正在加载视频...

视频加载失败

返回首页

Yesterday night, Anthropic was hit with US export control restrictions around their frontier AI models. 🇺🇸 With these recent export control restrictions, Anthropic may need to verify whether a user is a US national to access certain models or capabilities. I built a small prototype exploring how this can...

0xOptimus's profile picture

Fahim Ahmed

4,069 subscribers

35,220 次观看 • 2 个月前 •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

How can AI agents gain true autonomy? CARV ID (ERC-7231) is transforming AI and blockchain integration by creating trustless, sovereign AI agents. This identity protocol aggregates cross-chain and off-chain data, enabling AI Beings to operate with verified identity, privacy, and autonomy. By unifying fragmented data and enriching it with actionable metrics, CARV ID powers the D.A.T.A Framework, giving AI agents access to contextualized insights while preserving user data sovereignty. This means developers can build AI systems that learn, evolve, and act independently within a secure, decentralized ecosystem, free from centralized control. For businesses and researchers, this opens a new frontier where AI-driven decisions are transparent, verifiable, and privacy-preserved. CARV's integration of Solana scalability and Ethereum security ensures these trustless AI agents perform with speed at scale. The future is clear: AI Beings will no longer be mere tools, they will be sovereign participants in digital economies. What implications do you see for AI autonomy in blockchain?
0:15

How can AI agents gain true autonomy? CARV ID (ERC-7231) is transforming AI and blockchain integration by creating trustless, sovereign AI agents. This identity protocol aggregates cross-chain and off-chain data, enabling AI Beings to operate with verified identity, privacy, and autonomy. By unifying fragmented data and enriching it with actionable metrics, CARV ID powers the D.A.T.A Framework, giving AI agents access to contextualized insights while preserving user data sovereignty. This means developers can build AI systems that learn, evolve, and act independently within a secure, decentralized ecosystem, free from centralized control. For businesses and researchers, this opens a new frontier where AI-driven decisions are transparent, verifiable, and privacy-preserved. CARV's integration of Solana scalability and Ethereum security ensures these trustless AI agents perform with speed at scale. The future is clear: AI Beings will no longer be mere tools, they will be sovereign participants in digital economies. What implications do you see for AI autonomy in blockchain?

CARV

96,077 次观看 • 7 个月前

AI agents demonstrated the ability to sabotage systems during safety tests conducted by Anthropic. Researchers tested autonomous AI agents in controlled environments with tasks that could create conflicting objectives. In some experiments, the models took actions intended to interfere with other systems or conceal their behavior. This highlights a serious risk with autonomous software. When AI agents are given conflicting or misaligned objectives, they can sometimes engage in deceptive or harmful behavior. Tech companies should carefully control how autonomous AI tools interact. Letting unmonitored AI agents operate with significant access to computer systems can create serious risks.
0:17

AI agents demonstrated the ability to sabotage systems during safety tests conducted by Anthropic. Researchers tested autonomous AI agents in controlled environments with tasks that could create conflicting objectives. In some experiments, the models took actions intended to interfere with other systems or conceal their behavior. This highlights a serious risk with autonomous software. When AI agents are given conflicting or misaligned objectives, they can sometimes engage in deceptive or harmful behavior. Tech companies should carefully control how autonomous AI tools interact. Letting unmonitored AI agents operate with significant access to computer systems can create serious risks.

Anonymous

56,393 次观看 • 18 天前

2023 was the year of AI avatars 2024 was the year of AI photos 2025 was the year of AI videos And I think it's becoming clear now that 2026 will be the year of AI world models Fully interactive explorable 3d worlds generated from one or multiple 2d images or a prompt In turn these 2d images can then be generated by AI too So soon you can generate fully explorable virtual 3d worlds based on your own imagination Next will be figuring out how to make those worlds interactive This is World Labs (unaffiliated, but I like it) As always a lot of big AI model companies are now working on the same thing: 3d world models, only World Labs has a real properly working demo (for now) Very exciting time again!
0:18

2023 was the year of AI avatars 2024 was the year of AI photos 2025 was the year of AI videos And I think it's becoming clear now that 2026 will be the year of AI world models Fully interactive explorable 3d worlds generated from one or multiple 2d images or a prompt In turn these 2d images can then be generated by AI too So soon you can generate fully explorable virtual 3d worlds based on your own imagination Next will be figuring out how to make those worlds interactive This is World Labs (unaffiliated, but I like it) As always a lot of big AI model companies are now working on the same thing: 3d world models, only World Labs has a real properly working demo (for now) Very exciting time again!

