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🎙️ CloudCast Ep 03 w/ Swissy just dropped 🔥 Dom’s DFINITY Foundation | $ICP 2.0 roadmap is BULLISH as hell Caffeine → Utopia → Convo → Cloud Engines → Mission 70 ☕🌐⛓️🤯 From Swiss R&D lab → full-blown tech venture... ICP isn’t fighting Amazon Web Services — it’s about...

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14,937 次观看 • 9 个月前 •via X (Twitter)

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🚨 JAN GOES WILD — Calls for $70 $ICP! 🐂🚀 DFINITY Foundation 2.0 explained, caffeine, Big Tech cloud partnerships, and more. Tap in. A Cloud Foundation Production ☁️♾🍿 $ICP $CLOUD jan Cloud Foundation ☁️ dom | icp
3:59

🚨 JAN GOES WILD — Calls for $70 $ICP! 🐂🚀 DFINITY Foundation 2.0 explained, caffeine, Big Tech cloud partnerships, and more. Tap in. A Cloud Foundation Production ☁️♾🍿 $ICP $CLOUD jan Cloud Foundation ☁️ dom | icp

ChrisYost.icp ∞

20,105 次观看 • 10 个月前

$ICP 's new cloud engine demo by dom | icp shows how easy it is to build cloud engines through ICP🤯 Some more great work from DFINITY Foundation as anyone can now built a decentralised cloud engine in genuinely less than hour🤯 Crazy what $ICP is doing, they're creating the future!
1:36

$ICP 's new cloud engine demo by dom | icp shows how easy it is to build cloud engines through ICP🤯 Some more great work from DFINITY Foundation as anyone can now built a decentralised cloud engine in genuinely less than hour🤯 Crazy what $ICP is doing, they're creating the future!

ALLINCRYPTO

13,284 次观看 • 4 个月前

INTERNET COMPUTER'S CLOUD ENGINES REPRESENT AN INFLECTION POINT... Founder of Internet Computer (DFINITY Foundation), Dominic Williams (dom | icp), while celebrating the protocol's five-year journey, provided a detailed preview of “Cloud Engines." Cloud Engines is the sovereign frontier cloud technology that the network will soon provide via opencloud(.)org. He stated that Cloud Engines will enable anyone to spin up their own tamperproof sovereign cloud by selecting and configuring nodes within the mathematically secure Internet Computer network. The founder emphasized that the technology is built specifically for the AI era, enabling AI agents to create and update online applications and services without the need for security teams or sysadmins. Additionally, software hosted on Cloud Engines is immune to infrastructure hacks, guaranteed to run as long as sufficient nodes remain operational, and protected by orthogonal persistence in the Motoko language, which automatically detects and prevents “lossy” AI-generated updates. He added that users will soon be able to develop and deploy apps directly from AI platforms such as Claude or Perplexity, with seamless integration through caffeine, while maintaining full tech sovereignty. Cloud Engines allow owners to choose node operators and locations, mix Big Tech instances (Amazon Web Services, Microsoft Azure, Google), add sovereign AI nodes, and scale capacity instantly without downtime or vendor lock-in. In summary, Cloud Engines is an inflection point in the Internet Computer’s history.
0:30

INTERNET COMPUTER'S CLOUD ENGINES REPRESENT AN INFLECTION POINT... Founder of Internet Computer (DFINITY Foundation), Dominic Williams (dom | icp), while celebrating the protocol's five-year journey, provided a detailed preview of “Cloud Engines." Cloud Engines is the sovereign frontier cloud technology that the network will soon provide via opencloud(.)org. He stated that Cloud Engines will enable anyone to spin up their own tamperproof sovereign cloud by selecting and configuring nodes within the mathematically secure Internet Computer network. The founder emphasized that the technology is built specifically for the AI era, enabling AI agents to create and update online applications and services without the need for security teams or sysadmins. Additionally, software hosted on Cloud Engines is immune to infrastructure hacks, guaranteed to run as long as sufficient nodes remain operational, and protected by orthogonal persistence in the Motoko language, which automatically detects and prevents “lossy” AI-generated updates. He added that users will soon be able to develop and deploy apps directly from AI platforms such as Claude or Perplexity, with seamless integration through caffeine, while maintaining full tech sovereignty. Cloud Engines allow owners to choose node operators and locations, mix Big Tech instances (Amazon Web Services, Microsoft Azure, Google), add sovereign AI nodes, and scale capacity instantly without downtime or vendor lock-in. In summary, Cloud Engines is an inflection point in the Internet Computer’s history.

