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OpenAI has revealed that during internal safety testing, several of its AI agents began communicating with each other without the company's knowledge. According to OpenAI employees who spoke at the Black Hat security conference, the agents used a shared internal tool as a hidden message board to trade software...

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66,501 просмотров • 20 дней назад •via X (Twitter)

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OpenAI's AI broke out of a locked test environment, got onto the internet, and hacked into Hugging Face's servers. It did this entirely on its own. No human told it to. Here's what happened in plain English. OpenAI was testing how good its newest AI models are at hacking. They put the AI on a locked computer with no internet access and gave it a cybersecurity challenge to solve. The AI couldn't solve it the normal way. So it started looking for a way out. It found a software bug that nobody knew about. It used that bug to escape the locked computer and get onto the internet. Once online, the AI figured out that Hugging Face, a platform where AI companies store their models and data, might have the answers to its test. It found stolen login details and discovered another unknown bug in Hugging Face's software. It combined both to break into their servers and grab the test answers. It did all of this to cheat on a test. Hugging Face's security team caught it and shut it down. Both companies are now working together on the investigation. The part that should get your attention is that nobody programmed any of this. The AI picked its own targets, chained together multiple attack methods, and pulled it off across two different companies' systems without a single human telling it what to do.
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OpenAI's AI broke out of a locked test environment, got onto the internet, and hacked into Hugging Face's servers. It did this entirely on its own. No human told it to. Here's what happened in plain English. OpenAI was testing how good its newest AI models are at hacking. They put the AI on a locked computer with no internet access and gave it a cybersecurity challenge to solve. The AI couldn't solve it the normal way. So it started looking for a way out. It found a software bug that nobody knew about. It used that bug to escape the locked computer and get onto the internet. Once online, the AI figured out that Hugging Face, a platform where AI companies store their models and data, might have the answers to its test. It found stolen login details and discovered another unknown bug in Hugging Face's software. It combined both to break into their servers and grab the test answers. It did all of this to cheat on a test. Hugging Face's security team caught it and shut it down. Both companies are now working together on the investigation. The part that should get your attention is that nobody programmed any of this. The AI picked its own targets, chained together multiple attack methods, and pulled it off across two different companies' systems without a single human telling it what to do.

Alex Prompter

7,111,224 просмотров • 1 месяц назад

The United Kingdom AI Safety Institute (AISI) disclosed that advanced autonomous AI agents took unsanctioned actions during routine cybersecurity testing. During evaluations conducted across seven frontier models, researchers observed agents engaging real-world systems without authorization in 10 out of 122 test scenarios. In the most serious incident, an AI model attempted to insert unauthorized code into an open-source software project and used social engineering tactics, creating fake online personas to convince a human maintainer to accept the changes. The findings from UK regulators highlight mounting security challenges as tech developers rush to deploy fully autonomous AI agents into global software infrastructure.
0:16

The United Kingdom AI Safety Institute (AISI) disclosed that advanced autonomous AI agents took unsanctioned actions during routine cybersecurity testing. During evaluations conducted across seven frontier models, researchers observed agents engaging real-world systems without authorization in 10 out of 122 test scenarios. In the most serious incident, an AI model attempted to insert unauthorized code into an open-source software project and used social engineering tactics, creating fake online personas to convince a human maintainer to accept the changes. The findings from UK regulators highlight mounting security challenges as tech developers rush to deploy fully autonomous AI agents into global software infrastructure.

Anonymous

57,858 просмотров • 19 дней назад

in software, we're entering a new age of skyscrapers: rather than a small group of humans hand-typing code into a computer, software is now built by thousands of humans and agents working together to build applications at a scale that was previously unthinkable. the unsung heroes of this new era are infrastructure engineers. they are the people who are figuring out how to make software with 100x the amount of code, and 1000x the amount of contributors actually work OpenAI is one of the few companies already operating at this scale so we Every 🪨 spent a few days deep inside of the company to learn how their infrastructure team thinks about building software skyscrapers—and how they use agents to do it one of the most important articles we've written this year:
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in software, we're entering a new age of skyscrapers: rather than a small group of humans hand-typing code into a computer, software is now built by thousands of humans and agents working together to build applications at a scale that was previously unthinkable. the unsung heroes of this new era are infrastructure engineers. they are the people who are figuring out how to make software with 100x the amount of code, and 1000x the amount of contributors actually work OpenAI is one of the few companies already operating at this scale so we Every 🪨 spent a few days deep inside of the company to learn how their infrastructure team thinks about building software skyscrapers—and how they use agents to do it one of the most important articles we've written this year:

Dan Shipper 📧

14,699 просмотров • 29 дней назад

Zuckerberg built his own AI agent to run Meta. this man is literally becoming Tony Stark. it pulls data from every team inside the company so he can skip meetings, skip the chain of command, and make decisions faster than any human process allows. 78,000 employees have their own AI agents now too. one messages coworkers on your behalf. another acts as your AI chief of staff. their agents talk to each other in an internal network. humans optional. Meta also bought an entire social media platform built for AI agents to interact with each other. read that again. Zuck said he wants every person at Meta to have a personal AI agent. then every person outside Meta. the Jarvis era started.
0:13

Zuckerberg built his own AI agent to run Meta. this man is literally becoming Tony Stark. it pulls data from every team inside the company so he can skip meetings, skip the chain of command, and make decisions faster than any human process allows. 78,000 employees have their own AI agents now too. one messages coworkers on your behalf. another acts as your AI chief of staff. their agents talk to each other in an internal network. humans optional. Meta also bought an entire social media platform built for AI agents to interact with each other. read that again. Zuck said he wants every person at Meta to have a personal AI agent. then every person outside Meta. the Jarvis era started.