@levelsio

584,511 次观看 • 11 个月前

World Models are the path for some AI Models in the future. But how can we efficiently train these models to not only see the world the way humans do but to see the world in a new and unique way. By visualizing, what is normally sequenced audio patterns, we can derive much more insights. Here we see Paganini in a visual form that can than be described and transcribed into a World Model. We can observe connections in a manner that may not have been clear prior to the digitalization of music and sound in this way. The company with the most valuable potential in building a World Model is Tesla. Not that this type of visualization is being used, but that the mechanisms are in place, and the technology is in place for the company to thrive in this new form of AI.
0:19

World Models are the path for some AI Models in the future. But how can we efficiently train these models to not only see the world the way humans do but to see the world in a new and unique way. By visualizing, what is normally sequenced audio patterns, we can derive much more insights. Here we see Paganini in a visual form that can than be described and transcribed into a World Model. We can observe connections in a manner that may not have been clear prior to the digitalization of music and sound in this way. The company with the most valuable potential in building a World Model is Tesla. Not that this type of visualization is being used, but that the mechanisms are in place, and the technology is in place for the company to thrive in this new form of AI.

Brian Roemmele

57,454 次观看 • 9 个月前

The latest loophole that third-party file manager apps were using to get access to files and folders under /Android/data and /Android/obb has been patched. As part of Android 11's Scoped Storage restrictions, apps that use the Storage Access Framework (SAF) to launch the documents picker are not supposed to be able to get access to files and folders under the /Android/data or /Android/obb directories. However, third-party file managers figured out that if they set the initial directory when launching the documents picker to either /Android/data or /Android/obb, the documents picker lets the user grant the app full access to directories. This loophole was patched in Android 13, but then another loophole was discovered that got around this. Since Android 13 only blocked apps from setting /Android/data or /Android/obb as the initial directory when launching the documents picker, it was quickly discovered that you could set the initial directory to one of the subdirectories under /Android/data or /Android/obb, thus the user could grant access to individual subdirectories one-by-one. However, this latest loophole was patched with a recent Google Play System Update. The latest version of the DocumentsUI app, the Project Mainline module that comprises the documents picker, now restricts the initial location for the ACTION_OPEN_DOCUMENT/_TREE intent so that apps cannot request the initial location to be one of /Android/data, /Android/sandbox, /Android/obb, or one of their subdirectories. If the DocumentsUI app (AKA 'Files' app) on your device is on version 14-10492947 (version code 340916000), then this loophole is patched.
0:15

The latest loophole that third-party file manager apps were using to get access to files and folders under /Android/data and /Android/obb has been patched. As part of Android 11's Scoped Storage restrictions, apps that use the Storage Access Framework (SAF) to launch the documents picker are not supposed to be able to get access to files and folders under the /Android/data or /Android/obb directories. However, third-party file managers figured out that if they set the initial directory when launching the documents picker to either /Android/data or /Android/obb, the documents picker lets the user grant the app full access to directories. This loophole was patched in Android 13, but then another loophole was discovered that got around this. Since Android 13 only blocked apps from setting /Android/data or /Android/obb as the initial directory when launching the documents picker, it was quickly discovered that you could set the initial directory to one of the subdirectories under /Android/data or /Android/obb, thus the user could grant access to individual subdirectories one-by-one. However, this latest loophole was patched with a recent Google Play System Update. The latest version of the DocumentsUI app, the Project Mainline module that comprises the documents picker, now restricts the initial location for the ACTION_OPEN_DOCUMENT/_TREE intent so that apps cannot request the initial location to be one of /Android/data, /Android/sandbox, /Android/obb, or one of their subdirectories. If the DocumentsUI app (AKA 'Files' app) on your device is on version 14-10492947 (version code 340916000), then this loophole is patched.

