{"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":"2026-06-13T13:56:38.899Z","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":"2026-06-13T08:04:04.953+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":35,"forks":1,"releases":16,"talking":8,"repos":0},"data_radar_lanes":{"data":1,"evals":0,"infrastructure":9,"safety":3,"product":11},"data_radar_matches":20,"stored_analysis_evidence":94,"stored_analysis_web":6,"stored_analysis_signal_desks":{"forks":1,"repos":0,"hiring":35,"talking":8,"releases":16},"stored_analysis_data_radar_lanes":{"data":1,"evals":0,"safety":3,"product":11,"infrastructure":9},"stored_analysis_data_radar_matches":20},"stored_web_provenance":{"queries":["\"Anthropic\" frontier AI lab recent model release research hiring GitHub Hugging Face","\"Anthropic\" AI lab what they are building talking about hiring releasing forking"],"request_ids":["747be29aa24feae2a69fa6e1ec8febd0","ac5a159f6ed0e9517150936724110877"],"skipped":null},"evidence":[{"ref":"P1","kind":"page","title":"Field Reporting Insights Manager","date":"2026-06-12T07:03:53.810872+00:00","date_source":null,"source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5253257008","signal_url":null,"signal_json_url":null,"text":"Job Application for Field Reporting Insights Manager at Anthropic \n\nBack to jobs New \nField Reporting Insights Manager\nSan Francisco, CA | New York City, NY\n\nApply \nAbout Anthropic \n\nAnthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n\nAbout the role\n\nAnthropic's go-to-market organization is scaling fast, and the quality of our decisions increasingly depends on the quality of our reporting. We're looking for a Field Reporting and Insights Manager to build and own the reporting foundation that every GTM leader — from individual contributors to the CCO — relies on to run their business.\n\nThis is not a dashboard maintenance role. It's a role for someone who understands that a single, well-designed view of pipeline health can change how a sales leader runs their Monday morning, and who can build that view because they understand how that leader actually thinks. You'll own the design and delivery of certified, centralized reporting across pipeline, forecast, hiring, customer health, and rep productivity — and you'll do it for a business whose model doesn't map cleanly onto the traditional SaaS playbook.\n\nKey responsibilities\n\nBuild the reporting foundation \n\nDesign, build, and certify the core reporting suite that serves as the single source of truth for GTM performance — spanning pipeline, forecast, bookings, capacity, customer health, and rep productivity\n\nArchitect reporting with a clear audience hierarchy, so the same underlying data powers the CCO's board prep, the RVP's weekly operating review, and each rep's self-service view of their own book\n\nPartner with Analytics Engineering and Data Infrastructure to ensure data lineage is clean, metric definitions are governed, and calculations reconcile cleanly across Salesforce, Looker, and BigQuery\n\nEstablish and own the standard for \"certified\" reporting, so that when a number appears in an operating review, no one has to ask where it came from\n\nTransl"},{"ref":"P2","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":"P3","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":"P4","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":"P5","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":"P6","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":"P7","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":"P8","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":"P9","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":"P10","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":"P11","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":"P12","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":"P13","kind":"page","title":"Head of FX & Risk","date":"2026-06-13T08:03:26.474942+00:00","date_source":null,"source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5250433008","signal_url":null,"signal_json_url":null,"text":"Job Application for Head of FX & Risk at Anthropic \n\nBack to jobs New \nHead of FX & Risk\nRemote-Friendly (Travel Required) | San Francisco, CA\n\nApply \nAbout Anthropic \n\nAnthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n\nAbout the role\n\nAnthropic is seeking a Head of FX & Risk to join our Treasury team. In this foundational role, you will design and implement our financial risk management program from the ground up – quantifying exposures, setting risk tolerance thresholds, and executing on strategy – while building the frameworks that govern our long-term risk management objectives.\n\nThis is a rare opportunity to shape critical Treasury functions at a high-growth AI company. You will own FX, interest rate, commodity, and counterparty risk end-to-end. You will protect earnings and cash from volatility so the company can plan with confidence. This role reports directly to the Treasurer and partners closely with Finance leadership, Accounting, Legal, and cross-functional teams to align Treasury operations with broader financial strategies and business objectives.\n\nKey responsibilities\n\nBuild an integrated financial risk management framework spanning FX, commodity, rates, and counterparty risk\n\nDesign, implement, and execute Anthropic's FX program, including exposure quantification, risk tolerance thresholds, policy development, and instrument selection\n\nEstablish a commodity exposure strategy to support our growing compute portfolio\n\nManage interest rate risk across the investment portfolio and any debt\n\nDesign and operationalize a counterparty risk framework with documented limits and regular monitoring across a growing bank and asset manager footprint\n\nRun scenario planning and stress testing to understand how exposures evolve as the business scales\n\nDevelop Treasury policies and controls, and deliver integrated financial risk reporting to the CFO\n\nMinimum qualifications\n\nDeep expertise in FX hed"},{"ref":"P14","kind":"page","title":"Staff Software Engineer, Inference","date":"2026-06-13T08:03:26.402229+00:00","date_source":null,"source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5097742008","signal_url":null,"signal_json_url":null,"text":"Job Application for Staff Software Engineer, Inference at Anthropic \n\nBack to jobs \nStaff Software Engineer, Inference\nLondon, UK\n\nApply \nAbout Anthropic \n\nAnthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n\nAbout the role \n\nOur Inference team is responsible for building and maintaining the critical systems that serve Claude to millions of users worldwide. We bring Claude to life by serving our models via the industry's largest compute-agnostic inference deployments. We are responsible for the entire stack from intelligent request routing to fleet-wide orchestration across diverse AI accelerators. The team has a dual mandate: maximizing compute efficiency to serve our explosive customer growth, while enabling breakthrough research by giving our scientists the high-performance inference infrastructure they need to develop next-generation models. We tackle complex, distributed systems challenges across multiple accelerator families and emerging AI hardware running in multiple cloud platforms.