Scale’s data connectors will allow customers to import proprietary data sources like databases, Confluence, Google Drive and Outlook to work with Claude at scale.\nDario Amodei, CEO of Anthropic, says, “Partnering with Scale allows us to bring our useful model, Claude, to more customers in a thoughtful, scalable way. By combining Scale's AI engineering capabilities with our values-based model development approach, customers now have more assistance as they build and deploy Generative AI applications. I'm excited about this partnership and the work we'll do together to positively shape the future of AI.”\nBy combining Anthropic's Claude model and Constitutional AI system with Scale's robust tooling and functionality, customers gain an enterprise-ready solution to work with Generative AI. We look forward to sharing more details on our partnership to responsibly bring Claude to more organizations.\nThe future of AI is collaborative. We are excited to collaborate with Scale.\n\nRelated content\n\nClaude Fable 5 and Claude Mythos 5\nOur next generation of intelligence for the hardest knowledge work and coding problems.\nRead more \nIntroducing the Services Track and Partner Hub of the Claude Partner"},{"ref":"P19","kind":"page","title":"Skt Partnership Announcement","date":"2026-06-11T04:18:51.393887+00:00","date_source":null,"source_url":"https://www.anthropic.com/news/skt-partnership-announcement","signal_url":null,"signal_json_url":null,"text":"SKT Partnership Announcement \\ Anthropic \nAnnouncements \nSKT Partnership Announcement\nAug 15, 2023\n\nWe are pleased to announce that SK Telecom (\"SKT\"), the largest mobile operator in Korea rapidly integrating AI into its business, has become a commercial partner with Anthropic as well as a strategic investor.\nSKT and Anthropic will work together to develop a large language model that will be customized to best meet the needs of telcos. Using a technique called fine-tuning, Anthropic will leverage SKT’s domain experience in telecommunications in order to make the model optimized for a wide variety of telco applications including customer service, marketing, sales, and interactive consumer applications. The multilingual model will support languages including Korean, English, Japanese, Spanish, and more.\nFine-tuning creates a custom version of our LLM Claude that can be tailored to a specific industry or task, in this case improving performance on telco use cases. Fine-tuning is especially effective when Anthropic can harness the expertise of industry experts. SKT’s experts will provide feedback on Claude’s responses, and this feedback will be used to further train Claude on industry-specific solutions. This process allows Claude to scale the expertise of SKT’s industry-leading talent.\nIn addition to this commercial partnership, SKT has invested an additional $100 million in Anthropic, which follows the previous investment from SK Telecom Venture Capital (SKTVC) in Silicon Valley. “With our strategic investment in Anthropic, a global leading AI technology company, we will be working closely with Anthropic to promote AI innovation,” said Ryu Young-sang, CEO of SKT. “By combining our Korean language-based LLM with Anthropic's strong AI capabilities, we expect to create synergy and gain leadership in the AI ecosystem together with our global telco partners.”\nWe are excited about the opportunities we will unlock by partnering with SKT to build safer, more reliable AI technology that will serve telcos around the world.\n\nRelated content\n\nClaude Fable 5 and Claude Mythos 5\nOur next generation of intelligence for the hardest knowledge work and coding problems.\nRead mo"},{"ref":"P20","kind":"page","title":"Claude 2","date":"2026-06-11T04:18:50.776828+00:00","date_source":null,"source_url":"https://www.anthropic.com/news/claude-2","signal_url":null,"signal_json_url":null,"text":"Claude 2 \\ Anthropic \nAnnouncements \nClaude 2\nJul 11, 2023\nTalk to Claude \n\nWe are pleased to announce Claude 2 , our new model. Claude 2 has improved performance, longer responses, and can be accessed via API as well as a new public-facing beta website, claude.ai . We have heard from our users that Claude is easy to converse with, clearly explains its thinking, is less likely to produce harmful outputs, and has a longer memory. We have made improvements from our previous models on coding, math, and reasoning. For example, our latest model scored 76.5% on the multiple choice section of the Bar exam, up from 73.0% with Claude 1.3. When compared to college students applying to graduate school, Claude 2 scores above the 90th percentile on the GRE reading and writing exams, and similarly to the median applicant on quantitative reasoning.