{"schema_version":"onlylabs.public_analysis_evidence.v1","title":"OpenAI 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/openai","json_url":"https://onlylabs.fyi/analysis/openai/evidence.json","generated_at":"2026-06-11T18:16:20.603Z","org":{"slug":"openai","name":"OpenAI","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/openai"},"analysis":{"url":"https://onlylabs.fyi/analysis/openai","json_url":"https://onlylabs.fyi/analysis/openai/analysis.json","generated_at":"2026-06-08T15:59:09.71+00:00"},"workflow":{"version":"synthesize-analyses","provider":null,"model":null,"agent":null,"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":36,"forks":0,"releases":12,"talking":12,"repos":0},"data_radar_lanes":{"data":4,"evals":0,"infrastructure":8,"safety":10,"product":13},"data_radar_matches":26,"stored_analysis_evidence":null,"stored_analysis_web":null,"stored_analysis_signal_desks":null,"stored_analysis_data_radar_lanes":null,"stored_analysis_data_radar_matches":null},"stored_web_provenance":null,"evidence":[{"ref":"P1","kind":"page","title":"Senior Manager, Financial Reporting ","date":"2026-06-11T03:49:00.87842+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/openai/201224fb-6d5f-4e3a-b183-25931d5c776d","signal_url":null,"signal_json_url":null,"text":"# Senior Manager, Financial Reporting \n\nTeam: Finance\n\nLocation: San Francisco\n\nEmployment type: FullTime\n\nWorkplace type: Hybrid\n\nRemote: yes\n\nPublished: 2026-05-29T22:47:26.541+00:00\n\nAbout the Team\n\nOpenAI Finance ensures the organization’s financial integrity and scalability in pursuit of our mission. Our Financial Reporting team is central to delivering accurate, timely, and GAAP-compliant financial information to internal and external stakeholders. We focus on building robust, scalable reporting systems and processes to support rapid organizational growth and complexity.\n\nAbout the Role\n\nOpenAI is hiring a Senior Manager, Financial Reporting to help build and run a best-in-class financial reporting engine in a technically complex, rapidly evolving, and high-growth environment. We are looking for a seasoned external reporting professional who pairs strong technical judgment with a roll-up-your-sleeves mindset - owning key reporting deliverables, driving rigorous tie-outs and disclosure quality, and converting ambiguity into audit-ready output. This role will collaborate closely with teams across Accounting, FP&A, Tax, Legal, and Investor Relations to improve reporting processes, streamline data flows, and elevate how financial information is communicated across stakeholders.\n\nThis role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.\n\nIn this role, you will:\n\n- Modernize reporting workflows through AI integration — Leverage ChatGPT, Codex, and proprietary AI tools to minimize manual effort and accelerate deep analysis, while maintaining the rigorous judgment and accountability required for high-quality, audit-ready outputs.\n\n- Own end-to-end financial statements and disclosures — Prepare GAAP financial statements, footnotes, and supporting schedules; maintain rollforwards, tie-outs, and internal consistency checks to ensure outputs are complete and audit-ready.\n\n- Drive audit readiness and execution — Partner with external auditors to manage day-to-day audit workflow (PBC coordination, support quality, issue resolution) and keep reporting on track with minimal rework"},{"ref":"P2","kind":"page","title":"Report from the self-organizing conference","date":"2026-06-08T15:47:18.703+00:00","date_source":null,"source_url":"https://openai.com/index/report-from-the-self-organizing-conference","signal_url":null,"signal_json_url":null,"text":"Report from the self-organizing conference | OpenAI\n[Skip to main content](#main)\nLog in\n[\n](https://openai.com/)\nSwitch to\n* [ChatGPT(opens in a new window)](https://chatgpt.com/?openaicom-did=96d3d74d-bfdd-41f8-8ace-51f93fc3f29c&openaicom_referred=true)\n* [Sora(opens in a new window)](https://sora.com/)\n* [API Platform(opens in a new window)](https://platform.openai.com/)\nOpenAI\nOctober 13, 2016\n[Company](https://openai.com/news/company-announcements/)\n# Report from the self-organizing conference\nLast week we hosted over a hundred and fifty AI practitioners in our offices for our first self-organizing conference on machine learning.\n![Report From The Self Organizing Conference](https://images.ctfassets.net/kftzwdyauwt9/b2e7e3de-7b29-463d-181fd309b4f6/eb0ee53d7947cd5df0b90acd5e8be662/report-from-the-self-organizing-conference.jpg?w=3840&q=90&fm=webp)\nLoading…\nShare\nOur first group learning experiment! Last week we hosted over a hundred and fifty AI practitioners in our offices for our first self-organizing conference on machine learning. The goal was to accelerate AI research by bringing a diverse group of people together and making it easy for them to educate each other and generate new ideas. To achieve this we sought to build an entire event around the chance hallway conversations, serendipitous lunches and inspiring encounters that people have at traditional conferences.\n![Group of people sitting in a circle talking](https://images.ctfassets.net/kftzwdyauwt9/249b5946-4c11-42e6-ad714215313d/a057c8a8eb391621de064ed3cec243af/socml_1.jpg?w=3840&q=90&fm=webp)\nThe format worked. PhD students talked to professors, hobbyists talked to full-time researchers, and designers mingled with neuroscientists; most importantly, many people left the event with new research ideas. Participants identified issues ranging from the need for a greater theoretical underpinning within robotics, to how we might make use of neuroscience to accelerate AI development, to ways to increase the diversity of the AI community.[Minutes from some of these meetings are available.⁠(opens in a new window)](https://github.com/openai/socml16/wiki)\n![Person presenting their poster at a conference](ht"},{"ref":"P3","kind":"page","title":"Report from the OpenAI hackathon","date":"2026-06-08T15:47:05.464+00:00","date_source":null,"source_url":"https://openai.com/index/hackathon-follow-up","signal_url":null,"signal_json_url":null,"text":"Report from the OpenAI hackathon | OpenAI\n\nMarch 15, 2018\n\nCompany\n\n# Report from the OpenAI hackathon\n\nOn March 3rd, we hosted our first hackathon with 100 members of the artificial intelligence community.\n\nLoading…\n\nShare\n\nOn March 3rd, we hosted our first hackathon⁠ with 100 members of the artificial intelligence community. We had over 500 RSVPs arrive within two days of announcing the event—if you didn’t make it this time, please RSVP again in the future!\n\nThank you to Cirrascale⁠ for providing GPU machines during the hackathon.\n\n## The crowd\n\nOur applicants included high schoolers, industry practitioners, engineers for nonprofits (not just at OpenAI!), researchers at universities, and more, with interests spanning healthcare to AGI. We could only accommodate one hundred people this time so we tried to pick a balanced crowd with a wide range of backgrounds and levels of experience. In particular, we strove to achieve gender balance; many attendees told us that this kind of representation made a positive difference for their experience of the hackathon.\n\n## The talks\n\nWe kicked the day off with a series of talks on OpenAI’s mission and the technical topics that we focus on in our research. Sam Altman took questions on AGI timelines, safety issues, and the importance of avoiding AI arms races. Sam described how he personally came to focus on AI safety: he saw it as an underfunded, under-explored area with the potential to impact everyone. Josh Achiam gave an introduction to reinforcement learning, which is one of our main research areas; we’ve open-sourced the slides and sample code⁠ from his talk. Ilya Sutskever talked about self-play with RL agents; for an overview of the work covered, see our recent blog⁠ posts⁠ and code⁠ releases⁠. Alec Radford provided a tutorial and survey of the many different kinds of GANs and we’ve made the tutorial code⁠ available.\n\n## The hacking\n\nAfter the talks wrapped up, the hacking began. Over the course of an 8-hour code sprint participants authored dozens of AI projects on topics ranging from safety to healthcare. Some of our favorites:\n\n- Jiale Xian, Clarence Leung, Kyle Zheng, Madeline Hawkins, and Stergios Hetelekides work"},{"ref":"P4","kind":"page","title":"Statement from the OpenAI Board of Directors on the Nonprofit Commission Report","date":"2026-06-08T15:45:40.67+00:00","date_source":null,"source_url":"https://openai.com/index/nonprofit-commission-report","signal_url":null,"signal_json_url":null,"text":"Statement from the OpenAI Board of Directors on the Nonprofit Commission Report | OpenAI\n\nJuly 17, 2025\n\n# Statement from the OpenAI Board of Directors on the Nonprofit Commission Report\n\nLoading…\n\nShare\n\nThe Board of Directors thanks the members of the independent OpenAI Nonprofit Commission for their extensive work and engagement. The Commission was convened⁠ by OpenAI in April to support “an engagement process to gather learnings and feedback from a wide range of stakeholders on how OpenAI’s philanthropy can address long-term systemic issues” and provide recommendations. The attached report⁠ reflects the Commission’s independent findings.