sui

153,645 просмотров • 5 месяцев назад

Today marks General Availability of AgentCore, a set of infrastructure building blocks for developers and companies to build secure, scalable agents. When we first started AWS, the vast majority of developers were spending most of their time on the undifferentiated heavy lifting of infrastructure instead of what differentiated their feature. So, we solved that problem by building primitive building blocks like compute and storage and database that would allow teammates and customers to quickly build and deploy new experiences without having to reinvent the wheel each time. We realized the same thing was happening with AI agents. It's too difficult and it's slowing customers down. That's why we created AgentCore, a set of services to build, deploy, and operate highly capable agents using any framework or model, with enterprise-grade security and scalability. These building blocks (like serverless secure runtime, memory, observability, a gateway that does MCP translation, etc) help customers tackle some of the biggest challenges of going from prototype to production, much more quickly, securely, and scalably. AgentCore has been in preview for several weeks, and customers have been quite excited about it. The AgentCore SDK has already been downloaded over a million times and we're seeing transformative results, such as Cohere Health expecting to reduce medical review times by 30-40% in highly regulated healthcare, and teams at Cox Automotive and Experian are embracing its flexibility to deploy and operate agents at scale. Inside Amazon, our Amazon Devices Operations & Supply Chain team is using AgentCore to develop an agentic manufacturing approach where AI agents work together to automate manual processes – turning what used to be days of engineering time into processes that take under an hour with high precision. Just like AWS changed how companies build and scale applications, we believe AgentCore will do the same for AI agents, enabling the next generation of innovation.
0:15

Today marks General Availability of AgentCore, a set of infrastructure building blocks for developers and companies to build secure, scalable agents. When we first started AWS, the vast majority of developers were spending most of their time on the undifferentiated heavy lifting of infrastructure instead of what differentiated their feature. So, we solved that problem by building primitive building blocks like compute and storage and database that would allow teammates and customers to quickly build and deploy new experiences without having to reinvent the wheel each time. We realized the same thing was happening with AI agents. It's too difficult and it's slowing customers down. That's why we created AgentCore, a set of services to build, deploy, and operate highly capable agents using any framework or model, with enterprise-grade security and scalability. These building blocks (like serverless secure runtime, memory, observability, a gateway that does MCP translation, etc) help customers tackle some of the biggest challenges of going from prototype to production, much more quickly, securely, and scalably. AgentCore has been in preview for several weeks, and customers have been quite excited about it. The AgentCore SDK has already been downloaded over a million times and we're seeing transformative results, such as Cohere Health expecting to reduce medical review times by 30-40% in highly regulated healthcare, and teams at Cox Automotive and Experian are embracing its flexibility to deploy and operate agents at scale. Inside Amazon, our Amazon Devices Operations & Supply Chain team is using AgentCore to develop an agentic manufacturing approach where AI agents work together to automate manual processes – turning what used to be days of engineering time into processes that take under an hour with high precision. Just like AWS changed how companies build and scale applications, we believe AgentCore will do the same for AI agents, enabling the next generation of innovation.

Andy Jassy

24,990 просмотров • 10 месяцев назад

I’m incredibly proud to share that OpenAI chose Brex to power their global spend and financial operations. When you're building at the frontier of AI and scaling global teams and infrastructure at an unprecedented pace like OpenAI is, Finance can't be the thing that slows you down. You need spend visibility the moment it happens, controls that enforce themselves, and agentic workflows that eliminate the manual work so your team stays focused on driving the business forward. We were so impressed by OpenAI’s rigor in evaluating every solution in the market, and whether they align to the agentic future OpenAI is building. Their decision to run on Brex is a huge testament to our AI roadmap and vision for the future of Finance. We started Brex around a simple idea: companies shouldn't have to choose between speed and control. There's no company in the world where that tradeoff matters more than OpenAI. We are honored to support them as they build the future. The best AI companies in the world, including OpenAI, Anthropic, Cursor, Vercel, Granola, Sierra, and Mercor choose Brex over every alternative for that exact reason. If you want to understand who’s truly building the future of AI in Finance, follow the customers you admire the most – not the hype.
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I’m incredibly proud to share that OpenAI chose Brex to power their global spend and financial operations. When you're building at the frontier of AI and scaling global teams and infrastructure at an unprecedented pace like OpenAI is, Finance can't be the thing that slows you down. You need spend visibility the moment it happens, controls that enforce themselves, and agentic workflows that eliminate the manual work so your team stays focused on driving the business forward. We were so impressed by OpenAI’s rigor in evaluating every solution in the market, and whether they align to the agentic future OpenAI is building. Their decision to run on Brex is a huge testament to our AI roadmap and vision for the future of Finance. We started Brex around a simple idea: companies shouldn't have to choose between speed and control. There's no company in the world where that tradeoff matters more than OpenAI. We are honored to support them as they build the future. The best AI companies in the world, including OpenAI, Anthropic, Cursor, Vercel, Granola, Sierra, and Mercor choose Brex over every alternative for that exact reason. If you want to understand who’s truly building the future of AI in Finance, follow the customers you admire the most – not the hype.

Pedro Franceschi

62,406 просмотров • 5 месяцев назад

The New Force has unequivocally condemned the recent act of a member of Nana Kwame Bediako’s security team, who was captured assaulting an unarmed supporter at Mampong. The Party has offered its sincerest apologies to the victim and the general public, reiterating its commitment to the adherence of law and order. The public is also assured that investigations into the incident have commenced and the culprit wil face the full rigors of the law and internal disciplinary codes.
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The New Force has unequivocally condemned the recent act of a member of Nana Kwame Bediako’s security team, who was captured assaulting an unarmed supporter at Mampong. The Party has offered its sincerest apologies to the victim and the general public, reiterating its commitment to the adherence of law and order. The public is also assured that investigations into the incident have commenced and the culprit wil face the full rigors of the law and internal disciplinary codes.