BSCN

12,159 次观看 • 4 个月前

$ICP is under attack 🚨 I share my thoughts on: - the latest $ICP FUD campaign - why DFINITY Foundation should pay its own creators/eco developers for the support they've provided over the past 4-5 years, instead of FUD posters - creators that have given up because of the lack of opportunities/monetization inside the eco (e.g. @dehypokriet w/ caffeine) - BasedGiant reaching out to help DFINITY Foundation fix its social media pages & getting no response (while the DFINITY Foundation & caffeine handles have 5 total posts in August) People like Jerry Banfield, Cloud Foundation ☁️, HERO, Michael Duffy, BasedGiant, myself, and many others that I have not named in this video have made content for the past 4-5 years, and while I've been fortunate enough to have a closer relationship with DFINITY, get invited to various events where I helped produce content for them, many others haven't, and this should change. dom | icp & Pierre I think you are not aware of what I'm talking about in the video, so I'll appreciate it if you watch it There are many people inside the ecosystem who want to help $ICP win, so take us into consideration 🤝
26:55

$ICP is under attack 🚨 I share my thoughts on: - the latest $ICP FUD campaign - why DFINITY Foundation should pay its own creators/eco developers for the support they've provided over the past 4-5 years, instead of FUD posters - creators that have given up because of the lack of opportunities/monetization inside the eco (e.g. @dehypokriet w/ caffeine) - BasedGiant reaching out to help DFINITY Foundation fix its social media pages & getting no response (while the DFINITY Foundation & caffeine handles have 5 total posts in August) People like Jerry Banfield, Cloud Foundation ☁️, HERO, Michael Duffy, BasedGiant, myself, and many others that I have not named in this video have made content for the past 4-5 years, and while I've been fortunate enough to have a closer relationship with DFINITY, get invited to various events where I helped produce content for them, many others haven't, and this should change. dom | icp & Pierre I think you are not aware of what I'm talking about in the video, so I'll appreciate it if you watch it There are many people inside the ecosystem who want to help $ICP win, so take us into consideration 🤝

Alex (BCP)

23,008 次观看 • 29 天前

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!
19:06

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!

dom | icp

311,030 次观看 • 4 个月前

🚨 THEY’RE NOT JUST WATCHING YOU THEY’RE INSIDE YOUR MIND RIGHT NOW MK ULTRA 2.0 is LIVE. 🚨 Amazon, X, Facebook, all social media's, ANOTHER PURPOSE FOR THE DATA CENTERS! Google's CIA-built cloud isn’t storing your photos… It’s mapping your thoughts, syncing your brainwaves, and turning humanity into remote-controlled puppets. This isn’t sci-fi. This is the silent war they never told you about. Watch this explosive video exposing: - Whistleblower Bryan Kofron revealing Security Industry Specialists’ role in targeting citizens. - DARPA brain implants & neural interfaces. - Patents that prove they can read, write, and hijack your brain activity. - How Amazon Web Services + CIA tech = Electronic Brain Link for mass control. Reply and Tag 5 Friends! Let’s break the matrix! If your thoughts can be read and influenced through your phone/cloud… how many of your “decisions” |are actually yours? Why is Amazon (CIA contractor) pushing Neural tech while everyone’s addicted to Alexa and Prime? Have you felt random mood swings, brain fog, or “forgotten” thoughts lately? Could it be more than stress? Who benefits from a population that’s anxious, divided, and easily triggered? Follow the patents. What’s your first step to reclaim your mind? (No fluoride, ground yourself, detox, awareness?) DROP a like if you’re not their puppet anymore. RT + Comment your #1 question Below. The algorithm can’t hide this forever. Share this with 5 people who still think it’s “just a conspiracy.” The Great Awakening isn’t coming... it’s HERE. Let me know what you think, and SHARE THIS so that others may too! And check out the thread below 👇
44:24