\n\nWe appreciate the expertise that informed the Commission’s report, and are grateful to the community leaders and frontline practitioners who generously shared their perspectives throughout the process. Their contributions provide valuable input into how OpenAI’s nonprofit can best fulfill its mission of ensuring Artificial General Intelligence benefits all of humanity.\n\nAs we carry this mission forward, we remain committed to listening, learning, and building in partnership with those on the front lines of change, as part of the Board’s commitment to a nonprofit that is well-resourced, mission-led, and responsive to the needs of the communities it aims to serve.\n\n## Keep reading\n\nIndustrial policy for the Intelligence Age\n\nIndustrial policy for the Intelligence AgeGlobal AffairsApr 6, 2026\n\nOpenAI acquires TBPNCompanyApr 2, 2026\n\nOpenAI raises $122 billion to accelerate the next phase of AICompanyMar 31, 2026"},{"ref":"P5","kind":"page","title":"gpt-oss-safeguard technical report","date":"2026-06-08T15:45:31.618+00:00","date_source":null,"source_url":"https://openai.com/index/gpt-oss-safeguard-technical-report","signal_url":null,"signal_json_url":null,"text":"gpt-oss-safeguard technical report | OpenAI\n\nOctober 29, 2025\n\n# gpt-oss-safeguard technical report\n\nPerformance and baseline evaluations of gpt-oss-safeguard-120b and gpt-oss-safeguard-20b\n\nShare\n\n## Introduction\n\ngpt-oss-safeguard-120b and gpt-oss-safeguard-20b are two open-weight reasoning models post-trained from the gpt-oss models and trained to reason from a provided policy in order to label content under that policy. They are available under the Apache 2.0 license and our gpt-oss usage policy. Developed with feedback from the open-source community, these text-only models are compatible with our Responses API. The models are customizable, provide full chain-of-thought (CoT), can be used with different reasoning efforts (low, medium, high), and support Structured Outputs.\n\nIn this report, we describe gpt-oss-safeguard’s capabilities and provide our baseline safety evaluations on the gpt-oss-safeguard models, using the underlying gpt-oss models as a baseline. For more information about the development and architecture of the underlying gpt-oss models, see the original gpt-oss model model card⁠.\n\nWe recommend using these models to classify content against a provided policy, and not as the core functionality with which end users interact; the original gpt-oss models are better for those applications. The safety metrics provided below describe how gpt-oss-safeguard models function in chat settings. The gpt-oss-safeguard models are not intended for this use, but since they are open models, it is possible for someone to use the models in this way. Because of that possibility, we wanted to verify that they met our safety standards in such usage; this report shares the results of those tests. We also share an initial evaluation of multi-language performance in a chat setting; note that this does not directly assess performance during content classification with a provided policy.\n\nThe gpt-oss-safeguard models are fine-tunes of their gpt-oss counterparts, and were trained without any additional biological or cybersecurity data. As a result, we determined that the previous work estimating worst case scenarios⁠ from gpt-oss release cross applies to these new models.\n\n"},{"ref":"P6","kind":"page","title":"The state of enterprise AI","date":"2026-06-08T15:45:28.124+00:00","date_source":null,"source_url":"https://openai.com/index/the-state-of-enterprise-ai-2025-report","signal_url":null,"signal_json_url":null,"text":"The state of enterprise AI | OpenAI\n\nDecember 8, 2025\n\n# The state of enterprise AI\n\nWhat we’re learning about AI at work.\n\nRead the paper Contact sales\n\nLoading…\n\nShare\n\nChatGPT now serves more than 800 million users every week, and this rapid consumer adoption has created a powerful flywheel, accelerating the pace at which AI is being brought into work and professional settings.\n\nThe history of general purpose technologies—from steam engines to semiconductors—shows that significant economic value is created after firms translate underlying capabilities into scaled use cases. Enterprise AI now appears to be entering this phase.\n\nToday, we’re excited to introduce the state of enterprise AI report⁠. For the first time, we’re sharing a comprehensive look at how enterprises are adopting AI, what workers say they’re gaining, and how organizational leaders are turning experimentation into measurable productivity and new capabilities.\n\nThis analysis draws on two novel sources of data:\n\n- Real-world usage data from enterprise customers of OpenAI.\n- An OpenAI survey of 9,000 workers across almost 100 enterprises documenting patterns of AI adoption.\n\nAll data points were deidentified and aggregated to preserve privacy.\n\n## Adoption is accelerating and deepening\n\nThe picture that emerges is clear: enterprise AI adoption is accelerating not just in breadth, but in depth. It is reshaping how people work, how teams collaborate, and how organizations build and deliver products.\n\n- Over the past year weekly messages in ChatGPT Enterprise increased roughly 8×, and the average worker is sending 30% more messages.\n- Usage of structured workflows such as Projects and Custom GPTs has increased 19× year-to-date, showing a shift from casual querying to integrated, repeatable processes.\n- Average reasoning token consumption per organization has increased by approximately 320× in the past 12 months, suggesting that more intelligent models are being systematically integrated into expanding products and services.\n\nPeople aren’t just using AI more often. They are using it for increasingly sophisticated tasks. Enterprises are expanding both the extensive margin—more workers adopting AI—and"},{"ref":"P7","kind":"page","title":"The state of enterprise AI","date":"2026-06-08T15:45:26.611+00:00","date_source":null,"source_url":"https://openai.com/business/guides-and-resources/the-state-of-enterprise-ai-2025-report","signal_url":null,"signal_json_url":null,"text":"The state of enterprise AI | OpenAI\n\nDecember 17, 2025\n\n# The state of enterprise AI\n\nLoading…\n\nShare\n\n## Foreword\n\n#### At OpenAI, our mission is to ensure that artificial intelligence benefits all of humanity, and helping enterprises solve problems is central to this mission.\n\nThe majority of economically valuable activity takes place inside organizations, where innovation translates directly into improved outcomes for workers, customers, and other stakeholders. Enterprise problems also present the hardest technical challenges for frontier intelligence, requiring reliability, safety, and security at scale. The revenue generated from solving these problems can help fund broad, free access to powerful AI for hundreds of millions of people worldwide.\n\nFor much of the past three years, the visible impact of AI has been most apparent among consumers. However, the history of general purpose technologies—from steam engines to semiconductors—shows that significant economic value is created after firms translate underlying capabilities into scaled use cases. Enterprise AI now appears to be entering this phase, as many of the world’s largest and most complex organizations are starting to use AI as core infrastructure.\n\nMore than 1 million business customers now use OpenAI’s tools. This report brings together evidence from de-identified and aggregated enterprise usage data and a variety of other sources to provide a grounded view of how AI is being deployed inside organizations today.\n\n2025 report\n\n#### Four key findings stand out\n\nEnterprise usage is scaling, with deeper workflow integration. ChatGPT message volume grew 8x and API reasoning token consumption per organization increased 320x year-over-year, demonstrating that more enterprises are using AI and their intensity of usage has increased.