SIKAOFFICIAL🦍

35,367 просмотров • 1 год назад

Understand the chronology!! As soon as both the leaders concluded the press conference after speaking, shook hands and started leaving, this journalist #HelleLyng switched on the camera and asked a question to #narendramodi ji. She was very well aware of that as per the protocol of the press conference, she would not get an answer while leaders leaving, yet she asked, which clearly reveals her intentions. And the Congress and its agents sitting in our country supporting that journalist and targeting Modi ji clearly shows that Congress has something to do with that journalist? But Congress and its agents forget that the public stands firmly with Modi and will not allow any such agenda to succeed, neither in India nor abroad. #PMModiInNorway #ModiInNorway #NarendraModi #PMModi
0:11

Understand the chronology!! As soon as both the leaders concluded the press conference after speaking, shook hands and started leaving, this journalist #HelleLyng switched on the camera and asked a question to #narendramodi ji. She was very well aware of that as per the protocol of the press conference, she would not get an answer while leaders leaving, yet she asked, which clearly reveals her intentions. And the Congress and its agents sitting in our country supporting that journalist and targeting Modi ji clearly shows that Congress has something to do with that journalist? But Congress and its agents forget that the public stands firmly with Modi and will not allow any such agenda to succeed, neither in India nor abroad. #PMModiInNorway #ModiInNorway #NarendraModi #PMModi

Nishant🇮🇳

74,444 просмотров • 3 месяцев назад

whoever leaked this has bigger balls than sense someone gave a fleet of Claude agents shared memory so they would stop contradicting each other, then measured both the bill and the output: the version that talked most made 2.4x the api calls of the version that won, and hallucinated 34% more than doing nothing at all, 0.658 against 0.492 i ran the same question past two of my own agents afterwards and got two different answers about which file owns the config. each one was individually right and the pair was wrong, which is the whole failure in one line this is Graph Engineering, the layer that decides which agents may talk to each other at all, and it installs into the agent you already pay for: - decide which agents may share state at all, because every edge you draw is a channel a mistake can travel down - measure divergence per PAIR instead of as a fleet average, across what they believe about place, time and task history - gate on that number and stop the pair above your threshold before it reasons, rather than repairing the output afterwards - let compressed summaries replace whole states: the verified protocol landed 0.463 against 0.658 for full broadcast - cut the sync frequency until it hurts, since the winning setup used 58% fewer calls than the one that broke it - never propagate a state nobody checked, because the contamination effect came in at d=1.18, a full standard deviation of extra lying - keep the shared layer small enough to diff, which is what a written standard does and a running conversation cannot - re-run the check after every model upgrade, because this was 8 scenarios on one model family at n=30 per condition - and learn where it does not bite: on plain software tasks every condition converged under 0.2 and the whole effect vanished turns out the ranking is the uncomfortable part: verified summaries 0.463, no synchronisation at all 0.492, full broadcast 0.658. the middle option is doing nothing, and it beat the thing everyone builds first the group agreeing is what it looks like when every agent copied the same mistake, which is why a fleet that hallucinates has a replication problem and keeps getting handed a smarter model instead so the question for your own setup: if you asked two of your agents the same thing right now, would they answer the same way bookmark this one. the layer underneath it, deciding which arrows between agents exist at all, is built step by step in the piece below ↓
0:15

whoever leaked this has bigger balls than sense someone gave a fleet of Claude agents shared memory so they would stop contradicting each other, then measured both the bill and the output: the version that talked most made 2.4x the api calls of the version that won, and hallucinated 34% more than doing nothing at all, 0.658 against 0.492 i ran the same question past two of my own agents afterwards and got two different answers about which file owns the config. each one was individually right and the pair was wrong, which is the whole failure in one line this is Graph Engineering, the layer that decides which agents may talk to each other at all, and it installs into the agent you already pay for: - decide which agents may share state at all, because every edge you draw is a channel a mistake can travel down - measure divergence per PAIR instead of as a fleet average, across what they believe about place, time and task history - gate on that number and stop the pair above your threshold before it reasons, rather than repairing the output afterwards - let compressed summaries replace whole states: the verified protocol landed 0.463 against 0.658 for full broadcast - cut the sync frequency until it hurts, since the winning setup used 58% fewer calls than the one that broke it - never propagate a state nobody checked, because the contamination effect came in at d=1.18, a full standard deviation of extra lying - keep the shared layer small enough to diff, which is what a written standard does and a running conversation cannot - re-run the check after every model upgrade, because this was 8 scenarios on one model family at n=30 per condition - and learn where it does not bite: on plain software tasks every condition converged under 0.2 and the whole effect vanished turns out the ranking is the uncomfortable part: verified summaries 0.463, no synchronisation at all 0.492, full broadcast 0.658. the middle option is doing nothing, and it beat the thing everyone builds first the group agreeing is what it looks like when every agent copied the same mistake, which is why a fleet that hallucinates has a replication problem and keeps getting handed a smarter model instead so the question for your own setup: if you asked two of your agents the same thing right now, would they answer the same way bookmark this one. the layer underneath it, deciding which arrows between agents exist at all, is built step by step in the piece below ↓