\nThink of Claude as a friendly, enthusiastic colleague or personal assistant who can be instructed in natural language to help you with many tasks. The Claude 2 API for businesses is being offered for the same price as Claude 1.3. Additionally, anyone in the US and UK can start using our beta chat experience today.\nAs we work to improve both the performance and safety of our models, we have increased the length of Claude’s input and output. Users can input up to  100K tokens in each prompt, which means that Claude can work over hundreds of pages of technical documentation or even a book. Claude can now also write longer documents - from memos to letters to stories up to a few thousand tokens - all in one go.\n\nIn addition, our latest model has greatly improved coding skills. Claude 2 scored a 71.2% up from 56.0% on the Codex HumanEval , a Python coding test. On GSM8k, a large set of grade-school math problems, Claude 2 scored 88.0% up from 85.2%. We have an exciting roadmap of capability improvements planned for Claude 2 and will be slowly and iteratively deploying them in the coming months.\n\nWe've been iterating to improve the underlying safety of Claude 2, so that it is more harmless and harder to prompt to produce offensive or dangerous output. We have an internal red-teaming evaluation that scores our models on a large repres"},{"ref":"P21","kind":"page","title":"Anthropic Raises 124 Million To Build More Reliable General Ai Systems","date":"2026-06-11T04:18:50.084519+00:00","date_source":null,"source_url":"https://www.anthropic.com/news/anthropic-raises-124-million-to-build-more-reliable-general-ai-systems","signal_url":null,"signal_json_url":null,"text":"Anthropic raises $124 million to build more reliable, general AI systems \\ Anthropic \nAnnouncements \nAnthropic raises $124 million to build more reliable, general AI systems\nMay 28, 2021\n\nAnthropic, an AI safety and research company, has raised $124 million in a Series A. The financing round will support Anthropic in executing against its research roadmap and building prototypes of reliable and steerable AI systems.\n\nThe company is led by siblings Dario Amodei (CEO) and Daniela Amodei (President). The Anthropic team has previously conducted research into GPT-3 , Circuit-Based Interpretability , Multimodal Neurons , Scaling Laws , AI & Compute , Concrete Problems in AI Safety , and Learning from Human Preferences . Anthropic will use the funding for computationally-intensive research to develop large-scale AI systems that are steerable, interpretable, and robust.\n\n“Anthropic’s goal is to make the fundamental research advances that will let us build more capable, general, and reliable AI systems, then deploy these systems in a way that benefits people. We’re thrilled to be working with investors that support us in this mission and expect to concentrate on research in the immediate term,” said Anthropic CEO Dario Amodei.\n\nAnthropic will focus on research into increasing the safety of AI systems; specifically, the company is focusing on increasing the reliability of large-scale AI models, developing the techniques and tools to make them more interpretable, and building ways to more tightly integrate human feedback into the development and deployment of these systems.\n\nThe Series A round was led by Jaan Tallinn, technology investor and co-founder of Skype. The round included participation from James McClave, Dustin Moskovitz, the Center for Emerging Risk Research, Eric Schmidt, and others.\n\nTo find out more about Anthropic’s research agenda and approach, you can read our website and its job postings. The company is hiring researchers, engineers, and operational experts to support it in executing against its research roadmap. Find out more here: Anthropic.com.\n\nRelated content\n\nClaude Fable 5 and Claude Mythos 5\nOur next generation of intelligence for the hardest know"},{"ref":"P22","kind":"page","title":"Uk Ai Safety Summit","date":"2026-06-11T04:18:46.238713+00:00","date_source":null,"source_url":"https://www.anthropic.com/news/uk-ai-safety-summit","signal_url":null,"signal_json_url":null,"text":"Dario Amodei’s prepared remarks from the AI Safety Summit on Anthropic’s Responsible Scaling Policy \\ Anthropic \nPolicy \nDario Amodei’s prepared remarks from the AI Safety Summit on Anthropic’s Responsible Scaling Policy\nNov 1, 2023\n\nBefore I get into Anthropic’s Responsible Scaling Policy (RSP) , it’s worth explaining some of the unique challenges around measuring AI risks that led us to develop our RSP. The most important thing to understand about AI is how quickly it is moving. A few years ago, AI