\n\nAs a Staff Software Engineer on our Inference team , you will work end to end, identifying and addressing key infrastructure blockers to serve Claude to millions of users while enabling breakthrough AI research. Strong candidates should have familiarity with performance optimization, distributed systems, large-scale service orchestration, and intelligent request routing. Familiarity with LLM inference optimization, batching strategies, and multi-accelerator deployments is highly encouraged but not strictly necessary.\n\nStrong candidates may also have experience with \n\nHigh-performance, large-scale distributed systems\n\nImplementing and deploying machine learning systems at scale\n\nLoad balancing, request routing, or traffic management systems\n\nLLM inference optimization, batching, and caching strategies\n\nKubernetes and cloud infrastructure (AWS, GCP)\n\nPython or Rust\n\nYou may be a good fit if you \n\nHave significa"},{"ref":"P15","kind":"page","title":"Account Executive - ASEAN","date":"2026-06-13T08:03:26.375909+00:00","date_source":null,"source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5222180008","signal_url":null,"signal_json_url":null,"text":"Job Application for Account Executive - ASEAN at Anthropic \n\nBack to jobs New \nAccount Executive - ASEAN\nSydney, Australia\n\nApply \nAbout Anthropic \n\nAnthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n\nAbout the Role \n\nAs an Enterprise Account Executive at Anthropic, you'll join the foundational team at the forefront of introducing our cutting-edge AI productivity SaaS solution to Enterprise organizations across Southeast Asia. You'll drive the adoption of safe, frontier AI by securing strategic deals with major corporations, technology companies, and research institutions across the ASEAN region. You'll leverage your consultative sales expertise in the enterprise sector to propel revenue growth while becoming a trusted partner to enterprise stakeholders, helping them embed and deploy AI while uncovering its full range of capabilities across business operations, research, and administration. In collaboration with GTM, Product, and Marketing teams, you'll continuously refine our value proposition, sales methodology, and market positioning to resonate with enterprise decision-makers across ASEAN markets.\n\nThis role is based in Sydney, Australia, with a commercial remit focused squarely on the ASEAN market. You'll spend significant time in-region — expect frequent travel across Southeast Asia — serving as Anthropic's senior commercial presence for ASEAN as we build toward a dedicated in-market footprint. You'll play a direct role in shaping that regional expansion.\n\nWhat You'll Do \n\nWork with Anthropic's most strategic ASEAN enterprise accounts — providing senior regional coverage, executive relationship depth, and commercial momentum through frequent in-market engagement that the current team can't deliver from outside the region\n\nOwn and exceed revenue targets by winning new enterprise accounts and expanding existing relationships across ASEAN\n\nBuild and execute territory plans, identifying high-va"},{"ref":"P16","kind":"page","title":"Sr. Software Engineer, Inference","date":"2026-06-13T08:03:26.348269+00:00","date_source":null,"source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5152348008","signal_url":null,"signal_json_url":null,"text":"Job Application for Sr. Software Engineer, Inference at Anthropic \n\nBack to jobs \nSr. Software Engineer, Inference\nLondon, UK\n\nApply \nAbout Anthropic \n\nAnthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n\nAbout the role: \n\nOur Inference team is responsible for building and maintaining the critical systems that serve Claude to millions of users worldwide. We bring Claude to life by serving our models via the industry's largest compute-agnostic inference deployments. We are responsible for the entire stack from intelligent request routing to fleet-wide orchestration across diverse AI accelerators.\n\nThe team has a dual mandate: maximizing compute efficiency to serve our explosive customer growth, while enabling breakthrough research by giving our scientists the high-performance inference infrastructure they need to develop next-generation models. We tackle complex, distributed systems challenges across multiple accelerator families and emerging AI hardware running in multiple cloud platforms.\n\nStrong candidates may also have experience with: \n\nHigh-performance, large-scale distributed systems\n\nImplementing and deploying machine learning systems at scale\n\nLoad balancing, request routing, or traffic management systems\n\nLLM inference optimization, batching, and caching strategies\n\nKubernetes and cloud infrastructure (AWS, GCP)\n\nPython or Rust\n\nYou may be a good fit if you: \n\nHave significant software engineering experience, particularly with distributed systems\n\nAre results-oriented, with a bias towards flexibility and impact\n\nPick up slack, even if it goes outside your job description\n\nWant to learn more about machine learning systems and infrastructure\n\nThrive in environments where technical excellence directly drives both business results and research breakthroughs\n\nCare about the societal impacts of your work\n\nRepresentative projects across the org: \n\nDesigning intelligent routing algorithms that op"},{"ref":"P17","kind":"page","title":"Technical Program Manager, API Platform","date":"2026-06-13T07:02:04.911309+00:00","date_source":null,"source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5256303008","signal_url":null,"signal_json_url":null,"text":"Job Application for Technical Program Manager, API Platform at Anthropic \n\nBack to jobs New \nTechnical Program Manager, API Platform\nSan Francisco, CA | New York City, NY\n\nApply \nAbout Anthropic \n\nAnthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n\nAbout the Role \n\nAnthropic's API is the foundation that customers, developers, and our own products build on, and it is scaling fast. We're hiring a Technical Program Manager to drive the scaling and core efficiency programs for the API stack, working at the intersection of the API and the infrastructure it runs on (compute, databases, networking, and inference).