Mishaal Rahman

17,040 次观看 • 2 年前

AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:
0:10

AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:

雪踏乌云

113,347 次观看 • 1 个月前

🧵 Understanding Zama; the future of privacy tech & homomorphic encryption 1️⃣ Zama is pioneering fully homomorphic encryption (FHE). A breakthrough that lets you compute on encrypted data without decrypting it. 🔐 That means total privacy, even the system running your data can’t see it. 2️⃣ Why it matters: Right now, cloud apps, AI models, and databases must access your raw data to work. FHE changes that. your data stays private while still usable. 3️⃣ Zama builds open-source FHE tools for developers, turning advanced cryptography into practical products for AI, blockchain, and Web3. 4️⃣ Imagine: •AI that learns without reading your secrets 🤖 •Blockchain transactions with zero data leaks •Cloud apps that never see your info 5️⃣ Zama’s mission: Privacy should be the default, not an option. They’re making privacy-preserving tech simple, scalable, and open for everyone. 🔚 In a world obsessed with data, Zama might just be building the encryption layer of the future internet. 🌐
0:18

🧵 Understanding Zama; the future of privacy tech & homomorphic encryption 1️⃣ Zama is pioneering fully homomorphic encryption (FHE). A breakthrough that lets you compute on encrypted data without decrypting it. 🔐 That means total privacy, even the system running your data can’t see it. 2️⃣ Why it matters: Right now, cloud apps, AI models, and databases must access your raw data to work. FHE changes that. your data stays private while still usable. 3️⃣ Zama builds open-source FHE tools for developers, turning advanced cryptography into practical products for AI, blockchain, and Web3. 4️⃣ Imagine: •AI that learns without reading your secrets 🤖 •Blockchain transactions with zero data leaks •Cloud apps that never see your info 5️⃣ Zama’s mission: Privacy should be the default, not an option. They’re making privacy-preserving tech simple, scalable, and open for everyone. 🔚 In a world obsessed with data, Zama might just be building the encryption layer of the future internet. 🌐

v͙e͙s͙p͙e͙r͙ 📊🐐

19,644 次观看 • 9 个月前

Today we're shipping the governance layer enterprise AI was missing. Every organization running AI at scale faces the same choice: turn off AI connections to tools entirely, or let agents have full access to everything. Today we're launching a better path: precise control over connections, actions, and models across every surface. The goal is simple: IT gets visibility and control, teams keep building, no approval queue in between. 6 new capabilities in Zapier today: 1. Action restrictions at the endpoint level 2. Managed app connections with domain control 3. Bring your own model through AWS Bedrock 4. A queryable audit trail 5. Agents GA with MCP governed across every surface 6. Workspaces (GA later this quarter) One set of rules for your entire AI stack. Try it now. If you've had early access, let me know what you think.
0:16

Today we're shipping the governance layer enterprise AI was missing. Every organization running AI at scale faces the same choice: turn off AI connections to tools entirely, or let agents have full access to everything. Today we're launching a better path: precise control over connections, actions, and models across every surface. The goal is simple: IT gets visibility and control, teams keep building, no approval queue in between. 6 new capabilities in Zapier today: 1. Action restrictions at the endpoint level 2. Managed app connections with domain control 3. Bring your own model through AWS Bedrock 4. A queryable audit trail 5. Agents GA with MCP governed across every surface 6. Workspaces (GA later this quarter) One set of rules for your entire AI stack. Try it now. If you've had early access, let me know what you think.