systems could barely string together a coherent sentence. Today they can pass medical exams, write poetry, and tell jokes. This rapid progress is ultimately driven by the amount of available computation, which is growing by 8x per year and is unlikely to slow down in the next few years. The general trend of rapid improvement is predictable, however, it is actually very difficult to predict when AI will acquire specific skills or knowledge. This unfortunately includes dangerous skills, such as the ability to construct biological weapons1. We are thus facing a number of potential AI-related threats which, although relatively limited given today’s systems, are likely to become very serious at some unknown point in the near future. This is very different from most other industries: imagine if each new model of car had some chance of spontaneously sprouting a new (and dangerous) power, like the ability to fire a rocket boost or accelerate to supersonic speeds.\n\nWe need both a way to frequently monitor these emerging risks, and a protocol for responding appropriately when they occur. Responsible scaling policies—initially suggested by the Alignment Research Center—attempt to meet this need. Anthropic published its RSP in September, and was the first major AI company to do so. It has two major components:\nFirst, we’ve come up with a system called AI safety levels (ASL) , loosely modeled after the internationally recognized BSL system for handling biological materials. Each ASL level has an if-then structure: if an AI system exhibits certain dangerous capabilities, then we will not deploy it or train more powerful models, until certain safeguards are in place.\n\nSecond, we t"},{"ref":"P23","kind":"page","title":"Anthropic Partners With Google Cloud","date":"2026-06-11T04:18:40.336428+00:00","date_source":null,"source_url":"https://www.anthropic.com/news/anthropic-partners-with-google-cloud","signal_url":null,"signal_json_url":null,"text":"Anthropic Partners with Google Cloud \\ Anthropic \nAnnouncements \nAnthropic Partners with Google Cloud\nFeb 3, 2023\n\nAnthropic, an AI safety and research company, has selected Google Cloud as its cloud provider. The partnership is designed so that the companies can co-develop AI computing systems; Anthropic will leverage Google Cloud's cutting-edge GPU and TPU clusters to train, scale, and deploy its AI systems.\n\n“We're partnering with Google Cloud to support the next phase of Anthropic, where we're going to deploy our AI systems to a larger set of people,” said Anthropic CEO Dario Amodei. “This partnership gives us the cloud infrastructure performance and scale we need.”\n\nAnthropic is focused on developing and deploying Claude, an AI assistant based on the company's research into building safe, steerable AI. Anthropic has created safety techniques like Constitutional AI to create AI technologies that are easier to rely on and understand.\n\n“We are eager to use the Google Cloud infrastructure to build reliable, interpretable, and steerable AI systems. This partnership with Google Cloud will let us build a more robust AI platform,” said Dario Amodei.\n\nRelated content\n\nClaude Fable 5 and Claude Mythos 5\nOur next generation of intelligence for the hardest knowledge work and coding problems.\nRead more \nIntroducing the Services Track and Partner Hub of the Claude Partner Network\nRead more \nWhat we learned mapping a year’s worth of AI-enabled cyber threats\nAs AI transforms the nature of and methods behind cyberattacks, how well do the techniques and frameworks used by the security community hold up? In a new report, we seek to answer that question. \nRead more"},{"ref":"P24","kind":"page","title":"Claude 3 Haiku","date":"2026-06-11T04:18:40.090669+00:00","date_source":null,"source_url":"https://www.anthropic.com/news/claude-3-haiku","signal_url":null,"signal_json_url":null,"text":"Claude 3 Haiku: our fastest model yet \\ Anthropic \nAnnouncements \nClaude 3 Haiku: our fastest model yet\nMar 13, 2024\n\nToday we’re releasing Claude 3 Haiku, the fastest and most affordable model in its intelligence class. With state-of-the-art vision capabilities and strong performance on industry benchmarks, Haiku is a versatile solution for a wide range of enterprise applications. The model is now available alongside Sonnet and Opus in the Claude API and on claude.ai for our Claude Pro subscribers.\n\nSpeed is essential for our enterprise users who need to quickly analyze large datasets and generate timely output for tasks like customer support. Claude 3 Haiku is three times faster than its peers for the vast majority of workloads, processing 21K tokens (~30 pages) per second for prompts under 32K tokens [1]. It also generates swift output, enabling responsive, engaging chat experiences and the execution of many small tasks in tandem.