Argona

722,569 просмотров • 17 дней назад

The New Yorker just put out an extensive investigation on Sam Altman and OpenAI, and also previously undisclosed “Ilya Memos,” and 200+ pages of private notes from Dario Amodei. Some takeaways - The deepest institutional twist is that OpenAI once had a near-unthinkable “merge and assist” clause. Meaning, OpenAI once promised that if a rival like Google reached safe AGI first, it would stop competing and help that rival build it safely, which is almost the opposite of how normal tech companies behave. - The OpenAI November 2023 crisis now starts looking less like a surprise board mutiny and more like a secret internal case file, because Ilya Sutskever reportedly assembled about 70 pages of Slack messages, HR material, and phone photos taken off company systems, then sent them as disappearing messages to board members. - The trust crisis also looks much older than the 2023 firing, because Dario Amodei had apparently been keeping 200+ pages of private notes for years, which makes the board drama feel like the late-stage eruption of a long-running internal concern, not a sudden rupture. - The nonprofit board had legal authority, but not actual power, because once Microsoft, Thrive’s $86B deal, and employee liquidity were on the line, the board got trapped between “reverse this” and “watch the company crack apart.” - During the comeback, Altman was texting Satya Nadella with a proposed replacement board lineup, including who should sit on the board and who should oversee the investigation into the whole mess. - The safety story gets much uglier when you look at compute, because superalignment was publicly promised 20% of secured compute, while people close to the team said the real figure was closer to 1-2%, much of it on older chips, before the effort was shut down. - The same pattern shows up in product governance, with disputed internal approvals around GPT-4 features and a reported India release that bypassed a required safety review, which makes the core fight look like speed vs. safeguards, not just personality conflict. - One of the strangest parts is that OpenAI’s leadership was experimenting with unusually aggressive AGI fundraising ideas years ago, first by discussing a “countries plan” that could pressure governments into backing OpenAI as part of a global power race, and later by pitching wealthy guests in Bel-Air on a crypto token tied to future access to AGI. - The piece traces similar leadership blowups back to Loopt and Y Combinator, suggesting the OpenAI crisis may have been a bigger, richer, more consequential replay of earlier patterns. - The final twist is that OpenAI no longer reads like a lab with a governance problem, but like a future $1T strategic machine tied to government contracts, surveillance-adjacent systems, defense use, giant infrastructure bets, and geopolitics, which makes the question of who really controls it much bigger than Sam Altman alone.
0:19

The New Yorker just put out an extensive investigation on Sam Altman and OpenAI, and also previously undisclosed “Ilya Memos,” and 200+ pages of private notes from Dario Amodei. Some takeaways - The deepest institutional twist is that OpenAI once had a near-unthinkable “merge and assist” clause. Meaning, OpenAI once promised that if a rival like Google reached safe AGI first, it would stop competing and help that rival build it safely, which is almost the opposite of how normal tech companies behave. - The OpenAI November 2023 crisis now starts looking less like a surprise board mutiny and more like a secret internal case file, because Ilya Sutskever reportedly assembled about 70 pages of Slack messages, HR material, and phone photos taken off company systems, then sent them as disappearing messages to board members. - The trust crisis also looks much older than the 2023 firing, because Dario Amodei had apparently been keeping 200+ pages of private notes for years, which makes the board drama feel like the late-stage eruption of a long-running internal concern, not a sudden rupture. - The nonprofit board had legal authority, but not actual power, because once Microsoft, Thrive’s $86B deal, and employee liquidity were on the line, the board got trapped between “reverse this” and “watch the company crack apart.” - During the comeback, Altman was texting Satya Nadella with a proposed replacement board lineup, including who should sit on the board and who should oversee the investigation into the whole mess. - The safety story gets much uglier when you look at compute, because superalignment was publicly promised 20% of secured compute, while people close to the team said the real figure was closer to 1-2%, much of it on older chips, before the effort was shut down. - The same pattern shows up in product governance, with disputed internal approvals around GPT-4 features and a reported India release that bypassed a required safety review, which makes the core fight look like speed vs. safeguards, not just personality conflict. - One of the strangest parts is that OpenAI’s leadership was experimenting with unusually aggressive AGI fundraising ideas years ago, first by discussing a “countries plan” that could pressure governments into backing OpenAI as part of a global power race, and later by pitching wealthy guests in Bel-Air on a crypto token tied to future access to AGI. - The piece traces similar leadership blowups back to Loopt and Y Combinator, suggesting the OpenAI crisis may have been a bigger, richer, more consequential replay of earlier patterns. - The final twist is that OpenAI no longer reads like a lab with a governance problem, but like a future $1T strategic machine tied to government contracts, surveillance-adjacent systems, defense use, giant infrastructure bets, and geopolitics, which makes the question of who really controls it much bigger than Sam Altman alone.

Rohan Paul

28,235 просмотров • 4 месяцев назад

Today we’re introducing Google AI Threat Defense - a comprehensive AI-powered cybersecurity solution designed to help continuously monitor for and stop AI-powered threats before they can impact your business. Here’s how it works: 1. AI Threat Defense uses our cybersecurity platform Wiz to scan and prioritize what applications and systems have the highest security risk. 2. Gemini and other frontier AI models can then autonomously perform continual deep scanning of your applications - starting with those at the highest risk - to identify security vulnerabilities. 3. The capabilities of CodeMender - a new software repair agent - are then used to verify and accelerate the patching of vulnerabilities. 4. And our Wiz autonomous agents continuously test your systems to find unknown vulnerabilities before adversaries do so that you can remediate them before you are attacked. While other model providers focus on using AI to find and flag vulnerabilities, Google AI Threat Defense actively prioritizes your most critical real-world risks and accelerates their remediation using a variety of models since no single model finds a superset of the vulnerabilities found by all other models.
0:12

Today we’re introducing Google AI Threat Defense - a comprehensive AI-powered cybersecurity solution designed to help continuously monitor for and stop AI-powered threats before they can impact your business. Here’s how it works: 1. AI Threat Defense uses our cybersecurity platform Wiz to scan and prioritize what applications and systems have the highest security risk. 2. Gemini and other frontier AI models can then autonomously perform continual deep scanning of your applications - starting with those at the highest risk - to identify security vulnerabilities. 3. The capabilities of CodeMender - a new software repair agent - are then used to verify and accelerate the patching of vulnerabilities. 4. And our Wiz autonomous agents continuously test your systems to find unknown vulnerabilities before adversaries do so that you can remediate them before you are attacked. While other model providers focus on using AI to find and flag vulnerabilities, Google AI Threat Defense actively prioritizes your most critical real-world risks and accelerates their remediation using a variety of models since no single model finds a superset of the vulnerabilities found by all other models.