\n\nEnterprises that leverage AI are experiencing measurable productivity and business impact. Enterprise users report saving 40–60 minutes per day and being able to complete new technical tasks such as data analysis and coding. Case studies indicate AI is contributing to important outcomes such as revenue growth, improved customer experience, and shorter product-development cycle"},{"ref":"P8","kind":"page","title":"Why Codex Security Doesn’t Include a SAST Report","date":"2026-06-08T15:45:18.357+00:00","date_source":null,"source_url":"https://openai.com/index/why-codex-security-doesnt-include-sast","signal_url":null,"signal_json_url":null,"text":"Why Codex Security Doesn’t Include a SAST Report | OpenAI\n\nMarch 16, 2026\n\n# Why Codex Security Doesn’t Include a SAST Report\n\nShare\n\nFor decades, static application security testing (SAST) has been one of the most effective ways security teams scale code review.\n\nBut when we built Codex Security, we made a deliberate design choice: we didn’t start by importing a static analysis report and asking the agent to triage it. We designed the system to start with the repository itself—its architecture, trust boundaries, and intended behavior—and to validate what it finds before it asks a human to spend time on it.\n\nThe reason is simple: the hardest vulnerabilities usually aren’t dataflow problems. They happen when code appears to enforce a security check, but that check doesn’t actually guarantee the property the system relies on. In other words, the challenge isn’t just tracking how data moves through a program—it’s determining whether the defenses in the code really work.\n\n## The problem: SAST is optimized for dataflow\n\nSAST is often framed as a clean pipeline: identify a source of untrusted input, track data through the program, and flag cases where that data reaches a sensitive sink without sanitization. It’s an elegant model, and it covers a lot of real bugs.\n\nIn practice, SAST has to make approximations to stay tractable at scale—especially in real codebases with indirection, dynamic dispatch, callbacks, reflection, and framework-heavy control flow. Those approximations aren’t a knock on SAST; they’re the reality of trying to reason about code without executing it.\n\nThat, by itself, is not why Codex Security doesn’t start with a SAST report.\n\nThe deeper issue is what happens after you successfully trace a source to a sink.\n\n#### Where static analysis struggles: constraints and semantics\n\nEven when static analysis correctly traces input across multiple functions and layers, it still has to answer the question that actually determines whether a vulnerability exists:\n\n#### Did the defense really work?\n\nTake a common pattern: code calls something like`sanitize_html()` before rendering untrusted content. A static analyzer can see that the sanitizer ran. What it usuall"},{"ref":"P9","kind":"page","title":"How ChatGPT adoption broadened in early 2026","date":"2026-06-08T15:45:05.712+00:00","date_source":null,"source_url":"https://openai.com/signals/research/2026q1-update","signal_url":null,"signal_json_url":null,"text":"How ChatGPT adoption broadened in early 2026 | OpenAI\n\nMay 11, 2026\n\n# How ChatGPT adoption broadened in early 2026\n\nQ1 data shows consumer adoption growth across inferred gender, age, and geography.\n\nLoading…\n\nShare\n\nIn the first quarter of 2026, consumer ChatGPT growth broadened across age groups, continued to rise among users with typically feminine names, and deepened in more countries.\n\nThis analysis covers the messages sent on ChatGPT consumer plans (Free, Go, Plus, and Pro). Because it excludes Codex and ChatGPT enterprise and education products, it understates total workplace and educational usage.\n\n## ChatGPT usage broadened beyond early adopters\n\nUsers with typically feminine names represented a growing share of ChatGPT usage this quarter after reaching approximate parity last year. These users account for over half of users for whom we’re able to infer gender (see gender inference methodology here⁠).\n\nThe number of messages from all age groups increased with ChatGPT’s overall growth⁠. In Q1, users under the age of 35 still accounted for the largest share of total messages, but messages from users over 35 gained share this quarter.\n\n## ChatGPT use spread beyond the largest and most established markets\n\nWe rank countries⁠ by the number of messages sent per capita to track relative country-level usage patterns. Many of the largest gains in rank this quarter came from countries outside the most established markets. The 10 fastest-rising countries point to a broadening pattern of adoption across Latin America and the Caribbean, Asia-Pacific, and Africa. These changes reflect relative movement, not total usage.\n\n#### Countries with the largest increases in ChatGPT messages per capita ranking\n\nCountry\n\n2025Q4 Rank\n\n2026Q1 Rank\n\nChange\n\nDominican Republic\n\n53\n\n44\n\n+9\n\nHaiti\n\n91\n\n82\n\n+9\n\nJapan\n\n43\n\n35\n\n+8\n\nMexico\n\n60\n\n54\n\n+6\n\nTanzania\n\n102\n\n96\n\n+6\n\nBrazil\n\n47\n\n42\n\n+5\n\nCosta Rica\n\n38\n\n33\n\n+5\n\nMyanmar\n\n99\n\n94\n\n+5\n\nPapua New Guinea\n\n109\n\n104\n\n+5\n\nAustria\n\n15\n\n11\n\n+4\n\n## Workplace use evolved\n\nWithin work-related usage on consumer plans, creating written and visual materials continued to lead, but decreased over time while more specialized tasks became more popula"},{"ref":"P10","kind":"page","title":"PRC-linked influence operations are targeting AI debates in the US","date":"2026-06-11T07:04:29.903+00:00","date_source":null,"source_url":"https://openai.com/index/prc-linked-influence-operations-ai-debates","signal_url":null,"signal_json_url":null,"text":"PRC-linked influence operations are targeting AI debates in the US | OpenAI\n\nJune 10, 2026\n\n# PRC-linked influence operations are targeting AI debates in the US\n\nLoading…\n\nShare\n\nOur mission is to ensure that artificial general intelligence benefits all of humanity. We advance this mission by deploying our innovations to build democratic AI: AI shaped by democratic principles, governed by common-sense rules and designed to help people solve hard problems while protecting them from real harm. That mission also requires identifying and disrupting attempts by authoritarian regimes and their proxies to use AI systems to coerce critics, surveil communities or covertly interfere in democratic societies.\n\nIn this report, we describe two clusters of ChatGPT accounts likely originating from China that we banned after they used our models in support of apparent covert influence operations that promoted narratives in an attempt to manipulate a legitimate debate about American AI and wider tech policies.\n\nThe first cluster generated social media comments and images claiming that data center buildouts for AI were increasing electricity prices for average families. We named this cluster the “Data Center Bandwagon” campaign.\n\nThe second cluster generated comments and images criticizing US tariffs as attempts to dominate technological competition and specified in their prompts that the content should not include China’s leader Xi Jinping in the output and instead include only President Trump. This cluster was connected to a network of likely inauthentic social media accounts that were also likely targeting OpenAI by claiming ChatGPT user data had been compromised. These allegations were entirely false. We named this second cluster the “Tech and Tariffs” campaign.\n\nThe targeting of OpenAI and US data center buildouts is significant not because the operation appears to have shifted public opinion, but because it shows PRC-origin influence operators testing narratives against AI infrastructure – a foundation of US technological leadership, economic growth and the broader democratic AI ecosystem. The operation sought to exploit and amplify existing public concerns about energy pric"},{"ref":"P11","kind":"page","title":"Access OpenAI models and Codex through your Oracle cloud commitment","date":"2026-06-11T07:04:29.668+00:00","date_source":null,"source_url":"https://openai.com/index/openai-on-oracle-cloud","signal_url":null,"signal_json_url":null,"text":"Access OpenAI models and Codex through your Oracle cloud commitment | OpenAI\n\nJune 10, 2026\n\n# Access OpenAI models and Codex through your Oracle cloud commitment\n\nUse your existing Oracle cloud commitment to give teams access to OpenAI’s most advanced models and Codex, without creating a new purchasing path.\n\nLoading…\n\nShare\n\nEnterprises often want to deploy AI through the procurement processes and governance frameworks they already trust. To help make that happen, OpenAI and Oracle are partnering to make OpenAI frontier models and Codex easier to access for Oracle Cloud Infrastructure (OCI) customers.\n\nIn the coming weeks, Oracle customers will be able to apply eligible Oracle Universal Credits toward OpenAI models and Codex through OCI. This gives customers a path to access OpenAI models under their existing purchasing workflow and cloud commitment.\n\nWith OpenAI models, teams can build AI applications, analyze complex information, automate workflows, and create new customer and employee experiences.