\n\nToday this work spans many teams and many simultaneous programs, with no single person focused on cohesion across them. You'll be the center: the person who connects isolated workstreams into one coherent roadmap, makes cross-org dependencies visible before they become blockers, and creates clarity on who owns which decisions. Success looks like an API stack that scales efficiently and predictably, with programs that land because someone is accountable for the whole picture.\n\nResponsibilities \n\nDrive scaling and core efficiency programs for the API stack from architectural direction through sequenced, deliverable execution\n\nRun multiple large programs simultaneously across infrastructure, networking, and the API layer\n\nAct as the connective tissue between API, infrastructure, database, networking, and inference teams, owning cross-org dependencies end to end\n\nEstablish decision-making clarity: who owns what, where trade-offs get made, and how isolated streams roll up into one program view\n\nSurface risks, progress, and trade-offs to engineering leadership with the right altitude and cadence\n\nYou may be a good fit if you \n\nHave 7+ years of technical program management experience, with a track record of successfully delivering complex, cross-functional programs\n\nHave experience with API platform"},{"ref":"P18","kind":"page","title":"IT Systems Engineer, Client Platform Engineer","date":"2026-06-13T07:02:04.850555+00:00","date_source":null,"source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5255853008","signal_url":null,"signal_json_url":null,"text":"Job Application for IT Systems Engineer, Client Platform Engineer at Anthropic \n\nBack to jobs New \nIT Systems Engineer, Client Platform Engineer\nBoston, MA; New York City, NY; Remote-Friendly (Travel-Required) | Washington, DC\n\nApply \nAbout Anthropic \n\nAnthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n\nAbout the role\n\nThe Endpoint team (Client Platform Engineering) treats Anthropic’s device fleet as a distributed platform, not a collection of laptops. We run our own MDM as a production service and manage every piece of device configuration as code. Policies, configuration profiles, queries, remediation scripts, and software all ship through pull requests, CI, a staging environment, and a canary group before they reach the fleet. The fleet spans macOS, Windows, and a growing mobile footprint.\n\nYou’ll own that platform end to end: the infrastructure underneath the MDM, the configuration on top of it, the patching and software pipelines that keep thousands of devices patched and secure, and the telemetry that tells us what is actually true on every device. You’ll build zero touch provisioning that turns a sealed box into a productive machine on day one, manage rapid patching enforcement schedules while maintaining a good user experience, and build automation and Claude-driven workflows to eliminate operational toil. The role sits at the intersection of security and developer experience: working with Security teams on hardening, compliance controls, and detection and response, and with developer and infrastructure teams to make sure controls don't get in the way of getting work done. It also lays the groundwork for access decisions based on device trust.\n\nIf you think of “100% compliant” as a claim to audit rather than a fact to report, you’ll fit right in. The team is deliberately lean and runs with high autonomy. You’ll help define the endpoint roadmap, make architecture decisions, and own the pla"},{"ref":"P19","kind":"page","title":"Product Management, Human Data Platform","date":"2026-06-13T07:02:04.797799+00:00","date_source":null,"source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5195866008","signal_url":null,"signal_json_url":null,"text":"Job Application for Product Management, Human Data Platform at Anthropic \n\nBack to jobs \nProduct Management, Human Data Platform\nSan Francisco, CA | New York City, NY\n\nApply \nAbout Anthropic \n\nAnthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n\nAbout the Role \n\nAnthropic's Human Data Platform team builds systems designed to collect data that improves our models. This includes the infrastructure to simulate real-world environments and tasks, novel interfaces for data vendors to use, and the pipelines that enable researchers to gather high-quality data at scale. As Claude's real-world usage evolves, so do our data needs — and our tooling has to keep pace. You'll work alongside an engineering team that's quickly prototyping and shipping, help make smart bets about where to focus, and ensure we're investing in tooling that scales. You'll work across research teams, data ops, and external vendors, translating what you learn into clear direction on what to build next.\n\nResponsibilities \n\nOwn the product direction for our human data tooling, with clear prioritization across labeling interfaces, infrastructure investments, data quality, and operational visibility\n\nPartner with engineering to scope and ship quickly, staying close to the work in a fast-moving prototyping environment\n\nDevelop a deep understanding of research and training approaches to identify where tooling investments will have the highest leverage\n\nIdentify patterns across one-off requests and push toward reusable infrastructure that compounds over time\n\nSit in on crowd worker and vendor sessions to systematically understand pain points\n\nDefine and track outcome-based KPIs: time-to-launch for new data collection projects, end-to-end data quality scores, and measurable impact on model evaluation scores\n\nYou May Be a Good Fit If You \n\nBelieve that advanced AI systems could have a transformative effect on the world and are interested in help"},{"ref":"P20","kind":"page","title":"Sr. Manager, Procurement Lease Administration ","date":"2026-06-13T07:02:04.62789+00:00","date_source":null,"source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5253835008","signal_url":null,"signal_json_url":null,"text":"Job Application for Sr. Manager, Procurement Lease Administration at Anthropic \n\nBack to jobs New \nSr. Manager, Procurement Lease Administration \nSan Francisco, CA | New York City, NY\n\nApply \nAbout Anthropic \n\nAnthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n\nAbout the Role \n\nWe're looking for a Lease Admin to own the operational layer of Anthropic's Infrastructure Compute lease portfolio. That means data centers and co-location facilities, GPU and TPU equipment finance leases. This role sits in Infrastructure Compute Procurement and partners closely with the Infrastructure Accounting team on anything that touches the financials. Decision rights on renewals and commercial terms stay with Infra and Workplace. Your job is to make sure the data is clean, the dates are tracked, the bills are right, and the payments go out on time.