🚨 THEY’RE NOT JUST WATCHING YOU THEY’RE INSIDE YOUR MIND RIGHT NOW MK ULTRA 2.0 is LIVE. 🚨 Amazon, X, Facebook, all social media's, ANOTHER PURPOSE FOR THE DATA CENTERS! Google's CIA-built cloud isn’t storing your photos… It’s mapping your thoughts, syncing your brainwaves, and turning humanity into remote-controlled puppets. This isn’t sci-fi. This is the silent war they never told you about. Watch this explosive video exposing: - Whistleblower Bryan Kofron revealing Security Industry Specialists’ role in targeting citizens. - DARPA brain implants & neural interfaces. - Patents that prove they can read, write, and hijack your brain activity. - How Amazon Web Services + CIA tech = Electronic Brain Link for mass control. Reply and Tag 5 Friends! Let’s break the matrix! If your thoughts can be read and influenced through your phone/cloud… how many of your “decisions” |are actually yours? Why is Amazon (CIA contractor) pushing Neural tech while everyone’s addicted to Alexa and Prime? Have you felt random mood swings, brain fog, or “forgotten” thoughts lately? Could it be more than stress? Who benefits from a population that’s anxious, divided, and easily triggered? Follow the patents. What’s your first step to reclaim your mind? (No fluoride, ground yourself, detox, awareness?) DROP a like if you’re not their puppet anymore. RT + Comment your #1 question Below. The algorithm can’t hide this forever. Share this with 5 people who still think it’s “just a conspiracy.” The Great Awakening isn’t coming... it’s HERE. Let me know what you think, and SHARE THIS so that others may too! And check out the thread below 👇