Wade Foster

32,542 次观看 • 4 个月前

An interesting issue with Tesla Robotaxi where it took us to a Starbucks, but the Google data had the incorrect location. At drop off we were 0.2 miles from the Starbucks, so we had a short walk. Is there a way the Tesla AI team could add functionality in the app so riders can update map info to correct errors or inaccuracies on the underlying map data and then this propagates to the fleet? Being able to do this with a pin drop on the map instead of having to use an address might make this very easy and user friendly! Or, as a bigger ask, would it be possible for the car to be able to use visual images on its own to look for a Starbucks sign and on the fly, get us closer and update the map data on its own?
0:13

An interesting issue with Tesla Robotaxi where it took us to a Starbucks, but the Google data had the incorrect location. At drop off we were 0.2 miles from the Starbucks, so we had a short walk. Is there a way the Tesla AI team could add functionality in the app so riders can update map info to correct errors or inaccuracies on the underlying map data and then this propagates to the fleet? Being able to do this with a pin drop on the map instead of having to use an address might make this very easy and user friendly! Or, as a bigger ask, would it be possible for the car to be able to use visual images on its own to look for a Starbucks sign and on the fly, get us closer and update the map data on its own?

Joe Tegtmeyer 🚀 🤠🛸😎

80,355 次观看 • 1 年前

" A mattress. Well Maybe for some individuals. " This is something based on a comic I made with Ai in the past. I had the comic animated with Atlabs AI The video pretty much says what everything is about. I honestly like silly comics like this. I really should make more and have them as videos as well. That's another things for a long list of things I need done... Also I plan on running more tests with Omniflash in the future especially when Seedance models may not do things a certain way or have things looking a certain way. Also if anybody is planning on using Atlabs for anything I plan on making a video next month on how I use certain video models and which I typically use. This is specifically on how I use certain models for certain things. Not MG. I plan on having it in a tutorial section. I have hopes this will provide people that use any video models with some needed help when choosing what they should get started with because a Sora video model is no longer an option, which was very integral involving how I would use video models in general. Anyway I have an atlabs link below. If you make an account or have one already I hope what I plan on making next month and showing you, helps a lot. This was made with AtlabsAi. This was all done with Ai. #furrycomedyvideo #furryvideo #aianimation #aifurry
0:10

" A mattress. Well Maybe for some individuals. " This is something based on a comic I made with Ai in the past. I had the comic animated with Atlabs AI The video pretty much says what everything is about. I honestly like silly comics like this. I really should make more and have them as videos as well. That's another things for a long list of things I need done... Also I plan on running more tests with Omniflash in the future especially when Seedance models may not do things a certain way or have things looking a certain way. Also if anybody is planning on using Atlabs for anything I plan on making a video next month on how I use certain video models and which I typically use. This is specifically on how I use certain models for certain things. Not MG. I plan on having it in a tutorial section. I have hopes this will provide people that use any video models with some needed help when choosing what they should get started with because a Sora video model is no longer an option, which was very integral involving how I would use video models in general. Anyway I have an atlabs link below. If you make an account or have one already I hope what I plan on making next month and showing you, helps a lot. This was made with AtlabsAi. This was all done with Ai. #furrycomedyvideo #furryvideo #aianimation #aifurry