\n\nKey Responsibilities\n\nLease abstraction and system data hygiene ingest new leases and amendments into the LMS with clean, complete data; maintain the record of truth across all portfolio categories\n\nCritical-date tracking and routing monitor renewal windows, option deadlines, and notice periods; flag and route decisions to the right stakeholders (Infra / Workplace) with enough lead time to act\n\nLandlord billing audits review landlord invoices against lease terms, dispute errors, and reconcile CAM charges across the co-location and real estate portfolio\n\nRent payment processing and AP coding ensure rent and lease payments are processed accurately and on time; maintain correct GL coding at the source\n\nLandlord billing ops and payment escalations manage payment workflows and escalate commercial disputes to the appropriate Infra or Workplace relationship owner\n\nLease system data stewardship and Workday integration keep the LMS in sync with Workday/ERP\n\nSupport the accounting team's close cycle with clean subledger data and timely inputs\n\nMinimum qualifications \n\nExperience i"},{"ref":"P21","kind":"page","title":"Staff+ Software Engineer, Inference Runtime","date":"2026-06-13T07:02:04.592598+00:00","date_source":null,"source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5257650008","signal_url":null,"signal_json_url":null,"text":"Job Application for Staff+ Software Engineer, Inference Runtime at Anthropic \n\nBack to jobs New \nStaff+ Software Engineer, Inference Runtime\nRemote-Friendly (Travel-Required) | San Francisco, CA | Seattle, WA | New York City, NY\n\nApply \nAbout Anthropic \n\nAnthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n\nAbout the role\n\nAnthropic's Inference organization serves Claude to millions of users and enterprise customers with the speed, reliability, and efficiency that frontier AI demands. We build across GPUs, TPUs, and Trainium, and the complexity of our development environment grows with every platform we add.\n\nWe're looking for a Staff Engineer to be a technical lead for Inference Runtime: the team that owns the shared, accelerator-agnostic core of our inference serving stack, whose performance, correctness, and abstractions every accelerator builds on.\n\nThis is a senior IC role with broad technical ownership. You'll set technical direction for the runtime's architecture, its release and validation systems, and the workflows engineers use to develop on top of it. You will partner across Inferencing to make hard calls on boundaries, prioritization, and tradeoffs across heterogeneous accelerator platforms.\n\nYou'll pair with the team's Engineering Manager, who owns hiring and people development, while you own the technical roadmap and drive the work, representing the team in cross-org efforts spanning serving, scaling, and accelerator teams.\n\nThis role is for someone who has been the technical anchor of a platform with many internal consumers, who thinks in systems and feedback loops, and who gets real satisfaction from building abstractions that hold up as the system scales another order of magnitude.\n\nKey responsibilities\n\nSet technical direction for the team, owning the architecture and roadmap for the shared runtime of the inference serving stack\n\nOwn and evolve the accelerator-agnostic runtime its"},{"ref":"P22","kind":"page","title":"Manager, Startup Partnerships","date":"2026-06-13T07:02:04.495675+00:00","date_source":null,"source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5229558008","signal_url":null,"signal_json_url":null,"text":"Job Application for Manager, Startup Partnerships at Anthropic \n\nBack to jobs New \nManager, Startup Partnerships\nSan Francisco, CA | New York City, NY\n\nApply \nAbout Anthropic \n\nAnthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n\nAbout the role \n\nAs the Manager, Startup Partnerships at Anthropic, you'll lead a high-performing and technical team of startup partners focused on driving adoption of Anthropic's AI solutions across the startup landscape while ensuring responsible AI deployment. As a leadership role on the startups GTM team, you will lead a multi-functional team working across accelerators, incubators, and ecosystem partners across mature and emerging markets. The team’s goal is to obsess over winning day-zero founders globally and expand our customer base to ensure every new AI startup chooses to build on Claude. \n\nYou will be instrumental in establishing Anthropic's presence in the global startup ecosystem, driving pipeline and new logos to startup sales across multiple regions. This role requires someone who thrives in ambiguity, can build from the ground up in new markets, and is driven to deliver exceptional results. \n\nWhat you'll do: \n\nTeam Leadership \n\nExperience managing P&L and hitting aggressive growth targets in fast-paced environments\n\nBuild and lead a high-performing, geographically distributed team of startup partners capable of influencing at senior decision-makers and GPs within accelerator, and partner organizations\n\nWork with cross functional teams across sales, marketing, product & data science\n\nRecruit, develop, and retain exceptional talent across multiple regions and cultures. Create a unified, results-oriented culture across geographies that attracts top talent and delivers outstanding outcomes to customers and the business. \n\nEcosystem Development \n\nLead multi-threaded partner coverage across Accelerators, and Ecosystem Partners \n\nWork with partners to establish C"},{"ref":"P23","kind":"page","title":"Web Producer, CMS Publishing","date":"2026-06-13T07:02:04.427961+00:00","date_source":null,"source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5257669008","signal_url":null,"signal_json_url":null,"text":"Job Application for Web Producer, CMS Publishing at Anthropic \n\nBack to jobs New \nWeb Producer, CMS Publishing\nSan Francisco, CA | New York City, NY\n\nApply \nAbout Anthropic \n\nAnthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n\nAbout the role \n\nThe web operations team is part of Anthropic's Creative Studio — the in-house team responsible for brand expression across Anthropic and Claude. We're responsible for how content gets built, published, and maintained across anthropic.com and claude.com, and we care deeply about quality, consistency, and getting things right.