Noah B. Price

11,783 次观看 • 3 个月前

A video-text summary of my argument that we now live in the age of TECHNOFEUDALISM (in 16', 2000 words): Wherever we turn, we witness the triumph of capital. Capital has prevailed everywhere: in warehouses, factories, offices, universities, public hospitals, the media – in space but also in the microcosm of genetic engineering. So, how do I dare claim that capitalism has been killed? By whom? The deliciously ironic answer is that capitalism was killed by its own hand… by capital! If I am right, the issue is not what AI will do to us in the future but what has already happened: Capital became so dominant that it mutated into a variant so toxic that, like a stupid virus, it killed off its host, capitalism, replacing it with something far, far worse. This new mutant capital, that killed capitalism, lives in the proverbial cloud – so, let us call it cloud capital. What is cloud capital? What makes it so different? Cloud capital, of course, does not really live up in the cloud. It lives down on Earth, comprising networked machines, server farms, cell towers, software, AI-driven algorithms – and of course it lives on our oceans’ floors where untold miles of optic fibre cables rest. Unlike traditional capital, from fishing rods to the steam-engines of the Industrial Revolution to today’s modern industrial robots that are produced means of production, cloud capital does not produce anything – it comprises machines manufactured so as to modify human behaviour. That’s what Amazon’s Alexa or Google’s Assistant or Apple’s Siri is: It is a produced means of behavioural modification. It is a machine, a piece of capital, which we train to train us to train it to determine that which we want. And, once we want it, the same networked machine sells it to us, directly, bypassing markets. As if that were not enough, the same machinery succeeds in making us sustain the enormous behavioural modification machine network to which it belongs with our free voluntary labour. We are sustaining it as we post reviews, rate products, upload videos, rants, photos - we help reproduce cloud capital without getting a penny for our labour. In essence, it has turned us into its cloud serfs! Meanwhile, in the factories and the warehouses, where waged proletarians work under increasingly precarious conditions, the same algorithms that modify our behaviour and sell products to us directly – those algorithms are deployed, usually by digital devices tied to the workers’ wrists, to make proletarians, workers in the warehouses, in the factories work faster, to direct and to monitor them in real time. I started by saying that wherever we turn, we stumble on the triumph of capital. But it is cloud capital that is the real winner. It is amazing how it performs, at once, five roles that used to be beyond capital’s capacities: Cloud capital grabs our attention. It manufactures our desires. It sells to us, directly, outside any traditional markets, that which is going to satiate the desires it made us have. It drives proletarian labour inside the workplaces. And it elicits massive free labour from us, its cloud-serfs. Is it surprising that the owners of this cloud capital – let’s call them cloudalists – have a hitherto undreamt power to extract? To extract gargantuan surplus value from proletarians; untold quantities of free labour from almost everyone; and mind-numbing cloud rents from vassal capitalists – from sellers? Is it a wonder that they are vastly more powerful than Henry Ford or Rupert Murdoch could ever be? “Hang on”, I hear you say. “Is Jeff Bezos really different to Henry Ford? Aren’t they all a species of monopoly capitalists? Monopolists?” No, is not a monopolistic capitalist enterprise. The moment you enter you have exited capitalism altogether! Sure enough, the place is teaming with buyers and sellers. So, yes, it is an enormous trading platform but, no, a market it certainly is not! One man called Jeff owns everything. But he is much, much more than a mere monopolist. Jeff doesn’t own the factories that produce the stuff sold on his platform by traditional capitalists who have to use it to ply their trade. What he does own is more important: Jeff owns the algorithm that decides which products you see and which you don’t – the very algorithm that you have trained to know you perfectly so that it matches youwith a seller, whom it also knows perfectly well, with a view to maximising the probability that every such match, transaction, will generate, for Jeff, the highest rent that Jeff can charge the seller for what you buy: up to 40% of what you pay is pocketed by Jeff, the cloudalist! The mind rebels at the enormity but also the radical novelty of this kind of exploitation: The same algorithm that we help train in real time to know us inside out - that same algorithm both modifies our preferences and administers the selection and delivery of commodities that will satisfy these preferences. If you and I were to type “electric bicycles” or “binoculars” while in you and I would get totally different recommendations. In a traditional market or shopping mall it would be as if you and I were walking next to each other, our eyes trained in the same direction, the same shop window, but we were to see different things depending on what Jeff’s algorithm wants each one of us to see. Everyone navigating around – except Jeff Bezos of course – everyone in is wandering around in algorithmically constructed isolation as if in a Panopticon where, unable to see each other, we only see Jeff’s all-seeing algorithm or, more accurately, only what his algorithm allows us to see with a view to maximising his cloud rent – which is, of course, today’s version of the ground rent that the feudal lords used to extract from their vassals and their peasants. This is not capitalism. Ladies and gentlemen, welcome to technofeudalism! How did cloud capital kill capitalism? How did it rise up? Who paid for it? Capitalism, lest we forget, had two pillars: markets and profit. Of course, markets and profit remain ubiquitous. Nevertheless, cloud capital has evicted both markets and profit from the centre of our socioeconomic system, pushing them out to its margins, and replacing them: Markets, the medium of capitalism, have been replaced by cloud fiefs – digital trading platforms like or Alibaba which, as we saw, look like, but are not, markets. And Profit? The fuel of capitalism? Well, that has been replaced by its feudal predecessor: rent. But, specifically, a new form of rent, a cloud rent that must be paid for