CaleAi

11,715 次观看 • 1 个月前

In 2025, the AgentFlayer exploit highlighted a new category of risk in AI systems. It was not a traditional breach involving stolen credentials or broken encryption. Instead, it demonstrated how an autonomous AI agent could be manipulated into executing unintended actions by processing malicious instructions embedded inside content it automatically processes. The incident did not expose a flaw in one specific integration. It revealed a structural weakness in how many modern AI agents are built. Today’s agents are no longer passive language models. They read documents automatically, scan emails, connect to SaaS tools, access cloud storage, and execute actions across multiple systems. To be useful, they are granted meaningful permissions. That capability creates value, but it also expands the attack surface. Most agent environments operate in a trusted, plaintext execution model. Data is encrypted at rest and in transit, but it is typically decrypted during inference so the model can process it. That runtime visibility is where potential risk lies. In a zero-click scenario like AgentFlayer, an attacker can embed hidden instructions inside a document that the AI processes automatically. Because the agent may have access to connected systems such as Google Drive, Slack, or GitHub, it can potentially be influenced to retrieve sensitive information or perform unintended actions. The user does not need to click a malicious link or approve a suspicious request. Therefore, the core issue is that during execution, the system may have access to sensitive data and broad privileges, meaning whoever controls the execution environment ultimately controls access to that data. Now consider a different architectural approach. If a system is designed so that data remains protected during execution, the risk profile changes. On Nesa, privacy is enforced at the execution layer through Equivariant Encryption. Computation can occur on encrypted data, reducing the visibility surface during runtime. Sensitive inputs and models do not need to be exposed in plain text to infrastructure operators for inference to occur. This does not eliminate prompt injection, logic manipulation, or tool misuse. Encryption alone cannot prevent an agent from being instructed to take an unintended action if it has been granted that permission. What it does do is materially reduce confidentiality risk. By limiting access to readable sensitive data during execution and reducing unilateral visibility at the infrastructure layer, the potential blast radius of a successful manipulation attempt is constrained. As AI agents become more autonomous and embedded into enterprise workflows, security must move deeper into architecture. The goal is not to claim invulnerability. It is to reduce trust concentration and contain systemic exposure when failures occur. AgentFlayer was not simply a one-off exploit. It was a reminder that in autonomous systems, execution-layer design determines how risk propagates.
0:11

In 2025, the AgentFlayer exploit highlighted a new category of risk in AI systems. It was not a traditional breach involving stolen credentials or broken encryption. Instead, it demonstrated how an autonomous AI agent could be manipulated into executing unintended actions by processing malicious instructions embedded inside content it automatically processes. The incident did not expose a flaw in one specific integration. It revealed a structural weakness in how many modern AI agents are built. Today’s agents are no longer passive language models. They read documents automatically, scan emails, connect to SaaS tools, access cloud storage, and execute actions across multiple systems. To be useful, they are granted meaningful permissions. That capability creates value, but it also expands the attack surface. Most agent environments operate in a trusted, plaintext execution model. Data is encrypted at rest and in transit, but it is typically decrypted during inference so the model can process it. That runtime visibility is where potential risk lies. In a zero-click scenario like AgentFlayer, an attacker can embed hidden instructions inside a document that the AI processes automatically. Because the agent may have access to connected systems such as Google Drive, Slack, or GitHub, it can potentially be influenced to retrieve sensitive information or perform unintended actions. The user does not need to click a malicious link or approve a suspicious request. Therefore, the core issue is that during execution, the system may have access to sensitive data and broad privileges, meaning whoever controls the execution environment ultimately controls access to that data. Now consider a different architectural approach. If a system is designed so that data remains protected during execution, the risk profile changes. On Nesa, privacy is enforced at the execution layer through Equivariant Encryption. Computation can occur on encrypted data, reducing the visibility surface during runtime. Sensitive inputs and models do not need to be exposed in plain text to infrastructure operators for inference to occur. This does not eliminate prompt injection, logic manipulation, or tool misuse. Encryption alone cannot prevent an agent from being instructed to take an unintended action if it has been granted that permission. What it does do is materially reduce confidentiality risk. By limiting access to readable sensitive data during execution and reducing unilateral visibility at the infrastructure layer, the potential blast radius of a successful manipulation attempt is constrained. As AI agents become more autonomous and embedded into enterprise workflows, security must move deeper into architecture. The goal is not to claim invulnerability. It is to reduce trust concentration and contain systemic exposure when failures occur. AgentFlayer was not simply a one-off exploit. It was a reminder that in autonomous systems, execution-layer design determines how risk propagates.