\n\nHaiku's pricing model, with a 1:5 input-to-output token ratio, was designed for enterprise workloads which often involve longer prompts. Businesses can rely on Haiku to quickly analyze large volumes of documents, such as quarterly filings, contracts, or legal cases, for half the cost of other models in its performance tier. For instance, Claude 3 Haiku can process and analyze 400 Supreme Court cases [2] or 2,500 images [3] for just one US dollar.\nAlongside its speed and affordability, Claude 3 Haiku prioritizes enterprise-grade security and robustness. We conduct rigorous testing to reduce the likelihood of harmful outputs and jailbreaks of our models so they are as safe as possible. Additional layers of defense include continuous systems monitoring, endpoint hardening, secure coding practices, strong data encryption protocols, and stringent access controls to protect sensitive data. We also conduct regular security audits and work with experienced penetration testers to proactively identify and address vulnerabilities. More information about these measures can be found in the Claude 3 model card .\nStarting today, customers can use Claude 3 Haiku through our API or with a Claude Pro subscription on claude.ai. Claude 3 Haiku is"},{"ref":"P25","kind":"page","title":"Zoom Partnership And Investment","date":"2026-06-11T04:18:39.909669+00:00","date_source":null,"source_url":"https://www.anthropic.com/news/zoom-partnership-and-investment","signal_url":null,"signal_json_url":null,"text":"Zoom Partnership and Investment in Anthropic \\ Anthropic \nAnnouncements \nZoom Partnership and Investment in Anthropic\nMay 16, 2023\n\nWe are announcing a new partnership with Zoom, a leader in enterprise collaboration and communication solutions. Zoom will use Claude, our AI assistant built with Constitutional AI, to build customer-facing AI products focused on reliability, productivity, and safety.\n\"Collaborating with Zoom allows us to bring robust, steerable AI to more people in the workplace,\" said our CEO Dario Amodei. \"We are excited to showcase Anthropic's and Zoom's commitment to boosting productivity through AI-enabled solutions that prioritize safety and helpfulness.”\nWe appreciate Zoom's federated approach to AI, which will use its own technology plus other models, including Claude, for diverse customer needs. The first product integration of Claude will occur in the Zoom Contact Center portfolio, where Claude will help improve the end-user experience and enable superior contact center agent performance.\nWe are also pleased to announce that Zoom Ventures has made an investment in Anthropic. The Zoom team shares our vision of building customer-centric AI products with a foundation of trust and security, that are robust enough for real-world use. By combining our expertise, Zoom and Anthropic will help to incorporate AI into beneficial applications that meets customer needs.\n\nRelated content\n\nClaude Fable 5 and Claude Mythos 5\nOur next generation of intelligence for the hardest knowledge work and coding problems.\nRead more \nIntroducing the Services Track and Partner Hub of the Claude Partner Network\nRead more \nWhat we learned mapping a year’s worth of AI-enabled cyber threats\nAs AI transforms the nature of and methods behind cyberattacks, how well do the techniques and frameworks used by the security community hold up? In a new report, we seek to answer that question. \nRead more"},{"ref":"P26","kind":"page","title":"Anthropic Partners With Menlo Ventures To Launch Anthology Fund","date":"2026-06-11T04:18:38.339458+00:00","date_source":null,"source_url":"https://www.anthropic.com/news/anthropic-partners-with-menlo-ventures-to-launch-anthology-fund","signal_url":null,"signal_json_url":null,"text":"Anthropic partners with Menlo Ventures to launch Anthology Fund \\ Anthropic \nAnnouncements \nAnthropic partners with Menlo Ventures to launch Anthology Fund\nJul 17, 2024\n\nWe’re launching the Anthology Fund in partnership with Menlo Ventures to accelerate the development of groundbreaking AI applications. The Fund is a $100 million initiative financed by Menlo to support startups innovating broadly with Anthropic technology.\n“Through our partnership with Menlo Ventures and the Anthology Fund, we hope to accelerate the development of groundbreaking AI applications. We're particularly interested in ventures that leverage AI to enhance human capabilities and productivity in fields such as healthcare, legal services, education, energy, infrastructure, and scientific research,” said Daniela Amodei, co-founder and President of Anthropic. “We look forward to working closely with Menlo and the exceptional founders backed by this Fund to push the boundaries of what’s possible with AI.”