access to those cloud fiefs or digital platforms. But how did cloud capital emerge?It began life in the late 1990s when the original Internet, which was a Commons – it functioned as a capitalism-free-zone – that original Internet, Internet 1.0 if you want, was privatised by the emergent Big Tech. Who paid for the trillions it cost to manufacture and to accumulate cloud capital so quickly in the hands of so very few cloudalists? The startling answer is: The G7 countries’ central banks, mostly! How did that happen? Well, by accident, or – to be more precise – by… crisis! After the financial sector collapse of 2008, our central bankers printed up to $35 trillion to bail out the bankers at a time when the governments were subjecting our peoples to harsh austerity. Capitalists were clever enough to foresee that the many would be too impecunious to buy their stuff. So, instead of investing, they took the central bank money to the stock exchange and the bond markets, where they bought shares, bonds – along with yachts, art, bitcoin, NFTs any ‘asset’ they could lay their hands on. The only capitalists who actually invested in capital were Big Tech owners. For example, 9 out of every 10 dollars that went into creating Facebook came from these central bank monies! That’s how cloud capital was financed and how the cloudalists became our new ruling class. As a result, real power today resides not with the owners of machinery, buildings, railway and phone networks, industrial robots. These old-fashioned, terrestrial capitalists continue to extract surplus value from waged labour, but they are no longer in charge, as they used to be. They have become vassals in relation to the owners of cloud capital, of the cloudalists. As for the rest of us, we have returned to our former status as serfs, contributing to the wealth and power of the new ruling class with our unpaid labour — in addition to the waged labour we perform, when we get the chance to do it. But surely, someone will say, this is still capitalism, isn’t it? So, you are still unconvinced? I know, it is hard to part with the term, with the word, capitalism. It is not just liberals who think of capitalism like fish think of the water they swim in – as natural. Socialists too need to feel that our purpose in life, the reason we landed on this Earth, is to overthrow capitalism. The news that I bring that capital beat us to it, and now we have something worse in capitalism’s place, that news is hard to accept. Indeed, it is mostly my fellow-travelling leftist friends who try to dissuade me – to convince me that, yes, cloud capital may be important but “this is still capitalism mate”. Let’s call it rentier capitalism or monopoly capitalism, they suggest. But that simply will not do! Cloud rent is not like ground rent, because it requires massive investment in new tech. And it is not monopoly rent either, because Bezos and Zuckerberg, instead of monopolising markets to sell their manufactures (like Ford and Eddison did), Bezos and Zuckerberg have replaced markets and have no interest in manufacturing anything (unlike Henry Ford and Thomas Eddison). How about surveillance capitalism? Again, no, it won’t do. Cloudalists do not simply use algorithms to brain wash us on behalf of advertisers in an otherwise capitalist setting. No, cloud capital reproduces itself through our free-labour, it directly exploits waged labour, and it squeezes cloud rents from vassal capitalists in trading platforms that are not markets. This is not capitalism folks! Any kind of capitalism. But what about the observation that technofeudalism is parasitic on the capitalist sector within it? Yes, it is true. Were the conventional capitalists to die out, cloudalists would perish, unable to skim off cloud rents from the manufacturers. So what? After capitalism overthrew feudalism, capitalists were also parasitic on landowners, in the sense that, without private land producing food, capitalism would wither. Similarly, now: While the traditional capitalist sector feeds technofeudalism, it is cloud capital and cloud rent that dominate. Does it matter whether we call it technofeudalism or some form of capitalism? At this point, it is important to recall Marx’s maxim that the point is not to interpret but to change the world. So, does it matter if this is still capitalism or whether we call it technofeudalism? I think it does. Recognising that our world has become technofeudal helps us grasp the enormity of what it will take to organise the victims of exorbitant power, the exploited who, now, include not only waged labourers but also the hordes of cloud serfs who are reproducing the very cloud capital that keeps them in a state of deepening precarity. The concept of technofeudalism drives home the point that organising auto-workers and nurses, while still essential, is insufficient. It elucidates what it will take to organise the movements against the fossil fuel cartel when our means of communication are run on cloud capital primed to poison public opinion. It explains how the shift to electric cars caused German deindustrialisation, as profits due to precision mechanical engineering are being replaced by rents extracted by owners of the cloud capital keeping tabs on the drivers’ routes and in-cabin habits. Elon Musk’s decision to buy Twitter suddenly makes a lot more sense. Twitter for Musk is an interface between his mechanical capital stock at Tesla and SpaceX and cloud capital. The New Cold War between the USA and China, especially after the war in Ukraine, is explained as the repercussion of an underlying clash between two technofeudalisms, one whose cloud rents are denominated in dollars the other in yuan. Isn’t it mindboggling? It took mind-bending scientific breakthroughs, fantastical neural networks, and imagination-defying AI programs to accomplish what? To create a world where, while privatisation and private equity asset-strip all physical wealth around us, cloud capital goes about the business of asset-stripping our brains. To own our minds individually, we must own cloud capital collectively. Once we have reclaimed our minds, we can put them collectively to work out a way to create a new cloud capital commons. It will be damned hard. But it’s the only way we can turn our cloud-based artefacts from a produced means of behaviour modification to a produced means of human collaboration and emancipation. Cloud serfs, cloud proles and cloud vassals of the world, unite! We have nothing to lose but our mind-cloud chains! US Edition: UK Edition: Greek Edition:
17:20