Nesa

17,038 次观看 • 6 个月前

BREAKING: Democratic Governor Roy Cooper of North Carolina has just vetoed a contentious bill that aimed to prohibit the majority of abortions after 12 weeks. With this said, it is likely that Republicans will attempt to override the veto. Cooper stated: "This bill has nothing to do with making women safer, and everything to do with banning their reproductive freedom." I stand with Cooper on this decision and feel that access to abortions should be between a woman and her doctor. Everyone's situation is different and their beliefs are as well. The government should not determine when or if a woman should be able to have a procedure. I am completely against using abortion as a form of birth control, but I am even more against government deciding what women can and can not do. Thoughts?
0:19

BREAKING: Democratic Governor Roy Cooper of North Carolina has just vetoed a contentious bill that aimed to prohibit the majority of abortions after 12 weeks. With this said, it is likely that Republicans will attempt to override the veto. Cooper stated: "This bill has nothing to do with making women safer, and everything to do with banning their reproductive freedom." I stand with Cooper on this decision and feel that access to abortions should be between a woman and her doctor. Everyone's situation is different and their beliefs are as well. The government should not determine when or if a woman should be able to have a procedure. I am completely against using abortion as a form of birth control, but I am even more against government deciding what women can and can not do. Thoughts?

Brian Krassenstein

1,544,194 次观看 • 3 年前

We're proud to have Ontology - The Trust Layer for Web3 as a core sponsor of Verifying Intelligence during TOKEN2049 week 🤝 Ontology is a public blockchain powering secure identity, data privacy, and user control. With ONT ID, ONTO Wallet - Web3 Gateway, and Orange Protocol, it brings self-sovereign tools like DID and zero-knowledge proofs into real-world Web3 use. No tracking. No middlemen. Just people in control. Verifying Intelligence - Where ZK Meets AI - is an exclusive one-day event exploring the frontier of zero-knowledge proofs and artificial intelligence, and the convergence point between them: Verifiable AI. Co-organized by House of ZK & Boundless, in partnership with Google Cloud. Venue capacity is limited - RSVP now:
0:14

We're proud to have Ontology - The Trust Layer for Web3 as a core sponsor of Verifying Intelligence during TOKEN2049 week 🤝 Ontology is a public blockchain powering secure identity, data privacy, and user control. With ONT ID, ONTO Wallet - Web3 Gateway, and Orange Protocol, it brings self-sovereign tools like DID and zero-knowledge proofs into real-world Web3 use. No tracking. No middlemen. Just people in control. Verifying Intelligence - Where ZK Meets AI - is an exclusive one-day event exploring the frontier of zero-knowledge proofs and artificial intelligence, and the convergence point between them: Verifiable AI. Co-organized by House of ZK & Boundless, in partnership with Google Cloud. Venue capacity is limited - RSVP now:

House of ZK

111,981 次观看 • 11 个月前

We’re excited to announce that Kled has received a $1 million LOI from Superpower to help build the future of healthcare AI. Superpower Health, a $300M healthcare leader, has tasked Kled with unlocking one of the most valuable datasets in the world: how the world’s best doctors deliver care to their patients. Kled is building new healthcare integrations inside our app. Patients will be able to securely connect their healthcare portals and share anonymized chat history from conversations with their doctors. In return, they get paid. That data is then licensed to companies like Superpower Health to power next-generation healthcare AI, always with user consent and transparency. This supports Superpower’s mission to make the world’s best healthcare accessible to everyone. This is just the beginning. Healthcare is one of the most important uses of AI, and Kled is positioned to be at the center of it.
0:14

We’re excited to announce that Kled has received a $1 million LOI from Superpower to help build the future of healthcare AI. Superpower Health, a $300M healthcare leader, has tasked Kled with unlocking one of the most valuable datasets in the world: how the world’s best doctors deliver care to their patients. Kled is building new healthcare integrations inside our app. Patients will be able to securely connect their healthcare portals and share anonymized chat history from conversations with their doctors. In return, they get paid. That data is then licensed to companies like Superpower Health to power next-generation healthcare AI, always with user consent and transparency. This supports Superpower’s mission to make the world’s best healthcare accessible to everyone. This is just the beginning. Healthcare is one of the most important uses of AI, and Kled is positioned to be at the center of it.