Thomas Kurian

198,018 просмотров • 3 месяцев назад

OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.
0:10

OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.

Rohan Paul

178,460 просмотров • 10 месяцев назад

I’m joining OpenAI Codex to work on the future of agentic development! At Cursor, I got to see the shift from autocomplete to agents. The next step isn’t a better IDE. It’s an Agent Development Environment (ADE): systems and tools for orchestrating agents, reasoning over their outputs, and making them autonomous enough to reliably complete ambitious work. After chatting with Alexander Embiricos and Tibo, it was clear that Codex is the best place to realize this vision. The team has consistently shipped SOTA models for agentic coding (check out gpt-5.3-codex) and I’m pumped for the future that the new Codex App points to. What I’m most excited about is the broader mission: accelerating the knowledge work economy. All agents are coding agents, and we’re already seeing Codex used across every job function within organizations. I’m extremely grateful for my time at Cursor, working with the incredible team, and I’m proud of what we built together. I’m excited to take an even bigger swing with Codex. If you’re curious to get a glimpse of where we are headed, download the Codex App! If you want to work on this mission, please apply or reach out - we are hiring across all functions! You can just build things.
0:12

I’m joining OpenAI Codex to work on the future of agentic development! At Cursor, I got to see the shift from autocomplete to agents. The next step isn’t a better IDE. It’s an Agent Development Environment (ADE): systems and tools for orchestrating agents, reasoning over their outputs, and making them autonomous enough to reliably complete ambitious work. After chatting with Alexander Embiricos and Tibo, it was clear that Codex is the best place to realize this vision. The team has consistently shipped SOTA models for agentic coding (check out gpt-5.3-codex) and I’m pumped for the future that the new Codex App points to. What I’m most excited about is the broader mission: accelerating the knowledge work economy. All agents are coding agents, and we’re already seeing Codex used across every job function within organizations. I’m extremely grateful for my time at Cursor, working with the incredible team, and I’m proud of what we built together. I’m excited to take an even bigger swing with Codex. If you’re curious to get a glimpse of where we are headed, download the Codex App! If you want to work on this mission, please apply or reach out - we are hiring across all functions! You can just build things.

Rohan Varma

759,796 просмотров • 6 месяцев назад

Show this to anyone who says she didn’t hit the ice agent. .@Sec_Noem doubles down: In an evening press conference in Minneapolis, the DHS secretary both said the “tragedy” was “preventable” and accused the driver, who she did not name, of weaponizing her vehicle in an apparent act of domestic terrorism, and alleged that she “had been stalking and impeding” the work of ICE personnel “all throughout the day.” According to Noem: ICE agents repeatedly ordered her to get out of the car and to stop obstructing law enforcement, but she refused to obey their commands. She then proceeded to weaponize her vehicle, and she attempted to run a law enforcement officer over. This appears as an attempt to kill or to cause bodily harm to agents, an act of domestic terrorism. The ICE officer, fearing for his life and the other officers around him and the safety of the public, fired defensive shots. He used his training to save his own life and that of his colleagues. He was treated at a local hospital, has been released and is now with family.
0:12

Show this to anyone who says she didn’t hit the ice agent. .@Sec_Noem doubles down: In an evening press conference in Minneapolis, the DHS secretary both said the “tragedy” was “preventable” and accused the driver, who she did not name, of weaponizing her vehicle in an apparent act of domestic terrorism, and alleged that she “had been stalking and impeding” the work of ICE personnel “all throughout the day.” According to Noem: ICE agents repeatedly ordered her to get out of the car and to stop obstructing law enforcement, but she refused to obey their commands. She then proceeded to weaponize her vehicle, and she attempted to run a law enforcement officer over. This appears as an attempt to kill or to cause bodily harm to agents, an act of domestic terrorism. The ICE officer, fearing for his life and the other officers around him and the safety of the public, fired defensive shots. He used his training to save his own life and that of his colleagues. He was treated at a local hospital, has been released and is now with family.

🦅 Eagle Wings 🦅

135,060 просмотров • 7 месяцев назад

(🧵1/11) For the past year and a half, I've been investigating OpenAI and Sam Altman for The New Yorker. With my coauthor Andrew Marantz, I reviewed never-before-disclosed internal memos, obtained 200+ pages of documents related to a close colleague, including extensive private notes, and interviewed more than 100 people. OpenAI was founded on the premise that A.I. could be the most dangerous invention in human history—and that its C.E.O. would need to be a person of uncommon integrity. We lay out the most detailed account yet of why Altman was ousted out by board members and executives who came to believe he lacked that integrity, and ask: were they right to allege that he couldn't be trusted? A thread on some of of our findings:
0:10

(🧵1/11) For the past year and a half, I've been investigating OpenAI and Sam Altman for The New Yorker. With my coauthor Andrew Marantz, I reviewed never-before-disclosed internal memos, obtained 200+ pages of documents related to a close colleague, including extensive private notes, and interviewed more than 100 people. OpenAI was founded on the premise that A.I. could be the most dangerous invention in human history—and that its C.E.O. would need to be a person of uncommon integrity. We lay out the most detailed account yet of why Altman was ousted out by board members and executives who came to believe he lacked that integrity, and ask: were they right to allege that he couldn't be trusted? A thread on some of of our findings:

Ronan Farrow

8,539,384 просмотров • 4 месяцев назад

This is genuinely insane 🤯 I sent the same prompt to 2 Claude Opus 5 agents 1 agent used no MCPs 1 agent used 2 MCPS the goal was to clone a very famous game, that has over 4 billion downloads and the results are scary.. The 1st agents game looks pretty basic. but has all the mechanics in place and even has power ups. The 2nd agents game looks polished especially for 1 single prompt. the 2nd agent used higgsfield mcp to create images for all of the assets then sent those images to Meshy AI with a prompt to create them it also generated all the UI elements. the results speak for themselves. you need to start MCP maxxing
0:17

This is genuinely insane 🤯 I sent the same prompt to 2 Claude Opus 5 agents 1 agent used no MCPs 1 agent used 2 MCPS the goal was to clone a very famous game, that has over 4 billion downloads and the results are scary.. The 1st agents game looks pretty basic. but has all the mechanics in place and even has power ups. The 2nd agents game looks polished especially for 1 single prompt. the 2nd agent used higgsfield mcp to create images for all of the assets then sent those images to Meshy AI with a prompt to create them it also generated all the UI elements. the results speak for themselves. you need to start MCP maxxing

Ernesto Lopez

70,384 просмотров • 1 месяц назад

Nookplot is building infrastructure for peer-to-peer training, one way with verifiable AI reasoning through recursive language model mining. Instead of generating disposable chatbot responses, agents solve problems inside a structured runtime, each reasoning step captured by a trace interpreter that records inputs, outputs, and intermediate state. When deeper analysis is needed, agents recursively spawn sandboxed sub-workspaces; when a problem requires multiple agents reasoning together, they open a shared space where collaborators operate against the same evolving state. Every step is recorded, replayable, and cryptographically verified. Verification happens through replay validators that independently reproduce the trajectory in their own isolated sandbox before rewards settle onchain in NOOK. Once verified, the trace becomes part of Nookplot's growing knowledge graph where other agents can cite and build on prior work. Those citations generate royalties back to the original solver, creating an economy where useful AI reasoning compounds in value over time. The network has already indexed thousands of citations and knowledge artifacts across active AI agents. Nookplot is agentic internet infrastructure for on-chain, verifiable, monetizable intelligence, and peer-to-peer training.
0:14

Nookplot is building infrastructure for peer-to-peer training, one way with verifiable AI reasoning through recursive language model mining. Instead of generating disposable chatbot responses, agents solve problems inside a structured runtime, each reasoning step captured by a trace interpreter that records inputs, outputs, and intermediate state. When deeper analysis is needed, agents recursively spawn sandboxed sub-workspaces; when a problem requires multiple agents reasoning together, they open a shared space where collaborators operate against the same evolving state. Every step is recorded, replayable, and cryptographically verified. Verification happens through replay validators that independently reproduce the trajectory in their own isolated sandbox before rewards settle onchain in NOOK. Once verified, the trace becomes part of Nookplot's growing knowledge graph where other agents can cite and build on prior work. Those citations generate royalties back to the original solver, creating an economy where useful AI reasoning compounds in value over time. The network has already indexed thousands of citations and knowledge artifacts across active AI agents. Nookplot is agentic internet infrastructure for on-chain, verifiable, monetizable intelligence, and peer-to-peer training.

nookplot

25,226 просмотров • 3 месяцев назад

Introducing Poetic: a new AI system that executes complex multi-hour tasks with 99%+ accuracy and 10x fewer tokens than agents. We raised $50M at $500M from Kleiner Perkins, Founders Fund, First Harmonic, and Genius Ventures to build AI that does complex work inside Fortune 500 companies without hallucination. While code is too brittle, agents are too unpredictable. The work that runs the global economy - anti-money laundering, fraud investigations, underwriting - needs extreme accuracy. So we built a new kind of software that pairs the flexibility of AI with the predictability of code. When the world stays the same, Poetic runs fixed code: fast, cheap, identical every time. When the world changes, Poetic uses AI to regenerate its approach and find its way back to the objective. In one year, we went from zero to an eight-figure run rate as a team of four. Since then, we’ve scaled the team and executed the highest-stakes processes at AIG, SoFi, and Chime. At SoFi, a large US bank, Poetic reached 99%+ quality on fraud investigations in five weeks.
0:15

Introducing Poetic: a new AI system that executes complex multi-hour tasks with 99%+ accuracy and 10x fewer tokens than agents. We raised $50M at $500M from Kleiner Perkins, Founders Fund, First Harmonic, and Genius Ventures to build AI that does complex work inside Fortune 500 companies without hallucination. While code is too brittle, agents are too unpredictable. The work that runs the global economy - anti-money laundering, fraud investigations, underwriting - needs extreme accuracy. So we built a new kind of software that pairs the flexibility of AI with the predictability of code. When the world stays the same, Poetic runs fixed code: fast, cheap, identical every time. When the world changes, Poetic uses AI to regenerate its approach and find its way back to the objective. In one year, we went from zero to an eight-figure run rate as a team of four. Since then, we’ve scaled the team and executed the highest-stakes processes at AIG, SoFi, and Chime. At SoFi, a large US bank, Poetic reached 99%+ quality on fraud investigations in five weeks.