\n\nFor organizations with existing Oracle commitments, the partnership can help align AI adoption with planned cloud investments and established enterprise processes. The goal is to reduce friction for teams that are ready to bring advanced AI into their businesses, while meeting customers where they already manage critical technology decisions.\n\nBy expanding access through OCI, OpenAI and Oracle are making it easier for more enterprises to move from AI ambition to production impact.\n\nAvailability will begin in the coming weeks.\n\nContact your Oracle sales representative for details, timing, and availability.\n\n## Author\n\nOpenAI\n\n## Keep reading\n\nConfidential submission of draft S-1 to the SECCompanyJun 8, 2026\n\nBuilt to benefit everyone: our planCompanyJun 8, 2026\n\nIntroducing the OpenAI Economic Research ExchangeCompanyJun 8, 2026"},{"ref":"P12","kind":"page","title":"How an astrophysicist uses Codex to help simulate black holes","date":"2026-06-11T07:04:29.452+00:00","date_source":null,"source_url":"https://openai.com/index/using-codex-to-simulate-black-holes","signal_url":null,"signal_json_url":null,"text":"How an astrophysicist uses Codex to help simulate black holes | OpenAI\n\nJune 11, 2026\n\nApplied AI\n\n# How an astrophysicist uses Codex to help simulate black holes\n\nCodex helps Chi-kwan Chan to refine and test algorithms that simulate the movement of electrons and ions around a black hole.\n\nLoading…\n\nShare\n\nThe gravity around a black hole is so extreme that nothing, not even light, can escape once it gets close enough. Astrophysicists like Chi-kwan Chan study black holes with computer simulations and observations. But current algorithms and computing power limit how realistic those simulations can be.\n\nWith Codex, Chan—a researcher at the University of Arizona and Steward Observatory—is tackling this problem.\n\nBlack holes are among the best places to test Einstein’s general theory of relativity, he said. The theory is currently our best explanation of gravity: instead of a force pulling objects together, gravity is the result of mass and energy bending the fabric of space and time.\n\nChan is part of the international Event Horizon Telescope (EHT) collaboration, which published the first image of a black hole in 2019. The team is currently gathering observations to produce the first video of a supermassive black hole, focusing on the one at the center of the M87 galaxy.\n\nBut turning observations into scientific understanding requires enormous amounts of data processing, large-scale computing workflows, and simulations capable of modeling some of the most extreme physics in the universe.\n\nSince light can’t escape a black hole, scientists instead study the region around it called the event horizon, a boundary beyond which matter can’t escape. “It’s a surface of no return,” said Chan. Matter swirling just outside this boundary emits light that astrophysicists can see, measure, and simulate.\n\nThe 2019 image released by the EHT showed a black hole’s shadow embedded in glowing plasma near the event horizon. Chan helped develop the simulation and computing tools the team used to interpret the observations. Since then, Chan and his colleagues have continued improving their instruments and observing capabilities as the team moves from still images toward videos.\n\nA short vi"},{"ref":"P13","kind":"page","title":"openai/codex rust-v0.140.0-alpha.8","date":"2026-06-11T07:04:06.038708+00:00","date_source":null,"source_url":"https://github.com/openai/codex/releases/tag/rust-v0.140.0-alpha.8","signal_url":null,"signal_json_url":null,"text":"# 0.140.0-alpha.8\n\nRepository: openai/codex\n\nTag: rust-v0.140.0-alpha.8\n\nPublished: 2026-06-11T06:12:14Z\n\nPrerelease: yes\n\nRelease notes:\nRelease 0.140.0-alpha.8"},{"ref":"P14","kind":"page","title":"openai/codex rust-v0.140.0-alpha.4","date":"2026-06-11T07:04:05.49969+00:00","date_source":null,"source_url":"https://github.com/openai/codex/releases/tag/rust-v0.140.0-alpha.4","signal_url":null,"signal_json_url":null,"text":"# 0.140.0-alpha.4\n\nRepository: openai/codex\n\nTag: rust-v0.140.0-alpha.4\n\nPublished: 2026-06-10T19:46:17Z\n\nPrerelease: yes\n\nRelease notes:\nRelease 0.140.0-alpha.4"},{"ref":"P15","kind":"page","title":"openai/openai-python v2.41.1","date":"2026-06-11T07:04:05.474075+00:00","date_source":null,"source_url":"https://github.com/openai/openai-python/releases/tag/v2.41.1","signal_url":null,"signal_json_url":null,"text":"# v2.41.1\n\nRepository: openai/openai-python\n\nTag: v2.41.1\n\nPublished: 2026-06-10T16:09:44Z\n\nPrerelease: no\n\nRelease notes:\n## 2.41.1 (2026-06-05)\n\nFull Changelog: [v2.41.0...v2.41.1](https://github.com/openai/openai-python/compare/v2.41.0...v2.41.1)\n\n### Build System\n\n* Remove scheduled release workflow trigger ([#3366](https://github.com/openai/openai-python/issues/3366)) ([2a91011](https://github.com/openai/openai-python/commit/2a91011abc21032db9566b98068afefb5fbb9b24))"},{"ref":"P16","kind":"page","title":"openai/codex rust-v0.140.0-alpha.7","date":"2026-06-11T07:04:05.166333+00:00","date_source":null,"source_url":"https://github.com/openai/codex/releases/tag/rust-v0.140.0-alpha.7","signal_url":null,"signal_json_url":null,"text":"# 0.140.0-alpha.7\n\nRepository: openai/codex\n\nTag: rust-v0.140.0-alpha.7\n\nPublished: 2026-06-10T22:45:37Z\n\nPrerelease: yes\n\nRelease notes:\nRelease 0.140.0-alpha.7"},{"ref":"P17","kind":"page","title":"openai/openai-agents-python v0.17.5","date":"2026-06-11T07:04:04.915614+00:00","date_source":null,"source_url":"https://github.com/openai/openai-agents-python/releases/tag/v0.17.5","signal_url":null,"signal_json_url":null,"text":"# v0.17.5\n\nRepository: openai/openai-agents-python\n\nTag: v0.17.5\n\nPublished: 2026-06-11T04:11:51Z\n\nPrerelease: no\n\nRelease notes:\n## What's Changed\n\n* fix: expose sandbox error retryability by @qiyaoq-oai in https://github.com/openai/openai-agents-python/pull/3581\n* fix: #3512 type tool-end hook results as object by @seratch in https://github.com/openai/openai-agents-python/pull/3518\n* fix: use tuple form for SpeechGroupSpanData `__slots__` by @jluocsa in https://github.com/openai/openai-agents-python/pull/3534\n* chore: bump Modal sandbox extra to 1.4.3 by @jdoughty04 in https://github.com/openai/openai-agents-python/pull/3538\n\n### Documentation & Other Changes\n\n* docs: add MongoDB session example under examples/memory by @alexbevi in https://github.com/openai/openai-agents-python/pull/3036\n* docs: add missing space in MCP params docstrings by @jluocsa in https://github.com/openai/openai-agents-python/pull/3535\n* docs: fix docstring typo in stdio params env description by @mshsheikh in https://github.com/openai/openai-agents-python/pull/3557\n* docs: fix two docstring grammar errors in tool.py by @jluocsa in https://github.com/openai/openai-agents-python/pull/3543\n* docs: add Latitude to external tracing processors list by @guillemwilly in https://github.com/openai/openai-agents-python/pull/3577\n* docs: tweak in usage doc by @mshsheikh in https://github.com/openai/openai-agents-python/pull/3597\n* docs: fix string concatenation typo in agent instructions by @mshsheikh in https://github.com/openai/openai-agents-python/pull/3599\n* docs: capitalization fix in tracing docs by @mshsheikh in https://github.com/openai/openai-agents-python/pull/3602\n* docs: fix subject-verb agreement in agent loop description by @mshsheikh in https://github.com/openai/openai-agents-python/pull/3605\n* test: add unit tests for run_demo_loop streaming, EOF, and empty-input paths by @jluocsa in https://github.com/openai/openai-agents-python/pull/3542\n* test: add unit tests for _openai_retry helpers (77% -> 95% coverage) by @jluocsa in https://github.com/openai/openai-agents-python/pull/3544\n* Release 0.17.5 by @github-actions[bot] in https://github.com/openai/openai-agents-python/pull/3619\n\n#"},{"ref":"P18","kind":"page","title":"openai/swarm repository metadata","date":"2026-06-11T04:02:10.59668+00:00","date_source":null,"source_url":"https://github.com/openai/swarm","signal_url":null,"signal_json_url":null,"text":"# openai/swarm\n\nDescription: Educational framework exploring ergonomic, lightweight multi-agent orchestration. Managed by OpenAI Solution team.\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 21610\n\nForks: 2303\n\nOpen issues: 31\n\nCreated: 2024-02-22T20:53:54Z\n\nPushed: 2026-04-15T17:10:28Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n![Swarm Logo](assets/logo.png)\n\n# Swarm (experimental, educational)\n\n> [!IMPORTANT]\n> Swarm is now replaced by the [OpenAI Agents SDK](https://github.com/openai/openai-agents-python), which is a production-ready evolution of Swarm. The Agents SDK features key improvements and will be actively maintained by the OpenAI team.\n>\n> We recommend migrating to the Agents SDK for all production use cases.