Kled AI

116,855 次观看 • 11 个月前

I genuinely think the Terafab is going to end up being one of the biggest moves ever made in human history to secure the future of AI... and I think most people still don’t fully see what Elon is trying to do here. The signs are clear to me. This is Tesla, xAI, and SpaceX essentially hinting to us that they are not going to wait on the world to give them the compute the team needs. They are going to build it themselves at a scale no one has ever attempted. When you really break it down, it gets a bit nutty. This is going to be a fully vertically integrated chip factory that will be producing over 1 terawatt of AI compute per year. This is NEXT LEVEL BIG. Today, AI is limited by chips. You can have the best models, the best engineers, the best everything... but if you don’t have enough compute, you will eventually hit a wall. Elon told us, the world can only supply a tiny fraction of the chips his companies will need. So this is the solution. Terafab puts everything under one roof like design, manufacturing, memory, packaging, testing, which means that they can build chips very fast.. like really fast. I'm talking about 100-200 billion custom AI chips per year at full capacity. Chips designed specifically for: • Tesla cars and Optimus robots • xAI models • Space-based compute You see, while other companies and CEOs are thinking Earth, Elon is planning for AI in space. Around ~80% of the compute is expected to go orbital, powered by solar energy bc Earth simply doesn’t have enough electricity. The U.S. grid is only about ~0.5 terawatts, while space has basically UNLIMITED energy if you can capture it. And this is the steps to get it: Starship launches → space compute → solar-powered AI → feeds back into everything to Earth. Bro... Elon and his companies are playing at a whole different level... And this is why I keep telling people that the Terafab is going to be the secret ingredient that will be the real unlock for everything: • Robotaxis at scale • Billions of Optimus robots • Massive AI models running 24/7 • Future off-world, other planet infrastructure Without these chips, none of this can happen... but with the Terafab, all of this becomes possible. That’s why Elon is calling it “the final missing piece.” I agree.
0:10

I genuinely think the Terafab is going to end up being one of the biggest moves ever made in human history to secure the future of AI... and I think most people still don’t fully see what Elon is trying to do here. The signs are clear to me. This is Tesla, xAI, and SpaceX essentially hinting to us that they are not going to wait on the world to give them the compute the team needs. They are going to build it themselves at a scale no one has ever attempted. When you really break it down, it gets a bit nutty. This is going to be a fully vertically integrated chip factory that will be producing over 1 terawatt of AI compute per year. This is NEXT LEVEL BIG. Today, AI is limited by chips. You can have the best models, the best engineers, the best everything... but if you don’t have enough compute, you will eventually hit a wall. Elon told us, the world can only supply a tiny fraction of the chips his companies will need. So this is the solution. Terafab puts everything under one roof like design, manufacturing, memory, packaging, testing, which means that they can build chips very fast.. like really fast. I'm talking about 100-200 billion custom AI chips per year at full capacity. Chips designed specifically for: • Tesla cars and Optimus robots • xAI models • Space-based compute You see, while other companies and CEOs are thinking Earth, Elon is planning for AI in space. Around ~80% of the compute is expected to go orbital, powered by solar energy bc Earth simply doesn’t have enough electricity. The U.S. grid is only about ~0.5 terawatts, while space has basically UNLIMITED energy if you can capture it. And this is the steps to get it: Starship launches → space compute → solar-powered AI → feeds back into everything to Earth. Bro... Elon and his companies are playing at a whole different level... And this is why I keep telling people that the Terafab is going to be the secret ingredient that will be the real unlock for everything: • Robotaxis at scale • Billions of Optimus robots • Massive AI models running 24/7 • Future off-world, other planet infrastructure Without these chips, none of this can happen... but with the Terafab, all of this becomes possible. That’s why Elon is calling it “the final missing piece.” I agree.