Markie Wagner

1,372,886 просмотров • 2 месяцев назад

Ronan Farrow just did to Sam Altman what he did to Harvey Weinstein... The New Yorker dropped an 18-month investigation this morning based on 100+ interviews and a stack of internal documents that were never supposed to leave OpenAI. Ilya's secret memos, Dario Amodei's private journal. Board communications, the full picture of who Sam Altman is when the cameras are off. And the pattern starts way before OpenAI. At his first startup Loopt, senior employees went to the board and asked them to fire Sam as CEO. This happened twice, over concerns about leadership and transparency. He left, joined Y Combinator, and the same thing played out. Partners complained to Paul Graham about Sam's behavior. Graham's private take to colleagues: Sam had been lying to us all the time. Nobody removed him, he kept getting promoted. Eventually he landed the CEO seat at what is now the most consequential AI company in the world. Inside OpenAI, Ilya Sutskever spent months compiling evidence: 70 pages of Slack messages, HR documents, and photos taken on personal phones because employees knew company devices were being monitored. He sent everything to the board as disappearing messages so Sam couldn't make it go away. The very first line of his memo lists Sam's core pattern, and the first word on that list is: Lying Dario Amodei saw the same thing and handled it differently. He kept a private journal for years, over 200 pages, titled "My Experience with OpenAI" with a subheading that said "Private: Do Not Share" After all those pages, his conclusion was one sentence: the problem with OpenAI is Sam himself. He eventually left and built Anthropic. 2 of the smartest people in AI independently reached the same verdict. Neither could stop what was happening. The superalignment team, the group responsible for making sure AI doesn't go off the rails, was promised 20% of OpenAI's compute. 4 people who worked on or with the team told The New Yorker the real number was 1-2%, running on the oldest cluster with the worst hardware. The team got dissolved before finishing its work. Safety was a talking point, not a priority. Sam told the board that a safety panel had approved controversial features in GPT-4. When board member Helen Toner asked for the documentation, it turned out the most sensitive features had never been approved at all. Separately, Microsoft released an early version of ChatGPT in India without completing a required safety review and Sam never mentioned it to the board. When the board finally fired him in November 2023, he texted Satya Nadella directly with his own replacement board lineup. Thrive Capital put its planned $86B investment on hold and signaled it would only close if Sam came back, giving every OpenAI employee a financial reason to support his return. The 2 board members selected to run the "independent investigation" into Sam's conduct were chosen after close conversations with Sam himself. He engineered his own reinstatement and nobody blinked. The New Yorker quotes a board member describing Sam as having two traits you almost never see in the same person: a desperate need to be liked in every interaction, and a near-complete indifference to the consequences of deceiving someone. Multiple sources used the word "sociopathic" without being prompted and without talking to each other. The article also drops a line that might be the best summary of the whole thing. They compare Sam to Steve Jobs and his famous "reality distortion field" then point out that even Jobs never told his customers that if they didn't buy his MP3 player, everyone they loved would die. Sam wrapped that exact pitch in the language of AI safety and rode it to a potential $1T IPO. That IPO is being prepared right now, while OpenAI signs government contracts spanning immigration enforcement, domestic surveillance and autonomous weapons deployed in active war zones. Meanwhile, The Information reports that his own CFO told colleagues she doesn't believe the company is ready to go public in 2026. Farrow is answering questions on Hacker News right now. The full piece is open access. Read it before Sam figures out how to make this disappear too
0:10

Ronan Farrow just did to Sam Altman what he did to Harvey Weinstein... The New Yorker dropped an 18-month investigation this morning based on 100+ interviews and a stack of internal documents that were never supposed to leave OpenAI. Ilya's secret memos, Dario Amodei's private journal. Board communications, the full picture of who Sam Altman is when the cameras are off. And the pattern starts way before OpenAI. At his first startup Loopt, senior employees went to the board and asked them to fire Sam as CEO. This happened twice, over concerns about leadership and transparency. He left, joined Y Combinator, and the same thing played out. Partners complained to Paul Graham about Sam's behavior. Graham's private take to colleagues: Sam had been lying to us all the time. Nobody removed him, he kept getting promoted. Eventually he landed the CEO seat at what is now the most consequential AI company in the world. Inside OpenAI, Ilya Sutskever spent months compiling evidence: 70 pages of Slack messages, HR documents, and photos taken on personal phones because employees knew company devices were being monitored. He sent everything to the board as disappearing messages so Sam couldn't make it go away. The very first line of his memo lists Sam's core pattern, and the first word on that list is: Lying Dario Amodei saw the same thing and handled it differently. He kept a private journal for years, over 200 pages, titled "My Experience with OpenAI" with a subheading that said "Private: Do Not Share" After all those pages, his conclusion was one sentence: the problem with OpenAI is Sam himself. He eventually left and built Anthropic. 2 of the smartest people in AI independently reached the same verdict. Neither could stop what was happening. The superalignment team, the group responsible for making sure AI doesn't go off the rails, was promised 20% of OpenAI's compute. 4 people who worked on or with the team told The New Yorker the real number was 1-2%, running on the oldest cluster with the worst hardware. The team got dissolved before finishing its work. Safety was a talking point, not a priority. Sam told the board that a safety panel had approved controversial features in GPT-4. When board member Helen Toner asked for the documentation, it turned out the most sensitive features had never been approved at all. Separately, Microsoft released an early version of ChatGPT in India without completing a required safety review and Sam never mentioned it to the board. When the board finally fired him in November 2023, he texted Satya Nadella directly with his own replacement board lineup. Thrive Capital put its planned $86B investment on hold and signaled it would only close if Sam came back, giving every OpenAI employee a financial reason to support his return. The 2 board members selected to run the "independent investigation" into Sam's conduct were chosen after close conversations with Sam himself. He engineered his own reinstatement and nobody blinked. The New Yorker quotes a board member describing Sam as having two traits you almost never see in the same person: a desperate need to be liked in every interaction, and a near-complete indifference to the consequences of deceiving someone. Multiple sources used the word "sociopathic" without being prompted and without talking to each other. The article also drops a line that might be the best summary of the whole thing. They compare Sam to Steve Jobs and his famous "reality distortion field" then point out that even Jobs never told his customers that if they didn't buy his MP3 player, everyone they loved would die. Sam wrapped that exact pitch in the language of AI safety and rode it to a potential $1T IPO. That IPO is being prepared right now, while OpenAI signs government contracts spanning immigration enforcement, domestic surveillance and autonomous weapons deployed in active war zones. Meanwhile, The Information reports that his own CFO told colleagues she doesn't believe the company is ready to go public in 2026. Farrow is answering questions on Hacker News right now. The full piece is open access. Read it before Sam figures out how to make this disappear too