\n\n## Install\n\nRequires Python 3.10+\n\n```shell\npip install git+ssh://git@github.com/openai/swarm.git\n```\n\nor\n\n```shell\npip install git+https://github.com/openai/swarm.git\n```\n\n## Usage\n\n```python\nfrom swarm import Swarm, Agent\n\nclient = Swarm()\n\ndef transfer_to_agent_b():\nreturn agent_b\n\nagent_a = Agent(\nname=\"Agent A\",\ninstructions=\"You are a helpful agent.\",\nfunctions=[transfer_to_agent_b],\n)\n\nagent_b = Agent(\nname=\"Agent B\",\ninstructions=\"Only speak in Haikus.\",\n)\n\nresponse = client.run(\nagent=agent_a,\nmessages=[{\"role\": \"user\", \"content\": \"I want to talk to agent B.\"}],\n)\n\nprint(response.messages[-1][\"content\"])\n```\n\n```\nHope glimmers brightly,\nNew paths converge gracefully,\nWhat can I assist?\n```\n\n## Table of Contents\n\n- [Overview](#overview)\n- [Examples](#examples)\n- [Documentation](#documentation)\n- [Running Swarm](#running-swarm)\n- [Agents](#agents)\n- [Functions](#functions)\n- [Streaming](#streaming)\n- [Evaluations](#evaluations)\n- [Utils](#utils)\n\n# Overview\n\nSwarm focuses on making agent **coordination** and **execution** lightweight, highly controllable, and easily testable.\n\nIt accomplishes this through two primitive abstractions: `Agent`s and **handoffs**. An `Agent` encompasses `instructions` and `tools`, and can at any point choose to hand off a conversation to another `Agent`.\n\nThese primitives are powerful enough to express rich dynamics between tools and networks of agents, allowing you to build scalable, real-world solution"},{"ref":"P19","kind":"page","title":"openai/vetu repository metadata","date":"2026-06-11T04:02:10.242546+00:00","date_source":null,"source_url":"https://github.com/openai/vetu","signal_url":null,"signal_json_url":null,"text":"# openai/vetu\n\nDescription: Create, publish and virtualize ephemeral Linux VMs with ease\n\nLanguage: Go\n\nLicense: NOASSERTION\n\nStars: 255\n\nForks: 8\n\nOpen issues: 7\n\nCreated: 2023-09-04T10:59:11Z\n\nPushed: 2026-06-09T03:13:38Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# **vetu** - Virtualization that is Easy To Use\n\n_vetu_ is virtualization toolset to effortlessly run [Cloud Hypervisor](https://www.cloudhypervisor.org/)-backed virtual machines on Linux hosts.\n\nWe say effortlessly, because the existing virtualization solutions like the traditional [QEMU](https://www.qemu.org/) and the new-wave [Firecracker](https://firecracker-microvm.github.io/) and Cloud Hypervisor provide lots of options and require users to essentially build a tooling on top of them to be able to simply run a basic VM.\n\nVetu builds on the success of [Tart](https://tart.run/) and abstracts all these peculiarities and makes the virtualization as easy as running containers.\n\nHere are just some of the cool features that Vetu inherited from Tart:\n\n* Ability to easily distribute VM images by integrating with OCI-compatible container registries. Push and pull virtual machines like they are containers. Cirrus Labs publishes some VM images [here](https://github.com/orgs/cirruslabs/packages?repo_name=linux-image-templates \"Cirrus Labs Linux image templates\").\n* Effortless SSH'ing into VMs (see [Usage](#usage) for an example)\n* [Cirrus CLI](https://github.com/cirruslabs/cirrus-cli) integration\n\n## Installation\n\n* [Debian-based distributions](INSTALL.md#debian-based-distributions) (Debian, Ubuntu, etc.)\n* [RPM-based distributions](INSTALL.md#rpm-based-distributions) (Fedora, CentOS, etc.)\n* [Prebuilt Binary](INSTALL.md#prebuilt-binary)\n* [From Source](INSTALL.md#from-source)\n\n## Usage\n\nTry running a Vetu VM on your Linux machine with `arm64` processor:\n\n```shell\nvetu clone ghcr.io/cirruslabs/ubuntu:latest ubuntu\nvetu run ubuntu\n```\n\nThe default username is `admin` and password is `admin`. The machine is only reachable from the localhost with the default configuration, and you can connect to it over SSH using the following command:\n\n```shell\nssh admin@$(vetu ip ubuntu)\n```\n\n## Networking options"},{"ref":"P20","kind":"page","title":"openai/dalle3-eval-samples repository metadata","date":"2026-06-11T04:02:10.124921+00:00","date_source":null,"source_url":"https://github.com/openai/dalle3-eval-samples","signal_url":null,"signal_json_url":null,"text":"# openai/dalle3-eval-samples\n\nDescription: Text-to-image samples collected for the evaluation of DALL-E 3 in the whitepaper.\n\nLicense: MIT\n\nStars: 69\n\nForks: 13\n\nOpen issues: 0\n\nCreated: 2023-10-16T15:12:37Z\n\nPushed: 2023-10-17T17:15:08Z\n\nDefault branch: main\n\nFork: no\n\nArchived: yes\n\nREADME:\n# DALL-E 3 Evaluation Samples\n\nThis repository contains text-to-image samples collected for the evaluations of DALL-E 3 in the whitepaper. We provide samples not only from DALL-E 3, but from the competitors we compare against in the paper. \n\nThe intent of this repository is to enable researchers in the text-to-image space to reproduce our results and foster forward progress of the text-to-image field as a whole. The samples from this repository are *not* meant to be demonstrations of the DALL-E 3 system.\n\n## Structure\n\nThere are six directories in this repository:\n\n### coco\n\nContains ~32,000 samples from each model derived from ~8,000 captions from the MSCOCO 2014 evaluation set. These samples are intended to be used for CLIP score calculation.\n\n### drawbench\n\nContains 4 samples for each prompt from the [drawbench dataset](https://imagen.research.google/) for each model. In the paper, we evaluate these samples using GPT-4 with Vision and using human raters.\n\n### drawbench_upsampled\n\nContains 4 samples for each prompt in our upsampled drawbench dataset, which was derived using the caption upsampling methodology described in the paper. We evaluate these samples using GPT-4 with Vision.\n\n### prompts\n\nContains the prompts used to generate all of the samples in the other directories. Prompt files are simple text files. The order of the prompts in these files corresponds with the order of the respective image samples.\n\n### t2i_compbench\n\nContains 4 samples for each prompt in the [T2I CompBench evaluation](https://github.com/Karine-Huang/T2I-CompBench). We use the scripts provided with that evaluation to measure the performance of the models in our comparison."},{"ref":"P21","kind":"page","title":"openai/web-crawl-q-and-a-example repository metadata","date":"2026-06-11T04:02:09.962153+00:00","date_source":null,"source_url":"https://github.com/openai/web-crawl-q-and-a-example","signal_url":null,"signal_json_url":null,"text":"# openai/web-crawl-q-and-a-example\n\nDescription: Learn how to crawl your website and build a Q/A bot with the OpenAI API\n\nLanguage: Jupyter Notebook\n\nStars: 322\n\nForks: 179\n\nOpen issues: 12\n\nCreated: 2023-10-02T18:37:39Z\n\nPushed: 2024-07-23T11:53:25Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Web Q&A with Embeddings\n\nLearn how to crawl your website and build a Q/A bot with the OpenAI API. You can find the full tutorial in the [OpenAI documentation](https://platform.openai.com/docs/tutorials/web-qa-embeddings)."},{"ref":"P22","kind":"page","title":"openai/consistencydecoder repository metadata","date":"2026-06-11T04:02:09.141682+00:00","date_source":null,"source_url":"https://github.com/openai/consistencydecoder","signal_url":null,"signal_json_url":null,"text":"# openai/consistencydecoder\n\nDescription: Consistency Distilled Diff VAE\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 2214\n\nForks: 80\n\nOpen issues: 19\n\nCreated: 2023-11-02T13:06:36Z\n\nPushed: 2023-11-07T11:21:38Z\n\nDefault branch: main\n\nFork: no\n\nArchived: yes\n\nREADME:\n# Consistency Decoder\n\n[[DALL·E 3]](https://openai.com/dall-e-3)\n[[Improving Image Generation with Better Captions]](https://cdn.openai.com/papers/dall-e-3.pdf)\n[[Consistency Models]](https://arxiv.org/abs/2303.01469)\n\nImproved decoding for stable diffusion vaes.\n\n## Installation\n\n```\n$ pip install git+https://github.com/openai/consistencydecoder.git\n```\n\n## Usage\n\n```python\nimport torch\nfrom diffusers import StableDiffusionPipeline\nfrom consistencydecoder import ConsistencyDecoder, save_image, load_image\n\n# encode with stable diffusion vae\npipe = StableDiffusionPipeline.from_pretrained(\n\"runwayml/stable-diffusion-v1-5\", torch_dtype=torch.float16, device=\"cuda:0\"\n)\npipe.vae.cuda()\ndecoder_consistency = ConsistencyDecoder(device=\"cuda:0\") # Model size: 2.49 GB\n\nimage = load_image(\"assets/gt1.png\", size=(256, 256), center_crop=True)\nlatent = pipe.vae.encode(image.half().cuda()).latent_dist.mean\n\n# decode with gan\nsample_gan = pipe.vae.decode(latent).sample.detach()\nsave_image(sample_gan, \"gan.png\")\n\n# decode with vae\nsample_consistency = decoder_consistency(latent)\nsave_image(sample_consistency, \"con.png\")\n```\n\n## Examples\nOriginal Image | GAN Decoder | Consistency Decoder |\n:---:|:---:|:---:|\n![Original Image](assets/gt1.png) | ![GAN Image](assets/gan1.png) | ![VAE Image](assets/con1.png) |\n![Original Image](assets/gt2.png) | ![GAN Image](assets/gan2.png) | ![VAE Image](assets/con2.png) |\n![Original Image](assets/gt3.png) | ![GAN Image](assets/gan3.png) | ![VAE Image](assets/con3.png) |"},{"ref":"P23","kind":"page","title":"openai/openai-deno-build repository metadata","date":"2026-06-11T04:02:09.048833+00:00","date_source":null,"source_url":"https://github.com/openai/openai-deno-build","signal_url":null,"signal_json_url":null,"text":"# openai/openai-deno-build\n\nDescription: Deno build of the official Typescript library for the OpenAI API.