\n\nWe're looking for a Web Producer to join us in a hybrid production and content role, working at the intersection of editorial precision, web craft, and operational coordination. You'll be a trusted partner to content teams across the company, helping them navigate the publishing process, execute content updates, and build out pages that meet our quality bar. You'll also play a key role in keeping our web operations running smoothly by triaging incoming requests, coordinating smaller projects, and supporting launches with careful QA. You won't just execute tasks — you'll help improve the publishing experience for everyone who depends on it.\n\nKey responsibilities \n\nBuild and publish content across our CMS platforms, including assembling templated pages and maintaining content collections with accuracy and consistency\n\nOwn the web intake queue by triaging requests from partners, setting clear expectations on scope and timing, and keeping work moving\n\nPerform thorough QA across staging and production environments before content goes live, catching errors in copy, formatting, links, and rendering\n\nSupport web launches and campaigns by coordinating content publishing across relevant surfaces and flagging risks before they become problems\n\nPartner with marketers and content teams to help them work effectively within our web infrastructure — a"},{"ref":"P24","kind":"page","title":"Engineering Manager, Enterprise","date":"2026-06-13T07:02:04.391483+00:00","date_source":null,"source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5255912008","signal_url":null,"signal_json_url":null,"text":"Job Application for Engineering Manager, Enterprise at Anthropic \n\nBack to jobs New \nEngineering Manager, Enterprise\nSan Francisco, CA | New York City, NY\n\nApply \nAbout Anthropic \n\nAnthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n\nAbout the role \n\nEnterprise is central to Anthropic's mission. The organizations that could benefit most from Claude are often the most demanding buyers, with rigorous requirements around security, compliance, and control. We believe earning their trust is essential to ensuring AI benefits the world broadly.\n\nWe're looking for an engineering manager for our Enterprise pillar —the team that makes Claude enterprise-ready at scale. When a Fortune 500 company wants to roll out Claude to 100,000 employees, we're the team that makes it possible.\n\nYou'll build the foundational systems that large organizations require before they can deploy AI at scale. This work directly converts product-market fit into revenue by removing the deployment blockers that prevent large organizations from adopting Claude broadly.\n\nThis role sits at the intersection of enterprise product, platform infrastructure, and go-to-market. You'll partner closely with product, design, sales, and customer success to understand what our largest customers need, then translate those requirements into scalable technical solutions that work across Claude.ai , Claude Code, and API.\n\nResponsibilities \n\nLead and develop a team of engineers building out features and foundations that make Claude enterprise-ready at scale\n\nOwn engineering execution end-to-end: planning, prioritization, delivery quality, team health, and incident response\n\nPartner with engineering teams throughout the company to ensure that the platforms we build are extensible and easy to adopt \n\nPartner with sales and customer success on enterprise deals—understanding requirements, representing engineering in key conversations, and turning what you le"},{"ref":"P25","kind":"page","title":"anthropics/claude-code v2.1.177","date":"2026-06-13T07:02:03.962934+00:00","date_source":null,"source_url":"https://github.com/anthropics/claude-code/releases/tag/v2.1.177","signal_url":null,"signal_json_url":null,"text":"# v2.1.177\n\nRepository: anthropics/claude-code\n\nTag: v2.1.177\n\nPublished: 2026-06-13T01:25:15Z\n\nPrerelease: no\n\nRelease notes: none published."},{"ref":"P26","kind":"page","title":"anthropics/claude-code v2.1.176","date":"2026-06-13T07:02:03.958223+00:00","date_source":null,"source_url":"https://github.com/anthropics/claude-code/releases/tag/v2.1.176","signal_url":null,"signal_json_url":null,"text":"# v2.1.176\n\nRepository: anthropics/claude-code\n\nTag: v2.1.176\n\nPublished: 2026-06-12T21:53:27Z\n\nPrerelease: no\n\nRelease notes:\n## What's changed\n\n- Session titles are now generated in the language of your conversation (set the `language` setting to pin a specific language)\n- Added `footerLinksRegexes` setting for regex-matched link badges in the footer row, configurable via user or managed settings\n- Improved Bedrock credential caching: credentials from `awsCredentialExport` are now cached until their `Expiration` instead of a fixed 1 hour\n- Fixed `availableModels` enforcement: alias model picks can no longer be redirected to a blocked model via `ANTHROPIC_DEFAULT_*_MODEL` environment variables, and `/fast` now refuses to toggle when it would switch to a model outside the allowlist\n- Fixed auto mode failing on Fable 5 for organizations without Opus 4.8 enabled — the classifier now falls back to the best available Opus model\n- Fixed hook `if` conditions for Read/Edit/Write tool paths: documented patterns like `Edit(src/**)`, `Read(~/.ssh/**)`, and `Read(.env)` now match correctly\n- Fixed Linux sandbox failing to start when `.claude/settings.json` is a symlink with an absolute target\n- Fixed `/copy` and mouse-selection copy not reaching the system clipboard inside tmux over SSH, and tmux paste buffer not loading on versions older than 3.2\n- Fixed Remote Control connecting from web/mobile silently switching the session's model\n- Fixed Remote Control disconnect notifications showing a bare numeric code instead of a human-readable reason, and connection failures adding a duplicate line to the conversation transcript\n- Fixed Remote Control sessions not disconnecting when you sign in to a different account\n- Fixed `/cd` and worktree moves leaving the session reporting the previous directory's git branch\n- Fixed `claude agents`: pressing back in one window no longer detaches other windows attached to the same session\n- Fixed backgrounded sessions showing \"Working\" forever when `/bg` mid-turn had nothing left to continue\n- Fixed background agent search by PR URL: PRs opened during