\nThe Fund will back selected entrepreneurs primarily innovating in five key areas: AI infrastructure; novel applications of AI in industries like healthcare, education, scientific research, and more; consumer AI solutions; trust and safety tooling; and AI apps and technology that maximize societal benefits.\nStartups backed by the Anthology Fund will gain access to Anthropic products and research, $25,000 in free credits towards our most advanced models, and best-in-class venture support from Menlo, among other benefits and resources.\n“We are thrilled to join forces with Anthropic to launch the Anthology Fund,” said Matt Murphy, Partner at Menlo Ventures. “By combining Menlo’s company-building experience with Anthropic’s cutting-edge AI technology and talent, we are uniquely positioned to identify and partner with the most promising entrepreneurs shaping the future of AI.”\n\nWe look forward to working with Menlo in supporting the startup ecosystem and driving responsible AI innovation.\n\nInterested startups can learn more and apply here .\n\nRelated content\n\nClaude Fable 5 and Claude Mythos 5\nOur next generation of intelligence for the hardest knowledge work and coding problems.\nRead more \nIn"},{"ref":"P27","kind":"page","title":"Accenture Aws Anthropic","date":"2026-06-11T04:18:36.496677+00:00","date_source":null,"source_url":"https://www.anthropic.com/news/accenture-aws-anthropic","signal_url":null,"signal_json_url":null,"text":"Accenture, AWS, Anthropic Collaboration \\ Anthropic \nAnnouncements \nAnthropic, AWS, and Accenture team up to build trusted solutions for enterprises\nMar 20, 2024\n\nToday we announced a collaboration with Amazon Web Services (AWS) and Accenture. All three organizations are providing key resources to take generative AI ideas from concept to production, especially those in regulated sectors where accuracy, reliability and data security are paramount. Enterprises will be able to deploy models to address their specific needs, while keeping their data private and secure.\nOver 1,400 Accenture engineers will be trained as specialists in using Anthropic’s models on AWS, allowing them to provide customers with end-to-end support that accelerates their AI strategies from concept to production. Accenture’s engineers will help organizations use their own data to fine-tune Anthropic’s models on AWS to enhance performance for their use case and industry. Teams across Accenture and AWS will also guide customers on prompt and platform engineering to help them deploy AI models through Amazon Bedrock and Amazon SageMaker.\nThe initiative is already delivering impact in the public health sector. Powered by Claude through Amazon Bedrock, Accenture collaborated with the District of Columbia Department of Health to create a custom chatbot called Knowledge Assist. Available in both English and Spanish, this intelligent chatbot enables employees and residents to ask questions in natural language and receive quick, accurate responses about health programs and services.\nBy combining Anthropic's technical AI expertise, AWS's approach to security and reliability, and Accenture's deep industry knowledge, we hope to provide tailored solutions for trust-driven sectors and streamline the adoption of powerful AI systems that put humans at the center.\n\nRelated content\n\nClaude Fable 5 and Claude Mythos 5\nOur next generation of intelligence for the hardest knowledge work and coding problems.\nRead more \nIntroducing the Services Track and Partner Hub of the Claude Partner Network\nRead more \nWhat we learned mapping a year’s worth of AI-enabled cyber threats\nAs AI transforms the nature of "},{"ref":"P28","kind":"page","title":"Expanding Access To Claude For Government","date":"2026-06-11T04:18:36.296331+00:00","date_source":null,"source_url":"https://www.anthropic.com/news/expanding-access-to-claude-for-government","signal_url":null,"signal_json_url":null,"text":"Expanding Access to Claude for Government \\ Anthropic \nAnnouncements \nExpanding access to Claude for government\nJun 26, 2024\n\nAnthropic's mission is to build reliable, interpretable, steerable AI systems. We have been excited to see our technology used in areas like coding, customer service, drug discovery, and medical research. We're eager to make these tools available through expanded offerings to government users. Leveraging the flexibility and security of Amazon Web Services [AWS], our AI models Claude 3 Haiku and Claude 3 Sonnet are now available in the AWS Marketplace for the US Intelligence Community [IC] and in AWS GovCloud.