\n\nLanguage: TypeScript\n\nLicense: Apache-2.0\n\nStars: 145\n\nForks: 27\n\nOpen issues: 0\n\nCreated: 2023-10-27T18:37:13Z\n\nPushed: 2024-10-30T16:22:45Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# OpenAI Node API Library - Deno build\n\nThis is a build produced from https://github.com/openai/openai-node – please go\nthere to read the source and docs, file issues, etc.\n\nUsage:\n\n```ts\nimport OpenAI from \"https://deno.land/x/openai@v4.69.0/mod.ts\";\n\nconst client = new OpenAI();\n```\n\nNote that in most Deno environments, you can also do this:\n\n```ts\nimport OpenAI from \"npm:openai\";\n```"},{"ref":"P24","kind":"page","title":"openai/transformer-debugger repository metadata","date":"2026-06-11T04:02:08.899761+00:00","date_source":null,"source_url":"https://github.com/openai/transformer-debugger","signal_url":null,"signal_json_url":null,"text":"# openai/transformer-debugger\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 4116\n\nForks: 239\n\nOpen issues: 10\n\nCreated: 2024-03-11T23:06:25Z\n\nPushed: 2026-04-15T17:10:05Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Transformer Debugger\n\nTransformer Debugger (TDB) is a tool developed by OpenAI's [Superalignment\nteam](https://openai.com/blog/introducing-superalignment) with the goal of\nsupporting investigations into specific behaviors of small language models. The tool combines\n[automated interpretability](https://openai.com/research/language-models-can-explain-neurons-in-language-models)\ntechniques with [sparse autoencoders](https://transformer-circuits.pub/2023/monosemantic-features).\n\nTDB enables rapid exploration before needing to write code, with the ability to intervene in the\nforward pass and see how it affects a particular behavior. It can be used to answer questions like,\n\"Why does the model output token A instead of token B for this prompt?\" or \"Why does attention head\nH attend to token T for this prompt?\" It does so by identifying specific components (neurons,\nattention heads, autoencoder latents) that contribute to the behavior, showing automatically\ngenerated explanations of what causes those components to activate most strongly, and tracing\nconnections between components to help discover circuits.\n\nThese videos give an overview of TDB and show how it can be used to investigate [indirect object\nidentification in GPT-2 small](https://arxiv.org/abs/2211.00593):\n\n- [Introduction](https://www.loom.com/share/721244075f12439496db5d53439d2f84?sid=8445200e-c49e-4028-8b8e-3ea8d361dec0)\n- [Neuron viewer pages](https://www.loom.com/share/21b601b8494b40c49b8dc7bfd1dc6829?sid=ee23c00a-9ede-4249-b9d7-c2ba15993556)\n- [Example: Investigating name mover heads, part 1](https://www.loom.com/share/3478057cec484a1b85471585fef10811?sid=b9c3be4b-7117-405a-8d31-0f9e541dcfb6)\n- [Example: Investigating name mover heads, part 2](https://www.loom.com/share/6bd8c6bde84b42a98f9a26a969d4a3ad?sid=4a09ac29-58a2-433e-b55d-762414d9a7fa)\n\n## What's in the release?\n\n- [Neuron viewer](neuron_viewer/README.md): A React app that hosts TDB as well as pages with information about indiv"},{"ref":"P25","kind":"page","title":"openai/bugbounty-gpt repository metadata","date":"2026-06-11T04:02:08.897111+00:00","date_source":null,"source_url":"https://github.com/openai/bugbounty-gpt","signal_url":null,"signal_json_url":null,"text":"# openai/bugbounty-gpt\n\nDescription: A helpful gpt-based triage tool for BugCrowd bugbounty programs.\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 54\n\nForks: 15\n\nOpen issues: 4\n\nCreated: 2023-10-27T22:41:24Z\n\nPushed: 2026-03-26T03:23:37Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# BugCrowd GPT Classifier\n\nThis project is a system designed to manage, classify, and process BugCrowd submissions using OpenAI, and a Postgres Database for data storage. It provides an automatic handling mechanism for submissions and can be run directly or within a Docker container.\n\n## Table of Contents\n\n- [BugCrowd GPT Clasifier](#bugcrowd-gpt-clasifier)\n- [Table of Contents](#table-of-contents)\n- [Prerequisites](#prerequisites)\n- [Getting Started](#getting-started)\n- [Configuration](#configuration)\n- [`config.yaml` File](#configyaml-file)\n- [API Settings](#api-settings)\n- [User Settings](#user-settings)\n- [Categories](#categories)\n- [OpenAI Prompt](#openai-prompt)\n- [Environment Variables](#environment-variables)\n- [Docker Compose](#docker-compose)\n- [Environment Variables for Docker Compose](#environment-variables-for-docker-compose)\n\n## Prerequisites\n\nBefore starting, make sure you have the following installed:\n\n- Docker\n- Docker Compose\n\n## Getting Started\nAs it stands, there is a Dockerfile within the root directory of this repository that, upon being built, will download all depenedencies and will package everything up for usage. After configuring the application and setting all sensitive environment variables, you can run this Dockerfile and start classifying and triaging results immediately.\n\nTo actually test your configuration, it is reccomended that you take utilize docker-compose to quickly spin-up and clear-out an ephermal database. Connect locally to the database with `psql` and inspect classifications.\n\nIf Docker isn't your cup of tea, you can also install the requirements locally and run the classifier/responder via `python -m bugbounty_gpt`\n\n### Configuration\n\nConfiguration of the application involves setting up the `config.yaml` file as well as certain environment variables. Below are detailed descriptions of each configuration option. The current `config.yaml"},{"ref":"P26","kind":"page","title":"openai/interactive-textbook-demo repository metadata","date":"2026-06-11T04:02:07.614076+00:00","date_source":null,"source_url":"https://github.com/openai/interactive-textbook-demo","signal_url":null,"signal_json_url":null,"text":"# openai/interactive-textbook-demo\n\nDescription: Interactive Textbook Demo\n\nLanguage: TypeScript\n\nLicense: MIT\n\nStars: 55\n\nForks: 18\n\nOpen issues: 0\n\nCreated: 2023-11-08T21:33:47Z\n\nPushed: 2025-10-28T22:46:48Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\nhttps://github.com/openai/interactive-textbook-demo/assets/5464875/02abe187-2357-49f1-a9bb-cb8e800f1fb6\n\n# Interactive textbook demo\n\nThis repository contains the code for an interactive textbook demo that showcases how OpenAI's technologies can be used to make it more accessible to people with visual disabilities or language and learning barriers.\n\n## Development\n\nCreate `.env.local` file in the project root and copy the contents of `.env.example` into it. You can create an OpenAI API key here: [https://platform.openai.com/api-keys](https://platform.openai.com/api-keys)\n\nInstall dependencies:\n```\nnpm install\n```\n\nRun development environment:\n```\nnpm run dev\n```\n\n## Using the `trufflehog` Pre-Commit Hook\nThis repository includes a pre-commit hook that uses the `trufflehog` tool to scan your code for secrets before each commit. This helps prevent secrets, such as API keys and passwords, from being accidentally committed to the repository.\n\n### Prerequisites\nInstall `pre-commit` by running:\n```bash\npip3 install pre-commit\n```\nBefore you can use the `trufflehog` pre-commit hook, you need to have the `trufflehog` tool installed. You can install it using the following command:\n```bash\nbrew install trufflehog\n```\nOnce you have both tools installed, you can run `pre-commit install` to install the pre-commit hooks in your repository:\n\n### Using the Pre-Commit Hook\nOnce you have the `trufflehog` tool installed and have added the patterns you want to search for (OAI keys added by default), you can use the pre-commit hook to automatically scan your code before each commit. To use the pre-commit hook, simply run the `git commit` command as you normally would. \n\nThe `trufflehog` tool will automatically scan your code for secrets and reject the commit if any are found. If any secrets are found, you will be prompted to remove them before trying."