scheduled wakeups or while a job was blocked now appear in `claude agents` search\n- Fixed t"},{"ref":"P27","kind":"page","title":"anthropics/claude-agent-sdk-typescript v0.3.176","date":"2026-06-13T07:02:03.709299+00:00","date_source":null,"source_url":"https://github.com/anthropics/claude-agent-sdk-typescript/releases/tag/v0.3.176","signal_url":null,"signal_json_url":null,"text":"# v0.3.176\n\nRepository: anthropics/claude-agent-sdk-typescript\n\nTag: v0.3.176\n\nPublished: 2026-06-12T21:53:27Z\n\nPrerelease: no\n\nRelease notes:\n## What's changed\n\n- Fixed turn `result` messages being dropped when multiple turns complete while a background agent or workflow is running\n- Fixed background agent, remote agent, and MCP task state not being restored when resuming a session via the SDK\n\n## Update\n\n```sh\nnpm install @anthropic-ai/claude-agent-sdk@0.3.176\n# or\nyarn add @anthropic-ai/claude-agent-sdk@0.3.176\n# or\npnpm add @anthropic-ai/claude-agent-sdk@0.3.176\n# or\nbun add @anthropic-ai/claude-agent-sdk@0.3.176\n```"},{"ref":"P28","kind":"page","title":"anthropics/claude-code-action v1.0.147","date":"2026-06-13T07:02:03.66154+00:00","date_source":null,"source_url":"https://github.com/anthropics/claude-code-action/releases/tag/v1.0.147","signal_url":null,"signal_json_url":null,"text":"# v1.0.147\n\nRepository: anthropics/claude-code-action\n\nTag: v1.0.147\n\nPublished: 2026-06-12T21:55:51Z\n\nPrerelease: no\n\nRelease notes:\n## What's Changed\n* Add pr-stamp-sweep review workflow by @ashwin-ant in https://github.com/anthropics/claude-code-action/pull/1409\n\n**Full Changelog**: https://github.com/anthropics/claude-code-action/compare/v1...v1.0.147"},{"ref":"E1","kind":"event","title":"Claude Fable 5 Mythos 5","date":"2026-06-09T00:00:00.000Z","date_source":"page.visible_date","source_url":"https://www.anthropic.com/news/claude-fable-5-mythos-5","signal_url":"https://onlylabs.fyi/signals/2648db51-9d6a-42a9-aece-a0ca5f9ce64f","signal_json_url":"https://onlylabs.fyi/signals/2648db51-9d6a-42a9-aece-a0ca5f9ce64f/signal.json","text":"post_published · Claude Fable 5 Mythos 5 · signal_desk=talking · occurred_at=2026-06-09T00:00:00.000Z · url=https://www.anthropic.com/news/claude-fable-5-mythos-5 · hn=2603 points/2143 comments"},{"ref":"E2","kind":"event","title":"Fable Mythos Access","date":"2026-06-12T00:00:00.000Z","date_source":"page.visible_date","source_url":"https://www.anthropic.com/news/fable-mythos-access","signal_url":"https://onlylabs.fyi/signals/d53ba8a9-f9e3-4086-9760-d242145886bd","signal_json_url":"https://onlylabs.fyi/signals/d53ba8a9-f9e3-4086-9760-d242145886bd/signal.json","text":"post_published · Fable Mythos Access · signal_desk=talking · occurred_at=2026-06-12T00:00:00.000Z · url=https://www.anthropic.com/news/fable-mythos-access · hn=1306 points/854 comments"},{"ref":"E3","kind":"event","title":"Making Claude A Chemist","date":"2026-06-05T00:00:00.000Z","date_source":"page.visible_date","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=2026-06-05T00:00:00.000Z · url=https://www.anthropic.com/research/making-claude-a-chemist · hn=6 points/1 comments"},{"ref":"E4","kind":"event","title":"Claude Corps","date":"2026-06-11T13:36:07+00:00","date_source":"sitemap.lastmod","source_url":"https://www.anthropic.com/news/claude-corps","signal_url":"https://onlylabs.fyi/signals/a49e39ee-d01e-4f42-ac9f-19346441cc8a","signal_json_url":"https://onlylabs.fyi/signals/a49e39ee-d01e-4f42-ac9f-19346441cc8a/signal.json","text":"post_published · Claude Corps · signal_desk=talking · occurred_at=2026-06-11T13:36:07+00:00 · url=https://www.anthropic.com/news/claude-corps · hn=5 points/0 comments"},{"ref":"E5","kind":"event","title":"Tcs Anthropic Partnership","date":"2026-06-12T17:59:27+00:00","date_source":"sitemap.lastmod","source_url":"https://www.anthropic.com/news/tcs-anthropic-partnership","signal_url":"https://onlylabs.fyi/signals/b698dc92-b229-48c5-8a43-d57667b3d0ec","signal_json_url":"https://onlylabs.fyi/signals/b698dc92-b229-48c5-8a43-d57667b3d0ec/signal.json","text":"post_published · Tcs Anthropic Partnership · signal_desk=talking · occurred_at=2026-06-12T17:59:27+00:00 · url=https://www.anthropic.com/news/tcs-anthropic-partnership · hn=2 points/0 comments"},{"ref":"E6","kind":"event","title":"Agents In Biology","date":"2026-06-08T00:00:00.000Z","date_source":"page.visible_date","source_url":"https://www.anthropic.com/research/agents-in-biology","signal_url":"https://onlylabs.fyi/signals/6c78c028-3ab4-4b33-86f7-d86c8ba9e3ba","signal_json_url":"https://onlylabs.fyi/signals/6c78c028-3ab4-4b33-86f7-d86c8ba9e3ba/signal.json","text":"post_published · Agents In Biology · signal_desk=talking · occurred_at=2026-06-08T00:00:00.000Z · url=https://www.anthropic.com/research/agents-in-biology · hn=2 points/0 comments"},{"ref":"E7","kind":"event","title":"Engineering Manager, Enterprise","date":"2026-06-13T03:48:51+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5255912008","signal_url":"https://onlylabs.fyi/signals/0543d363-25bc-4aee-aa57-3d1849e0afed","signal_json_url":"https://onlylabs.fyi/signals/0543d363-25bc-4aee-aa57-3d1849e0afed/signal.json","text":"job_opened · Engineering Manager, Enterprise · signal_desk=hiring · occurred_at=2026-06-13T03:48:51+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/5255912008 · 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":"E8","kind":"event","title":"anthropics/claude-agent-sdk-python v0.2.101","date":"2026-06-13T01:38:30+00:00","date_source":"source","source_url":"https://github.com/anthropics/claude-agent-sdk-python/releases/tag/v0.2.101","signal_url":"https://onlylabs.fyi/signals/189ea083-35f3-4632-89e8-6d89bd964e7d","signal_json_url":"https://onlylabs.fyi/signals/189ea083-35f3-4632-89e8-6d89bd964e7d/signal.json","text":"release · anthropics/claude-agent-sdk-python v0.2.101 · signal_desk=releases · occurred_at=2026-06-13T01:38:30+00:00 · url=https://github.com/anthropics/claude-agent-sdk-python/releases/tag/v0.2.101 · 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anthropics/claude-agent-sdk-typescript v0.3.177 · signal_desk=releases · occurred_at=2026-06-13T01:25:09+00:00 · url=https://github.com/anthropics/claude-agent-sdk-typescript/releases/tag/v0.3.177 · raw={\"repo\":\"anthropics/claude-agent-sdk-typescript\"}"},{"ref":"E12","kind":"event","title":"Sr. Manager, Procurement Lease Administration ","date":"2026-06-13T00:00:51+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5253835008","signal_url":"https://onlylabs.fyi/signals/5fc8b494-51de-4bb5-9e9f-0b5d69148056","signal_json_url":"https://onlylabs.fyi/signals/5fc8b494-51de-4bb5-9e9f-0b5d69148056/signal.json","text":"job_opened · Sr. Manager, Procurement Lease Administration  · signal_desk=hiring · occurred_at=2026-06-13T00:00:51+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/5253835008 · raw={\"location\":\"San Francisco, CA | New York City, NY\",\"ats\":\"greenhouse\"}"},{"ref":"E13","kind":"event","title":"People 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anthropics/claude-code v2.1.176 · signal_desk=releases · occurred_at=2026-06-12T21:53:27+00:00 · url=https://github.com/anthropics/claude-code/releases/tag/v2.1.176 · raw={\"repo\":\"anthropics/claude-code\"}"},{"ref":"E17","kind":"event","title":"anthropics/claude-agent-sdk-typescript v0.3.176","date":"2026-06-12T21:53:27+00:00","date_source":"source","source_url":"https://github.com/anthropics/claude-agent-sdk-typescript/releases/tag/v0.3.176","signal_url":"https://onlylabs.fyi/signals/b025304c-45b6-48f1-bdc9-0e70f96a65de","signal_json_url":"https://onlylabs.fyi/signals/b025304c-45b6-48f1-bdc9-0e70f96a65de/signal.json","text":"release · anthropics/claude-agent-sdk-typescript v0.3.176 · signal_desk=releases · occurred_at=2026-06-12T21:53:27+00:00 · url=https://github.com/anthropics/claude-agent-sdk-typescript/releases/tag/v0.3.176 · raw={\"repo\":\"anthropics/claude-agent-sdk-typescript\"}"},{"ref":"E18","kind":"event","title":"Web Producer, CMS 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Technical Program Manager, API Platform · signal_desk=hiring · occurred_at=2026-06-12T17:53:07+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/5256303008 · data_radar_lanes=Infrastructure · data_radar_terms=platform · data_radar_reason=Anthropic has a job signal matching infrastructure. · raw={\"location\":\"San Francisco, CA | New York City, NY\",\"ats\":\"greenhouse\"}"},{"ref":"E25","kind":"event","title":"Strategic Account Executive, Tech","date":"2026-06-12T17:16:18+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5109135008","signal_url":"https://onlylabs.fyi/signals/4090204e-ecfe-4a8f-b407-0b6548fbb82b","signal_json_url":"https://onlylabs.fyi/signals/4090204e-ecfe-4a8f-b407-0b6548fbb82b/signal.json","text":"job_opened · Strategic Account Executive, Tech · signal_desk=hiring · occurred_at=2026-06-12T17:16:18+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/5109135008 · raw={\"location\":\"San 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occurred_at=2026-06-12T15:54:49+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/5097742008 · data_radar_lanes=Infrastructure · data_radar_terms=inference · data_radar_reason=Anthropic has a job signal matching infrastructure. · raw={\"location\":\"London, UK\",\"ats\":\"greenhouse\"}"},{"ref":"E30","kind":"event","title":"Sr. Software Engineer, Inference","date":"2026-06-12T15:54:48+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5152348008","signal_url":"https://onlylabs.fyi/signals/a78d6634-face-4771-b6b9-b71348eb50cf","signal_json_url":"https://onlylabs.fyi/signals/a78d6634-face-4771-b6b9-b71348eb50cf/signal.json","text":"job_opened · Sr. Software Engineer, Inference · signal_desk=hiring · occurred_at=2026-06-12T15:54:48+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/5152348008 · data_radar_lanes=Infrastructure · data_radar_terms=inference · data_radar_reason=Anthropic has a job signal matching 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Customer Success · signal_desk=hiring · occurred_at=2026-06-12T10:26:09+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/4989228008 · data_radar_lanes=Product and customer · data_radar_terms=customer · data_radar_reason=Anthropic has a job signal matching product and customer. · raw={\"location\":\"London, UK\",\"ats\":\"greenhouse\"}"},{"ref":"E35","kind":"event","title":"Applied AI Architect","date":"2026-06-12T06:15:38+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5076109008","signal_url":"https://onlylabs.fyi/signals/2ff581bf-149e-4074-8d94-fd32dab00ae8","signal_json_url":"https://onlylabs.fyi/signals/2ff581bf-149e-4074-8d94-fd32dab00ae8/signal.json","text":"job_opened · Applied AI Architect · signal_desk=hiring · occurred_at=2026-06-12T06:15:38+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/5076109008 · raw={\"location\":\"Tokyo, Japan\",\"ats\":\"greenhouse\"}"},{"ref":"E36","kind":"event","title":"anthropics/claude-agent-sdk-python v0.2.99","date":"2026-06-12T04:36:28+00:00","date_source":"source","source_url":"https://github.com/anthropics/claude-agent-sdk-python/releases/tag/v0.2.99","signal_url":"https://onlylabs.fyi/signals/3a97d5ff-55c9-46c3-9f46-318380d9fdd4","signal_json_url":"https://onlylabs.fyi/signals/3a97d5ff-55c9-46c3-9f46-318380d9fdd4/signal.json","text":"release · anthropics/claude-agent-sdk-python v0.2.99 · signal_desk=releases · occurred_at=2026-06-12T04:36:28+00:00 · url=https://github.com/anthropics/claude-agent-sdk-python/releases/tag/v0.2.99 · raw={\"repo\":\"anthropics/claude-agent-sdk-python\"}"},{"ref":"E37","kind":"event","title":"Strategy & Operations Lead, Marketing","date":"2026-06-12T04:33:55+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5248946008","signal_url":"https://onlylabs.fyi/signals/0fe098de-024e-44f0-a17b-1c161f85939c","signal_json_url":"https://onlylabs.fyi/signals/0fe098de-024e-44f0-a17b-1c161f85939c/signal.json","text":"job_opened · Strategy & Operations Lead, Marketing · signal_desk=hiring · occurred_at=2026-06-12T04:33:55+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/5248946008 · raw={\"location\":\"San Francisco, CA | New York City, NY\",\"ats\":\"greenhouse\"}"},{"ref":"E38","kind":"event","title":"IT Support 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signal_desk=hiring · occurred_at=2026-06-12T04:22:51+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/5169167008 · raw={\"location\":\"San Francisco, CA | New York City, NY\",\"ats\":\"greenhouse\"}"},{"ref":"E43","kind":"event","title":"anthropics/claude-agent-sdk-python v0.2.98","date":"2026-06-12T01:30:12+00:00","date_source":"source","source_url":"https://github.com/anthropics/claude-agent-sdk-python/releases/tag/v0.2.98","signal_url":"https://onlylabs.fyi/signals/5bf2e1e8-788a-4208-a604-9dc3513fd23d","signal_json_url":"https://onlylabs.fyi/signals/5bf2e1e8-788a-4208-a604-9dc3513fd23d/signal.json","text":"release · anthropics/claude-agent-sdk-python v0.2.98 · signal_desk=releases · occurred_at=2026-06-12T01:30:12+00:00 · url=https://github.com/anthropics/claude-agent-sdk-python/releases/tag/v0.2.98 · raw={\"repo\":\"anthropics/claude-agent-sdk-python\"}"},{"ref":"E44","kind":"event","title":"anthropics/claude-code-action 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signal_desk=hiring · occurred_at=2026-06-11T23:20:09+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/4979585008 · data_radar_lanes=Product and customer · data_radar_terms=product, support · data_radar_reason=Anthropic has a job signal matching product and customer. · raw={\"location\":\"New York City, NY; San Francisco, CA; Seattle, WA\",\"ats\":\"greenhouse\"}"},{"ref":"E48","kind":"event","title":"Research Engineer, Code RL (Reinforcement Learning)","date":"2026-06-11T21:49:19+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5254364008","signal_url":"https://onlylabs.fyi/signals/fc7132ab-2ca8-4b86-8de4-5ecd7b4ac685","signal_json_url":"https://onlylabs.fyi/signals/fc7132ab-2ca8-4b86-8de4-5ecd7b4ac685/signal.json","text":"job_opened · Research Engineer, Code RL (Reinforcement Learning) · signal_desk=hiring · occurred_at=2026-06-11T21:49:19+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/5254364008 · raw={\"location\":\"San Francisco, CA | New York City, NY\",\"ats\":\"greenhouse\"}"},{"ref":"E49","kind":"event","title":"Product Manager, GTM Experiences","date":"2026-06-11T20:03:44+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5254623008","signal_url":"https://onlylabs.fyi/signals/83abbee0-7226-437b-bc63-6989a4c3e1ed","signal_json_url":"https://onlylabs.fyi/signals/83abbee0-7226-437b-bc63-6989a4c3e1ed/signal.json","text":"job_opened · Product Manager, GTM Experiences · signal_desk=hiring · occurred_at=2026-06-11T20:03:44+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/5254623008 · data_radar_lanes=Product and customer · data_radar_terms=product, gtm · data_radar_reason=Anthropic has a job signal matching product and customer. · raw={\"location\":\"San Francisco, CA | New York City, NY\",\"ats\":\"greenhouse\"}"},{"ref":"E50","kind":"event","title":" Product Manager, Safeguards Rare 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Manager","date":"2026-06-11T19:46:40+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5253257008","signal_url":"https://onlylabs.fyi/signals/952bcecb-6866-4bc1-ab49-a5cdf39e787f","signal_json_url":"https://onlylabs.fyi/signals/952bcecb-6866-4bc1-ab49-a5cdf39e787f/signal.json","text":"job_opened · Field Reporting Insights Manager · signal_desk=hiring · occurred_at=2026-06-11T19:46:40+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/5253257008 · raw={\"location\":\"San Francisco, CA | New York City, NY\",\"ats\":\"greenhouse\"}"},{"ref":"E52","kind":"event","title":"Partner Enablement Lead, System 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Alliance","date":"2026-06-11T18:00:23+00:00","date_source":"sitemap.lastmod","source_url":"https://www.anthropic.com/news/dxc-anthropic-alliance","signal_url":"https://onlylabs.fyi/signals/45ec76bf-87fe-418d-8b62-182ca5a30cc0","signal_json_url":"https://onlylabs.fyi/signals/45ec76bf-87fe-418d-8b62-182ca5a30cc0/signal.json","text":"post_published · Dxc Anthropic Alliance · signal_desk=talking · occurred_at=2026-06-11T18:00:23+00:00 · url=https://www.anthropic.com/news/dxc-anthropic-alliance"},{"ref":"E54","kind":"event","title":"Engineering Manager, GRC Platform","date":"2026-06-11T17:00:34+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/anthropic/jobs/4980335008","signal_url":"https://onlylabs.fyi/signals/d4324b74-87b9-4142-8fb1-5c63cd16eada","signal_json_url":"https://onlylabs.fyi/signals/d4324b74-87b9-4142-8fb1-5c63cd16eada/signal.json","text":"job_opened · Engineering Manager, GRC Platform · signal_desk=hiring · occurred_at=2026-06-11T17:00:34+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/4980335008 · data_radar_lanes=Infrastructure · data_radar_terms=platform · data_radar_reason=Anthropic has a job signal matching infrastructure. · raw={\"location\":\"San Francisco, CA | New York City, NY | Seattle, WA\",\"ats\":\"greenhouse\"}"},{"ref":"E55","kind":"event","title":"Real Estate Project Manager","date":"2026-06-11T16:40:28+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/anthropic/jobs/4939288008","signal_url":"https://onlylabs.fyi/signals/19d54f4f-9121-4003-a48b-4d15e14a3e4e","signal_json_url":"https://onlylabs.fyi/signals/19d54f4f-9121-4003-a48b-4d15e14a3e4e/signal.json","text":"job_opened · Real Estate Project Manager · signal_desk=hiring · occurred_at=2026-06-11T16:40:28+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/4939288008 · raw={\"location\":\"San Francisco, CA\",\"ats\":\"greenhouse\"}"},{"ref":"E56","kind":"event","title":"Product Manager, Enterprise","date":"2026-06-11T15:37:53+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5253339008","signal_url":"https://onlylabs.fyi/signals/5d51f3d2-1da9-4db6-b071-35ad12bd4dd6","signal_json_url":"https://onlylabs.fyi/signals/5d51f3d2-1da9-4db6-b071-35ad12bd4dd6/signal.json","text":"job_opened · Product Manager, Enterprise · signal_desk=hiring · occurred_at=2026-06-11T15:37:53+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/5253339008 · data_radar_lanes=Product and customer · data_radar_terms=product, 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":"E57","kind":"event","title":"Policy Communications Manager","date":"2026-06-11T15:37:51+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5254582008","signal_url":"https://onlylabs.fyi/signals/eb3e4532-a048-46f2-b193-9cad1e89bdba","signal_json_url":"https://onlylabs.fyi/signals/eb3e4532-a048-46f2-b193-9cad1e89bdba/signal.json","text":"job_opened · Policy Communications Manager · signal_desk=hiring · occurred_at=2026-06-11T15:37:51+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/5254582008 · data_radar_lanes=Safety and policy · data_radar_terms=policy · data_radar_reason=Anthropic has a job signal matching safety and policy. · raw={\"location\":\"San Francisco, CA | New York City, NY\",\"ats\":\"greenhouse\"}"},{"ref":"E58","kind":"event","title":"Staff+ Software Engineer, Developer 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Intelligence","date":"2026-06-11T07:36:12+00:00","date_source":"greenhouse.updated_at","source_url":"https://job-boards.greenhouse.io/anthropic/jobs/5252342008","signal_url":"https://onlylabs.fyi/signals/de4ee57d-f2c9-49f2-bcfa-df68e41ed08a","signal_json_url":"https://onlylabs.fyi/signals/de4ee57d-f2c9-49f2-bcfa-df68e41ed08a/signal.json","text":"job_opened · Senior/Staff Security Engineer, Threat Intelligence · signal_desk=hiring · occurred_at=2026-06-11T07:36:12+00:00 · url=https://job-boards.greenhouse.io/anthropic/jobs/5252342008 · data_radar_lanes=Safety and policy · data_radar_terms=security · data_radar_reason=Anthropic has a job signal matching safety and policy. · raw={\"location\":\"Zürich, CH\",\"ats\":\"greenhouse\"}"},{"ref":"E60","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\"}"}]}