\nClaude offers a wide range of potential applications for government agencies, both in the present and looking toward the future. Government agencies can use Claude to provide improved citizen services, streamline document review and preparation, enhance policymaking with data-driven insights, and create realistic training scenarios. In the near future, AI could assist in disaster response coordination, enhance public health initiatives, or optimize energy grids for sustainability. Used responsibly, AI has the potential to transform how elected governments serve their constituents and promote peace and security.\nAdapting to government needs\nWe have been carefully considering the needs of public servants about how Claude could be used to further their missions and the unique needs of government users. In addition to making our models available in the AWS marketplaces that meet stringent government security standards, we are also adapting our service agreements to the unique needs, missions, and legal authorities of governments. \nFor example, we have crafted a set of contractual exceptions to our general Usage Policy that are carefully calibrated to enable beneficial uses by carefully selected government agencies. These allow Claude to be used for legally authorized foreign intelligence analysis, such as combating human trafficking, identifying covert influence or sabotage campaigns, and providing warning in advance of potential military activities, opening a window for diplomacy to prevent or deter them. All other restriction"},{"ref":"E1","kind":"event","title":"anthropics/claude-agent-sdk-python v0.2.97","date":"2026-06-11T05:56:56+00:00","date_source":"source","source_url":"https://github.com/anthropics/claude-agent-sdk-python/releases/tag/v0.2.97","signal_url":"https://onlylabs.fyi/signals/9b5ed084-3cd7-4642-bebf-5dc4d03851fb","signal_json_url":"https://onlylabs.fyi/signals/9b5ed084-3cd7-4642-bebf-5dc4d03851fb/signal.json","text":"release · anthropics/claude-agent-sdk-python v0.2.97 · signal_desk=releases · occurred_at=2026-06-11T05:56:56+00:00 · url=https://github.com/anthropics/claude-agent-sdk-python/releases/tag/v0.2.97 · raw={\"repo\":\"anthropics/claude-agent-sdk-python\"}"},{"ref":"E2","kind":"event","title":"anthropics/claude-code-action 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anthropics/buffa v0.7.1 · signal_desk=releases · occurred_at=2026-06-11T00:11:05+00:00 · url=https://github.com/anthropics/buffa/releases/tag/v0.7.1 · raw={\"repo\":\"anthropics/buffa\"}"},{"ref":"E12","kind":"event","title":"IT Support Engineer","date":"2026-06-11T00:00:13+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/anthropic/jobs/4802076008","signal_url":"https://onlylabs.fyi/signals/29186ecb-74ce-4861-8d21-42ee4e7b6e19","signal_json_url":"https://onlylabs.fyi/signals/29186ecb-74ce-4861-8d21-42ee4e7b6e19/signal.json","text":"job_opened · IT Support Engineer · signal_desk=hiring · occurred_at=2026-06-11T00:00:13+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/4802076008 · data_radar_lanes=Product and customer · data_radar_terms=support · data_radar_reason=Anthropic has a job signal matching product and customer. · raw={\"location\":\"Seattle, WA\",\"ats\":\"greenhouse\"}"},{"ref":"E13","kind":"event","title":"Staff+ Security 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· signal_desk=hiring · occurred_at=2026-06-10T20:06:11+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/5227641008 · data_radar_lanes=Safety and policy · data_radar_terms=security · data_radar_reason=Anthropic has a job signal matching safety and policy. · raw={\"location\":\"London, UK\",\"ats\":\"greenhouse\"}"},{"ref":"E20","kind":"event","title":"anthropics/leptos-chartistry","date":"2026-06-10T19:26:54+00:00","date_source":"source","source_url":"https://github.com/anthropics/leptos-chartistry","signal_url":"https://onlylabs.fyi/signals/041e270f-4748-413b-a5de-0b337c854bd2","signal_json_url":"https://onlylabs.fyi/signals/041e270f-4748-413b-a5de-0b337c854bd2/signal.json","text":"repo_forked · anthropics/leptos-chartistry · signal_desk=forks · occurred_at=2026-06-10T19:26:54+00:00 · url=https://github.com/anthropics/leptos-chartistry · raw={\"repo\":\"anthropics/leptos-chartistry\",\"parent\":\"feral-dot-io/leptos-chartistry\"}"},{"ref":"E21","kind":"event","title":"Communications Lead, Enterprise","date":"2026-06-10T18:33:29+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5164551008","signal_url":"https://onlylabs.fyi/signals/25dfa195-1aa7-494f-b982-ba05b26dfd2e","signal_json_url":"https://onlylabs.fyi/signals/25dfa195-1aa7-494f-b982-ba05b26dfd2e/signal.json","text":"job_opened · Communications Lead, Enterprise · signal_desk=hiring · occurred_at=2026-06-10T18:33:29+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/5164551008 · data_radar_lanes=Product and customer · data_radar_terms=enterprise · data_radar_reason=Anthropic has a job signal matching product and customer. · raw={\"location\":\"San Francisco, CA | New York City, NY\",\"ats\":\"greenhouse\"}"},{"ref":"E22","kind":"event","title":"Program Manager, Communications","date":"2026-06-10T18:06:15+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5252781008","signal_url":"https://onlylabs.fyi/signals/a9061e9b-4f8c-4ab7-8cb9-e120d4fd085f","signal_json_url":"https://onlylabs.fyi/signals/a9061e9b-4f8c-4ab7-8cb9-e120d4fd085f/signal.json","text":"job_opened · Program Manager, Communications · signal_desk=hiring · occurred_at=2026-06-10T18:06:15+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/5252781008 · raw={\"location\":\"San Francisco, CA | New York City, NY\",\"ats\":\"greenhouse\"}"},{"ref":"E23","kind":"event","title":"anthropics/connect-rust 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Advisory · signal_desk=hiring · occurred_at=2026-06-10T17:42:46+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/5205497008 · raw={\"location\":\"San Francisco, CA\",\"ats\":\"greenhouse\"}"},{"ref":"E25","kind":"event","title":"Staff UI Software Engineer, Claude.ai Consumer Product ","date":"2026-06-10T17:24:55+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5026097008","signal_url":"https://onlylabs.fyi/signals/fc7c85b4-d3c7-48a3-a803-601f06e9c17c","signal_json_url":"https://onlylabs.fyi/signals/fc7c85b4-d3c7-48a3-a803-601f06e9c17c/signal.json","text":"job_opened · Staff UI Software Engineer, Claude.ai Consumer Product  · signal_desk=hiring · occurred_at=2026-06-10T17:24:55+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/5026097008 · data_radar_lanes=Product and customer · data_radar_terms=product · data_radar_reason=Anthropic has a job signal matching product and customer. · raw={\"location\":\"San 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Privacy","date":"2026-06-10T13:21:45+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5159146008","signal_url":"https://onlylabs.fyi/signals/f139275e-ade3-4f3e-96cf-ae64f39364df","signal_json_url":"https://onlylabs.fyi/signals/f139275e-ade3-4f3e-96cf-ae64f39364df/signal.json","text":"job_opened · Staff+ Software Engineer, Privacy · signal_desk=hiring · occurred_at=2026-06-10T13:21:45+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/5159146008 · data_radar_lanes=Safety and policy · data_radar_terms=privacy · data_radar_reason=Anthropic has a job signal matching safety and policy. · raw={\"location\":\"San Francisco, CA | New York City, NY | Seattle, WA\",\"ats\":\"greenhouse\"}"},{"ref":"E34","kind":"event","title":"Applied AI Architect, Public 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Corporate Communication Lead · signal_desk=hiring · occurred_at=2026-06-10T07:15:43+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/5149725008 · raw={\"location\":\"Bangalore, India\",\"ats\":\"greenhouse\"}"},{"ref":"E36","kind":"event","title":"Recruiter, AI Research","date":"2026-06-10T00:39:36+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/anthropic/jobs/4935314008","signal_url":"https://onlylabs.fyi/signals/1b7ad944-45af-45ec-8d7b-b7c208894cde","signal_json_url":"https://onlylabs.fyi/signals/1b7ad944-45af-45ec-8d7b-b7c208894cde/signal.json","text":"job_opened · Recruiter, AI Research · signal_desk=hiring · occurred_at=2026-06-10T00:39:36+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/4935314008 · raw={\"location\":\"San Francisco, CA | Seattle, WA\",\"ats\":\"greenhouse\"}"},{"ref":"E37","kind":"event","title":"anthropics/anthropic-sdk-java 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anthropics/anthropic-cli v1.12.1 · signal_desk=releases · occurred_at=2026-06-10T00:09:48+00:00 · url=https://github.com/anthropics/anthropic-cli/releases/tag/v1.12.1 · raw={\"repo\":\"anthropics/anthropic-cli\"}"},{"ref":"E39","kind":"event","title":"anthropics/anthropic-sdk-php v0.29.1","date":"2026-06-09T23:57:10+00:00","date_source":"source","source_url":"https://github.com/anthropics/anthropic-sdk-php/releases/tag/v0.29.1","signal_url":"https://onlylabs.fyi/signals/b72941a9-de08-46b7-a40d-cab02b793a5d","signal_json_url":"https://onlylabs.fyi/signals/b72941a9-de08-46b7-a40d-cab02b793a5d/signal.json","text":"release · anthropics/anthropic-sdk-php v0.29.1 · signal_desk=releases · occurred_at=2026-06-09T23:57:10+00:00 · url=https://github.com/anthropics/anthropic-sdk-php/releases/tag/v0.29.1 · raw={\"repo\":\"anthropics/anthropic-sdk-php\"}"},{"ref":"E40","kind":"event","title":"anthropics/anthropic-sdk-ruby 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· anthropics/anthropic-sdk-go v1.50.1 · signal_desk=releases · occurred_at=2026-06-09T23:54:53+00:00 · url=https://github.com/anthropics/anthropic-sdk-go/releases/tag/v1.50.1 · raw={\"repo\":\"anthropics/anthropic-sdk-go\"}"},{"ref":"E44","kind":"event","title":"Marketing Events Producer","date":"2026-06-09T23:07:44+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5251832008","signal_url":"https://onlylabs.fyi/signals/9e658f4c-684d-496a-bdc6-86516b679067","signal_json_url":"https://onlylabs.fyi/signals/9e658f4c-684d-496a-bdc6-86516b679067/signal.json","text":"job_opened · Marketing Events Producer · signal_desk=hiring · occurred_at=2026-06-09T23:07:44+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/5251832008 · raw={\"location\":\"San Francisco, CA | New York City, NY\",\"ats\":\"greenhouse\"}"},{"ref":"E45","kind":"event","title":"Recruiting Coordinator 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signal_desk=hiring · occurred_at=2026-06-09T20:17:22+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/5197538008 · data_radar_lanes=Product and customer · data_radar_terms=enterprise · data_radar_reason=Anthropic has a job signal matching product and customer. · raw={\"location\":\"New York City, NY; San Francisco, CA | New York City, NY; Seattle, WA\",\"ats\":\"greenhouse\"}"},{"ref":"E50","kind":"event","title":"anthropics/anthropic-cli v1.12.0","date":"2026-06-09T20:14:01+00:00","date_source":"source","source_url":"https://github.com/anthropics/anthropic-cli/releases/tag/v1.12.0","signal_url":"https://onlylabs.fyi/signals/7eaecfa5-073e-403b-978f-620823161a8f","signal_json_url":"https://onlylabs.fyi/signals/7eaecfa5-073e-403b-978f-620823161a8f/signal.json","text":"release · anthropics/anthropic-cli v1.12.0 · signal_desk=releases · occurred_at=2026-06-09T20:14:01+00:00 · url=https://github.com/anthropics/anthropic-cli/releases/tag/v1.12.0 · raw={\"repo\":\"anthropics/anthropic-cli\"}"},{"ref":"E51","kind":"event","title":"anthropics/anthropic-sdk-csharp Bedrock-v0.10.0","date":"2026-06-09T20:05:14+00:00","date_source":"source","source_url":"https://github.com/anthropics/anthropic-sdk-csharp/releases/tag/Bedrock-v0.10.0","signal_url":"https://onlylabs.fyi/signals/cd50cbdd-0092-40b9-9a34-42cc25e2fdaa","signal_json_url":"https://onlylabs.fyi/signals/cd50cbdd-0092-40b9-9a34-42cc25e2fdaa/signal.json","text":"release · anthropics/anthropic-sdk-csharp Bedrock-v0.10.0 · signal_desk=releases · occurred_at=2026-06-09T20:05:14+00:00 · url=https://github.com/anthropics/anthropic-sdk-csharp/releases/tag/Bedrock-v0.10.0 · raw={\"repo\":\"anthropics/anthropic-sdk-csharp\"}"},{"ref":"E52","kind":"event","title":"anthropics/anthropic-sdk-csharp 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anthropics/anthropic-sdk-ruby v1.48.0 · signal_desk=releases · occurred_at=2026-06-09T20:04:57+00:00 · url=https://github.com/anthropics/anthropic-sdk-ruby/releases/tag/v1.48.0 · raw={\"repo\":\"anthropics/anthropic-sdk-ruby\"}"},{"ref":"E55","kind":"event","title":"anthropics/anthropic-sdk-java v2.40.0","date":"2026-06-09T20:04:35+00:00","date_source":"source","source_url":"https://github.com/anthropics/anthropic-sdk-java/releases/tag/v2.40.0","signal_url":"https://onlylabs.fyi/signals/1c9d71bb-03e8-48b7-a376-45cbe20455c8","signal_json_url":"https://onlylabs.fyi/signals/1c9d71bb-03e8-48b7-a376-45cbe20455c8/signal.json","text":"release · anthropics/anthropic-sdk-java v2.40.0 · signal_desk=releases · occurred_at=2026-06-09T20:04:35+00:00 · url=https://github.com/anthropics/anthropic-sdk-java/releases/tag/v2.40.0 · raw={\"repo\":\"anthropics/anthropic-sdk-java\"}"},{"ref":"E56","kind":"event","title":"anthropics/anthropic-sdk-typescript 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Biology · signal_desk=talking · occurred_at=2026-06-08T00:00:00.000Z · url=https://www.anthropic.com/research/agents-in-biology"},{"ref":"E59","kind":"event","title":"Making Claude A Chemist","date":"2026-06-05T20:13:40+00:00","date_source":"sitemap.lastmod","source_url":"https://www.anthropic.com/research/making-claude-a-chemist","signal_url":"https://onlylabs.fyi/signals/e4fbfcdd-15b4-41b9-b011-fd83e7068ae9","signal_json_url":"https://onlylabs.fyi/signals/e4fbfcdd-15b4-41b9-b011-fd83e7068ae9/signal.json","text":"post_published · Making Claude A Chemist · signal_desk=talking · occurred_at=2026-06-05T20:13:40+00:00 · url=https://www.anthropic.com/research/making-claude-a-chemist"},{"ref":"E60","kind":"event","title":"How We Contain 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