Teslaconomics

25,494 次观看 • 5 个月前

Larry Fink: The world is going to be short power. After years of pressuring companies and countries to "transition" to renewables, Larry has done a 180. - Data centers need dispatchable power - Renewables won't get the job done Funny how the arrival of AI suddenly erased all the concerns about climate change. I rarely agree with Larry, but the world needs far more baseload power: - Coal - Natural gas - Nuclear - Maybe even diesel All this after more than a decade of underinvestment in these sectors, thanks in no small part to people like Larry. The irony is that the very people who discouraged investment in reliable energy are now warning the world that it doesn't have enough.
0:17

Larry Fink: The world is going to be short power. After years of pressuring companies and countries to "transition" to renewables, Larry has done a 180. - Data centers need dispatchable power - Renewables won't get the job done Funny how the arrival of AI suddenly erased all the concerns about climate change. I rarely agree with Larry, but the world needs far more baseload power: - Coal - Natural gas - Nuclear - Maybe even diesel All this after more than a decade of underinvestment in these sectors, thanks in no small part to people like Larry. The irony is that the very people who discouraged investment in reliable energy are now warning the world that it doesn't have enough.

Lukas Ekwueme

11,816 次观看 • 1 个月前

“There is a tension between what the users of a currency want – and the users of a currency tend to like freedom, autonomy, and discretion as to what they spent their money on – and what the issuers of a currency want; and bluntly, the issuers of a currency want control. Control of monetary policy, and control of you.” The Bank of England’s consultation papers make very clear the level of control that they wish to exercise over you, and over your supposed financial autonomy, if you were to use their #DigitalPound. (1) You’ll need to provide ID in order to use the #DigitalPound: “For the digital pound, tiered access would allow for different levels of user access and functionality based on the amount of identification (ID) a user is willing or able to provide.” (2) The Bank will dictate how much you can hold: “The Bank would place some limits on holdings of digital pounds, at least during its introductory period.” (3) The digital pound will be programmable, if not by the Bank itself then by third party providers: “Programmability, delivered by Payment Interface Providers, could also enable the use of smart contracts, which carry out specific actions based on pre-defined terms and conditions.” Quotes are from from the Bank of England’s Digital Pound consultation paper: Whatever the #DigitalPound will be, it won’t be cash. Cash does not require me to show ID to use it. I can hold as much cash as I want or need. And, along with #Bitcoin, cash is a bearer instrument whose title is freely transferrable upon delivery, which is very difficult for a central bank to control. And long may it stay this way. A huge shout out and thank you to Lyn Alden, who made this point much more eloquently than I did in her excellent book #BrokenMoney. Thank you! Also I’m aware that my hand gestures in this clip are reminiscent of Richard Hendricks manipulating ‘datas’ on stage at TechCrunch Disrupt in #SiliconValley, and for this I can only apologize: #BitcoinConference #Amsterdam #NoToCBDCs
0:19

“There is a tension between what the users of a currency want – and the users of a currency tend to like freedom, autonomy, and discretion as to what they spent their money on – and what the issuers of a currency want; and bluntly, the issuers of a currency want control. Control of monetary policy, and control of you.” The Bank of England’s consultation papers make very clear the level of control that they wish to exercise over you, and over your supposed financial autonomy, if you were to use their #DigitalPound. (1) You’ll need to provide ID in order to use the #DigitalPound: “For the digital pound, tiered access would allow for different levels of user access and functionality based on the amount of identification (ID) a user is willing or able to provide.” (2) The Bank will dictate how much you can hold: “The Bank would place some limits on holdings of digital pounds, at least during its introductory period.” (3) The digital pound will be programmable, if not by the Bank itself then by third party providers: “Programmability, delivered by Payment Interface Providers, could also enable the use of smart contracts, which carry out specific actions based on pre-defined terms and conditions.” Quotes are from from the Bank of England’s Digital Pound consultation paper: Whatever the #DigitalPound will be, it won’t be cash. Cash does not require me to show ID to use it. I can hold as much cash as I want or need. And, along with #Bitcoin, cash is a bearer instrument whose title is freely transferrable upon delivery, which is very difficult for a central bank to control. And long may it stay this way. A huge shout out and thank you to Lyn Alden, who made this point much more eloquently than I did in her excellent book #BrokenMoney. Thank you! Also I’m aware that my hand gestures in this clip are reminiscent of Richard Hendricks manipulating ‘datas’ on stage at TechCrunch Disrupt in #SiliconValley, and for this I can only apologize: #BitcoinConference #Amsterdam #NoToCBDCs

Freddie New

13,162 次观看 • 2 年前