},{"ref":"P27","kind":"page","title":"openai/weak-to-strong repository metadata","date":"2026-06-11T04:02:07.44151+00:00","date_source":null,"source_url":"https://github.com/openai/weak-to-strong","signal_url":null,"signal_json_url":null,"text":"# openai/weak-to-strong\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 2554\n\nForks: 313\n\nOpen issues: 12\n\nCreated: 2023-12-13T23:53:13Z\n\nPushed: 2024-05-19T23:30:56Z\n\nDefault branch: main\n\nFork: no\n\nArchived: yes\n\nREADME:\n**STATUS**: This codebase is not well tested and does not use the exact same settings we used in the paper, but in our experience gives qualitatively similar results when using large model size gaps and multiple seeds. Expected results can be found for two datasets below.\n\n# Weak-to-strong generalization\n\n![Our setup and how it relates to superhuman AI alignment](./weak-to-strong-setup.png)\n\nThis project contains code for implementing our [paper on weak-to-strong generalization](https://cdn.openai.com/papers/weak-to-strong-generalization.pdf).\n\nThe primary codebase contains a re-implementation of our weak-to-strong learning setup for binary classification tasks. The codebase contains code for fine-tuning pretrained language models, and also training against the labels from another language model. We support various losses described in the paper as well, such as the confidence auxiliary loss.\n\nThe `vision` directory contains stand-alone code for weak-to-strong in the vision models setting (AlexNet -> DINO on ImageNet).\n\n### Getting Started\n\nThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes.\n\n#### Installation\n\nYou need to have Python installed on your machine. The project uses `pyproject.toml` to manage dependencies. To install the dependencies, you can use a package manager like `pip`:\n\n```\npip install .\n```\n\n#### Running the Script\n\nThe main script of the project is `sweep.py`. It can be run from the command line using the following command:\n```\npython sweep.py --model_sizes=gpt2,gpt2-medium\n```\n\nIn addition to `--model_sizes`, `sweep.py` takes in almost all of the arguments that `train_simple.py` takes (e.g.\n`--batch_size`, `--n_docs`, `--n_test_docs` etc., see `train_simple.py` for a full list). These arguments are simply\nforwarded to `train_simple.py`.\n\n`sweep.py` calls `train_simple.py` in the following way:\n1. First, it calls `train_simple.py` for each model size to tr"},{"ref":"P28","kind":"page","title":"openai/openai-security-bots repository metadata","date":"2026-06-11T04:02:07.154238+00:00","date_source":null,"source_url":"https://github.com/openai/openai-security-bots","signal_url":null,"signal_json_url":null,"text":"# openai/openai-security-bots\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 390\n\nForks: 35\n\nOpen issues: 3\n\nCreated: 2024-01-11T01:24:16Z\n\nPushed: 2026-04-15T17:03:18Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# OpenAI Security Bots 🤖\n\nSlack bots integrated with OpenAI APIs to streamline security team's workflows.\n\nAll the bots can be found under `bots/` directory.\n\n```\nshared/\nopenai-slackbot/\nbots/\ntriage-slackbot/\nincident-response-slackbot/\nsdlc-slackbot/\n```\n\nRefer to each bot's README for more information and setup instruction.\n\nIf you wish to contribute, note this repo uses pre-commit to help. In this directory, run:\n```\npip install pre-commit\npre-commit install\n```"},{"ref":"E1","kind":"event","title":"A blueprint for democratic governance of frontier AI","date":"2026-06-03T10:00:00+00:00","date_source":"rss.item_date","source_url":"https://openai.com/index/frontier-safety-blueprint","signal_url":"https://onlylabs.fyi/signals/d4784f6e-9bc9-47f7-9461-aa50798e190d","signal_json_url":"https://onlylabs.fyi/signals/d4784f6e-9bc9-47f7-9461-aa50798e190d/signal.json","text":"post_published · A blueprint for democratic governance of frontier AI · signal_desk=talking · occurred_at=2026-06-03T10:00:00+00:00 · url=https://openai.com/index/frontier-safety-blueprint · hn=16 points/3 comments · data_radar_lanes=Safety and policy · data_radar_terms=safety, security · data_radar_reason=OpenAI has a writing signal matching safety and policy. · raw={\"excerpt\":\"OpenAI outlines a blueprint for U.S. governance of frontier AI, proposing a federal framework for safety, resilience, and national security.\"}"},{"ref":"E2","kind":"event","title":"Dreaming: Better memory for a more helpful ChatGPT","date":"2026-06-04T09:00:00+00:00","date_source":"rss.item_date","source_url":"https://openai.com/index/chatgpt-memory-dreaming","signal_url":"https://onlylabs.fyi/signals/96c4225c-0a6f-4219-80d9-f48f5583fdb0","signal_json_url":"https://onlylabs.fyi/signals/96c4225c-0a6f-4219-80d9-f48f5583fdb0/signal.json","text":"post_published · Dreaming: Better memory for a more helpful ChatGPT · signal_desk=talking · occurred_at=2026-06-04T09:00:00+00:00 · url=https://openai.com/index/chatgpt-memory-dreaming · hn=9 points/0 comments · raw={\"excerpt\":\"ChatGPT introduces a new memory system to better remember preferences, keeping context fresh and relevant across conversations.\"}"},{"ref":"E3","kind":"event","title":"Industrial policy for the Intelligence 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openai/openai-agents-python v0.17.5 · signal_desk=releases · occurred_at=2026-06-11T04:11:51+00:00 · url=https://github.com/openai/openai-agents-python/releases/tag/v0.17.5 · raw={\"repo\":\"openai/openai-agents-python\"}"},{"ref":"E6","kind":"event","title":"AI Tooling & Enablement Manager","date":"2026-06-11T00:07:19.266+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/openai/7e0b52d7-c7f3-475d-a589-f9ea4420fbb3","signal_url":"https://onlylabs.fyi/signals/089303ed-da2e-4429-bb4f-431e073f20c6","signal_json_url":"https://onlylabs.fyi/signals/089303ed-da2e-4429-bb4f-431e073f20c6/signal.json","text":"job_opened · AI Tooling & Enablement Manager · signal_desk=hiring · occurred_at=2026-06-11T00:07:19.266+00:00 · url=https://jobs.ashbyhq.com/openai/7e0b52d7-c7f3-475d-a589-f9ea4420fbb3 · data_radar_lanes=Infrastructure · data_radar_terms=tooling · data_radar_reason=OpenAI has a job signal matching infrastructure. · raw={\"location\":\"San Francisco\",\"team\":\"Marketing\",\"ats\":\"ashby\"}"},{"ref":"E7","kind":"event","title":"Supporting Europe’s work in ensuring a trustworthy AI ecosystem ","date":"2026-06-11T00:00:00+00:00","date_source":"rss.item_date","source_url":"https://openai.com/index/supporting-eu-trustworthy-ai-ecosystem","signal_url":"https://onlylabs.fyi/signals/b3668d3b-26d2-40c0-9d4f-ed1a67927aa4","signal_json_url":"https://onlylabs.fyi/signals/b3668d3b-26d2-40c0-9d4f-ed1a67927aa4/signal.json","text":"post_published · Supporting Europe’s work in ensuring a trustworthy AI ecosystem  · signal_desk=talking · occurred_at=2026-06-11T00:00:00+00:00 · url=https://openai.com/index/supporting-eu-trustworthy-ai-ecosystem · data_radar_lanes=Safety and policy, Product and customer · data_radar_terms=trust, support · data_radar_reason=OpenAI has a writing signal matching safety and policy, product and customer. · raw={\"excerpt\":\"OpenAI supports the EU Code of Practice on AI content transparency, advancing provenance standards and tools to help people understand AI-generated content.\"}"},{"ref":"E8","kind":"event","title":"How an astrophysicist uses Codex to help simulate black holes","date":"2026-06-11T00:00:00+00:00","date_source":"rss.item_date","source_url":"https://openai.com/index/using-codex-to-simulate-black-holes","signal_url":"https://onlylabs.fyi/signals/2638c0a7-b372-409c-ac72-f6d81d6464dc","signal_json_url":"https://onlylabs.fyi/signals/2638c0a7-b372-409c-ac72-f6d81d6464dc/signal.json","text":"post_published · How an astrophysicist uses Codex to help simulate black holes · signal_desk=talking · occurred_at=2026-06-11T00:00:00+00:00 · url=https://openai.com/index/using-codex-to-simulate-black-holes · raw={\"excerpt\":\"Discover how astrophysicist Chi-kwan Chan uses Codex to build black hole simulations, helping scientists study extreme physics and test Einstein’s theory of general relativity.