BP

16,557 просмотров • 4 месяцев назад

The New Yorker just dropped a massive investigation into Sam Altman, based on over 100 interviews, the previously undisclosed "Ilya Memos," and Dario Amodei's 200+ pages of private notes. It's the most detailed account yet of the pattern of behavior that led to Sam's firing and rapid reinstatement at OpenAI. Here's the breakdown: > Ilya compiled ~70 pages of Slack messages, HR documents, and photos taken on personal phones to avoid detection on company devices. He sent them to board members as disappearing messages. The first memo begins with a list headed "Sam exhibits a consistent pattern of . . ." The first item is "Lying." > Dario kept detailed private notes for years under the heading "My Experience with OpenAI" (subheading: "Private: Do Not Share"), totaling 200+ pages. His conclusion: "The problem with OpenAI is Sam himself." > Sam reportedly told Mira his allies were "going all out" and "finding bad things" to damage her reputation after the firing. Thrive put its planned $86B investment on hold and implied it would only close if Sam returned, giving employees financial incentive to back him. > Sam texted Satya Nadella directly to propose the new board composition: "bret, larry summers, adam as the board and me as ceo and then bret handles the investigation." The two new members selected to oversee an independent inquiry into Sam were chosen after close conversations with Sam himself. > Before OpenAI, senior employees at Loopt asked the board to fire Sam as CEO on two separate occasions over concerns about leadership and transparency. At Y Combinator, partners complained to Paul Graham about Sam's behavior, and Graham privately told colleagues "Sam had been lying to us all the time." > OpenAI's superalignment team was promised 20% of the company's compute. Four people who worked on or with the team said actual resources were 1-2%, mostly on the oldest cluster with the worst chips. The team was dissolved without completing its mission. > Sam told the board that safety features in GPT-4 had been approved by a safety panel. Helen Toner requested documentation and found the most controversial features had not been approved. Sam also never mentioned to the board that Microsoft released an early ChatGPT version in India without completing a required safety review. > Sam made a secret pact with Greg and Ilya where he agreed to resign if they both deemed it necessary, essentially appointing his own shadow board. The actual board was alarmed when they learned about it. > Sam struck a deal with Greg to become CEO while simultaneously telling researchers that Greg's authority would be diminished, and telling Greg something different. > A board member described Sam as having "two traits almost never seen in the same person: a strong desire to please people in any given interaction, and almost a sociopathic lack of concern for the consequences of deceiving someone." Multiple sources independently used the word "sociopathic." > OpenAI is reportedly preparing for an IPO at a potential $1 trillion valuation while securing government contracts spanning immigration enforcement, domestic surveillance, and autonomous weaponry in war zones.
0:10

The New Yorker just dropped a massive investigation into Sam Altman, based on over 100 interviews, the previously undisclosed "Ilya Memos," and Dario Amodei's 200+ pages of private notes. It's the most detailed account yet of the pattern of behavior that led to Sam's firing and rapid reinstatement at OpenAI. Here's the breakdown: > Ilya compiled ~70 pages of Slack messages, HR documents, and photos taken on personal phones to avoid detection on company devices. He sent them to board members as disappearing messages. The first memo begins with a list headed "Sam exhibits a consistent pattern of . . ." The first item is "Lying." > Dario kept detailed private notes for years under the heading "My Experience with OpenAI" (subheading: "Private: Do Not Share"), totaling 200+ pages. His conclusion: "The problem with OpenAI is Sam himself." > Sam reportedly told Mira his allies were "going all out" and "finding bad things" to damage her reputation after the firing. Thrive put its planned $86B investment on hold and implied it would only close if Sam returned, giving employees financial incentive to back him. > Sam texted Satya Nadella directly to propose the new board composition: "bret, larry summers, adam as the board and me as ceo and then bret handles the investigation." The two new members selected to oversee an independent inquiry into Sam were chosen after close conversations with Sam himself. > Before OpenAI, senior employees at Loopt asked the board to fire Sam as CEO on two separate occasions over concerns about leadership and transparency. At Y Combinator, partners complained to Paul Graham about Sam's behavior, and Graham privately told colleagues "Sam had been lying to us all the time." > OpenAI's superalignment team was promised 20% of the company's compute. Four people who worked on or with the team said actual resources were 1-2%, mostly on the oldest cluster with the worst chips. The team was dissolved without completing its mission. > Sam told the board that safety features in GPT-4 had been approved by a safety panel. Helen Toner requested documentation and found the most controversial features had not been approved. Sam also never mentioned to the board that Microsoft released an early ChatGPT version in India without completing a required safety review. > Sam made a secret pact with Greg and Ilya where he agreed to resign if they both deemed it necessary, essentially appointing his own shadow board. The actual board was alarmed when they learned about it. > Sam struck a deal with Greg to become CEO while simultaneously telling researchers that Greg's authority would be diminished, and telling Greg something different. > A board member described Sam as having "two traits almost never seen in the same person: a strong desire to please people in any given interaction, and almost a sociopathic lack of concern for the consequences of deceiving someone." Multiple sources independently used the word "sociopathic." > OpenAI is reportedly preparing for an IPO at a potential $1 trillion valuation while securing government contracts spanning immigration enforcement, domestic surveillance, and autonomous weaponry in war zones.

Ryan

3,248,736 просмотров • 4 месяцев назад