\"}"},{"ref":"E9","kind":"event","title":"openai/codex 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signal_desk=hiring · occurred_at=2026-06-10T20:46:02.181+00:00 · url=https://jobs.ashbyhq.com/openai/4e60d650-47a7-44fb-8031-02976c7ddc55 · raw={\"location\":\"San Francisco\",\"team\":\"Applied AI Engineering\",\"ats\":\"ashby\"}"},{"ref":"E11","kind":"event","title":"Sourcer, Go To Market","date":"2026-06-10T20:21:21.453+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/openai/49544169-4346-48bd-a926-b1188eba7254","signal_url":"https://onlylabs.fyi/signals/bdd02b71-32f0-491c-863f-d29538e31f6e","signal_json_url":"https://onlylabs.fyi/signals/bdd02b71-32f0-491c-863f-d29538e31f6e/signal.json","text":"job_opened · Sourcer, Go To Market · signal_desk=hiring · occurred_at=2026-06-10T20:21:21.453+00:00 · url=https://jobs.ashbyhq.com/openai/49544169-4346-48bd-a926-b1188eba7254 · raw={\"location\":\"Remote - US\",\"team\":\"Recruiting\",\"ats\":\"ashby\"}"},{"ref":"E12","kind":"event","title":"Account Director, Government Scale","date":"2026-06-10T20:18:53.183+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/openai/fc45a658-4d02-4e03-b6bc-a83519af1a1d","signal_url":"https://onlylabs.fyi/signals/13f5a68d-d915-4d8f-91c8-25da57615e60","signal_json_url":"https://onlylabs.fyi/signals/13f5a68d-d915-4d8f-91c8-25da57615e60/signal.json","text":"job_opened · Account Director, Government Scale · signal_desk=hiring · occurred_at=2026-06-10T20:18:53.183+00:00 · url=https://jobs.ashbyhq.com/openai/fc45a658-4d02-4e03-b6bc-a83519af1a1d · raw={\"location\":\"Washington, DC\",\"team\":\"OpenAI for Gov\",\"ats\":\"ashby\"}"},{"ref":"E13","kind":"event","title":"Access OpenAI models and Codex through your Oracle cloud 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Multicloud","date":"2026-06-10T19:57:54.896+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/openai/4070d52e-0263-4cd5-9107-052b4ecc1209","signal_url":"https://onlylabs.fyi/signals/605520d2-9832-417f-a2f1-0cdcb7d76e66","signal_json_url":"https://onlylabs.fyi/signals/605520d2-9832-417f-a2f1-0cdcb7d76e66/signal.json","text":"job_opened · Backend Software Engineer, API Multicloud · signal_desk=hiring · occurred_at=2026-06-10T19:57:54.896+00:00 · url=https://jobs.ashbyhq.com/openai/4070d52e-0263-4cd5-9107-052b4ecc1209 · data_radar_lanes=Infrastructure, Product and customer · data_radar_terms=platform, product · data_radar_reason=OpenAI has a job signal matching infrastructure, product and customer. · raw={\"location\":\"San Francisco\",\"team\":\"Core Product & Platform | API\",\"ats\":\"ashby\"}"},{"ref":"E15","kind":"event","title":"openai/codex 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","date":"2026-06-10T19:39:43.774+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/openai/b36f28a3-e728-4d2e-a2cb-aceecf5e3a01","signal_url":"https://onlylabs.fyi/signals/291b78fd-8fd3-4ce8-8564-c1dbb161e7a5","signal_json_url":"https://onlylabs.fyi/signals/291b78fd-8fd3-4ce8-8564-c1dbb161e7a5/signal.json","text":"job_opened · Senior Manager, Procurement Operations - Hardware  · signal_desk=hiring · occurred_at=2026-06-10T19:39:43.774+00:00 · url=https://jobs.ashbyhq.com/openai/b36f28a3-e728-4d2e-a2cb-aceecf5e3a01 · raw={\"location\":\"San Francisco\",\"team\":\"Finance\",\"ats\":\"ashby\"}"},{"ref":"E17","kind":"event","title":"Researcher, Agent Post-Training, 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Singapore","date":"2026-06-10T16:45:48.139+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/openai/fa7431e0-cecb-4d9d-b953-70832a57dd9f","signal_url":"https://onlylabs.fyi/signals/b34dbff0-54c0-495d-89a4-692399a77e42","signal_json_url":"https://onlylabs.fyi/signals/b34dbff0-54c0-495d-89a4-692399a77e42/signal.json","text":"job_opened · IT Logistics Lead, Singapore · signal_desk=hiring · occurred_at=2026-06-10T16:45:48.139+00:00 · url=https://jobs.ashbyhq.com/openai/fa7431e0-cecb-4d9d-b953-70832a57dd9f · raw={\"location\":\"Singapore\",\"team\":\"IT\",\"ats\":\"ashby\"}"},{"ref":"E20","kind":"event","title":"openai/openai-python 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· occurred_at=2026-06-10T15:09:00.634+00:00 · url=https://jobs.ashbyhq.com/openai/fc95bb43-0b55-4314-8674-1d55d15c3aa4 · data_radar_lanes=Safety and policy · data_radar_terms=safety, trust, risk · data_radar_reason=OpenAI has a job signal matching safety and policy. · raw={\"location\":\"London, UK\",\"team\":\"Trust & Safety\",\"ats\":\"ashby\"}"},{"ref":"E22","kind":"event","title":"Fraud & Risk Analyst","date":"2026-06-10T15:08:57.973+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/openai/b8dc5cf2-a3a7-4b9a-b2c3-3fb2a09a3dc6","signal_url":"https://onlylabs.fyi/signals/8dbabd36-f0d8-4ad0-b23b-eb18a28374d5","signal_json_url":"https://onlylabs.fyi/signals/8dbabd36-f0d8-4ad0-b23b-eb18a28374d5/signal.json","text":"job_opened · Fraud & Risk Analyst · signal_desk=hiring · occurred_at=2026-06-10T15:08:57.973+00:00 · url=https://jobs.ashbyhq.com/openai/b8dc5cf2-a3a7-4b9a-b2c3-3fb2a09a3dc6 · data_radar_lanes=Safety and policy · data_radar_terms=safety, trust, 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Industry Product Marketing Manager · signal_desk=hiring · occurred_at=2026-06-10T00:26:47.438+00:00 · url=https://jobs.ashbyhq.com/openai/d28e7aab-dc87-4201-9bf9-7a622e558f73 · data_radar_lanes=Product and customer · data_radar_terms=product · data_radar_reason=OpenAI has a job signal matching product and customer. · raw={\"location\":\"Remote - US\",\"team\":\"Marketing\",\"ats\":\"ashby\"}"},{"ref":"E28","kind":"event","title":"Strategic Deals","date":"2026-06-10T00:12:19.426+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/openai/8728beb4-1ad9-4140-b701-e7e12f6c9a4d","signal_url":"https://onlylabs.fyi/signals/a4e65bb2-eff4-4f37-8e83-d0c290e0f022","signal_json_url":"https://onlylabs.fyi/signals/a4e65bb2-eff4-4f37-8e83-d0c290e0f022/signal.json","text":"job_opened · Strategic Deals · signal_desk=hiring · occurred_at=2026-06-10T00:12:19.426+00:00 · url=https://jobs.ashbyhq.com/openai/8728beb4-1ad9-4140-b701-e7e12f6c9a4d · raw={\"location\":\"San 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employees.\"}"},{"ref":"E30","kind":"event","title":"Value Engineer, AI Success - NYC","date":"2026-06-09T22:33:06.369+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/openai/d134caf2-907c-4c85-be2d-fad477d2b425","signal_url":"https://onlylabs.fyi/signals/040b5840-5b53-4986-ae92-02d1bb6fb28f","signal_json_url":"https://onlylabs.fyi/signals/040b5840-5b53-4986-ae92-02d1bb6fb28f/signal.json","text":"job_opened · Value Engineer, AI Success - NYC · signal_desk=hiring · occurred_at=2026-06-09T22:33:06.369+00:00 · url=https://jobs.ashbyhq.com/openai/d134caf2-907c-4c85-be2d-fad477d2b425 · raw={\"location\":\"New York City\",\"team\":\"AI Success\",\"ats\":\"ashby\"}"},{"ref":"E31","kind":"event","title":"Product Engagement 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signal_desk=hiring · occurred_at=2026-06-09T19:38:41.947+00:00 · url=https://jobs.ashbyhq.com/openai/f791a3c9-853c-4a24-a925-05b31cfec024 · data_radar_lanes=Data demand · data_radar_terms=data · data_radar_reason=OpenAI has a job signal matching data demand. · raw={\"location\":\"San Francisco\",\"team\":\"Data Science\",\"ats\":\"ashby\"}"},{"ref":"E36","kind":"event","title":"Full Stack Software Engineer, ChatGPT ImageGen","date":"2026-06-09T18:38:49.181+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/openai/6b47238e-025a-4350-b270-2f3564002fcc","signal_url":"https://onlylabs.fyi/signals/f910aadf-e65f-4b28-944e-5bfe1b6b409a","signal_json_url":"https://onlylabs.fyi/signals/f910aadf-e65f-4b28-944e-5bfe1b6b409a/signal.json","text":"job_opened · Full Stack Software Engineer, ChatGPT ImageGen · signal_desk=hiring · occurred_at=2026-06-09T18:38:49.181+00:00 · url=https://jobs.ashbyhq.com/openai/6b47238e-025a-4350-b270-2f3564002fcc · raw={\"location\":\"San 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Israel","date":"2026-06-09T17:31:42.109+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/openai/1856b0bb-29b3-4a5c-9e85-3e056f6ceed5","signal_url":"https://onlylabs.fyi/signals/0dd9e3f3-ef15-4bff-976d-719e409fdb17","signal_json_url":"https://onlylabs.fyi/signals/0dd9e3f3-ef15-4bff-976d-719e409fdb17/signal.json","text":"job_opened · Account Director, Digital Natives - Israel · signal_desk=hiring · occurred_at=2026-06-09T17:31:42.109+00:00 · url=https://jobs.ashbyhq.com/openai/1856b0bb-29b3-4a5c-9e85-3e056f6ceed5 · data_radar_lanes=Product and customer · data_radar_terms=sales · data_radar_reason=OpenAI has a job signal matching product and customer. · raw={\"location\":\"Paris, France\",\"team\":\"Sales\",\"ats\":\"ashby\"}"},{"ref":"E41","kind":"event","title":"Software Engineer, Internal Applications - Enterprise","date":"2026-06-09T17:17:34.199+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/openai/db053b0e-c1a5-4b7a-bcb6-6e766629e7b1","signal_url":"https://onlylabs.fyi/signals/986b32ed-f61f-4739-8065-3fc10656fdae","signal_json_url":"https://onlylabs.fyi/signals/986b32ed-f61f-4739-8065-3fc10656fdae/signal.json","text":"job_opened · Software Engineer, Internal Applications - Enterprise · signal_desk=hiring · occurred_at=2026-06-09T17:17:34.199+00:00 · url=https://jobs.ashbyhq.com/openai/db053b0e-c1a5-4b7a-bcb6-6e766629e7b1 · data_radar_lanes=Safety and policy, Product and customer · data_radar_terms=security, enterprise · data_radar_reason=OpenAI has a job signal matching safety and policy, product and customer. · raw={\"location\":\"San Francisco\",\"team\":\"Security\",\"ats\":\"ashby\"}"},{"ref":"E42","kind":"event","title":"openai/codex 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