A video-text summary of my argument that we now live in the age of TECHNOFEUDALISM (in 16', 2000 words): Wherever we turn, we witness the triumph of capital. Capital has prevailed everywhere: in warehouses, factories, offices, universities, public hospitals, the media – in space but also in the microcosm of genetic engineering. So, how do I dare claim that capitalism has been killed? By whom? The deliciously ironic answer is that capitalism was killed by its own hand… by capital! If I am right, the issue is not what AI will do to us in the future but what has already happened: Capital became so dominant that it mutated into a variant so toxic that, like a stupid virus, it killed off its host, capitalism, replacing it with something far, far worse. This new mutant capital, that killed capitalism, lives in the proverbial cloud – so, let us call it cloud capital. What is cloud capital? What makes it so different? Cloud capital, of course, does not really live up in the cloud. It lives down on Earth, comprising networked machines, server farms, cell towers, software, AI-driven algorithms – and of course it lives on our oceans’ floors where untold miles of optic fibre cables rest. Unlike traditional capital, from fishing rods to the steam-engines of the Industrial Revolution to today’s modern industrial robots that are produced means of production, cloud capital does not produce anything – it comprises machines manufactured so as to modify human behaviour. That’s what Amazon’s Alexa or Google’s Assistant or Apple’s Siri is: It is a produced means of behavioural modification. It is a machine, a piece of capital, which we train to train us to train it to determine that which we want. And, once we want it, the same networked machine sells it to us, directly, bypassing markets. As if that were not enough, the same machinery succeeds in making us sustain the enormous behavioural modification machine network to which it belongs with our free voluntary labour. We are sustaining it as we post reviews, rate products, upload videos, rants, photos - we help reproduce cloud capital without getting a penny for our labour. In essence, it has turned us into its cloud serfs! Meanwhile, in the factories and the warehouses, where waged proletarians work under increasingly precarious conditions, the same algorithms that modify our behaviour and sell products to us directly – those algorithms are deployed, usually by digital devices tied to the workers’ wrists, to make proletarians, workers in the warehouses, in the factories work faster, to direct and to monitor them in real time. I started by saying that wherever we turn, we stumble on the triumph of capital. But it is cloud capital that is the real winner. It is amazing how it performs, at once, five roles that used to be beyond capital’s capacities: Cloud capital grabs our attention. It manufactures our desires. It sells to us, directly, outside any traditional markets, that which is going to satiate the desires it made us have. It drives proletarian labour inside the workplaces. And it elicits massive free labour from us, its cloud-serfs. Is it surprising that the owners of this cloud capital – let’s call them cloudalists – have a hitherto undreamt power to extract? To extract gargantuan surplus value from proletarians; untold quantities of free labour from almost everyone; and mind-numbing cloud rents from vassal capitalists – from sellers? Is it a wonder that they are vastly more powerful than Henry Ford or Rupert Murdoch could ever be? “Hang on”, I hear you say. “Is Jeff Bezos really different to Henry Ford? Aren’t they all a species of monopoly capitalists? Monopolists?” No, is not a monopolistic capitalist enterprise. The moment you enter you have exited capitalism altogether! Sure enough, the place is teaming with buyers and sellers. So, yes, it is an enormous trading platform but, no, a market it certainly is not! One man called Jeff owns everything. But he is much, much more than a mere monopolist. Jeff doesn’t own the factories that produce the stuff sold on his platform by traditional capitalists who have to use it to ply their trade. What he does own is more important: Jeff owns the algorithm that decides which products you see and which you don’t – the very algorithm that you have trained to know you perfectly so that it matches youwith a seller, whom it also knows perfectly well, with a view to maximising the probability that every such match, transaction, will generate, for Jeff, the highest rent that Jeff can charge the seller for what you buy: up to 40% of what you pay is pocketed by Jeff, the cloudalist! The mind rebels at the enormity but also the radical novelty of this kind of exploitation: The same algorithm that we help train in real time to know us inside out - that same algorithm both modifies our preferences and administers the selection and delivery of commodities that will satisfy these preferences. If you and I were to type “electric bicycles” or “binoculars” while in you and I would get totally different recommendations. In a traditional market or shopping mall it would be as if you and I were walking next to each other, our eyes trained in the same direction, the same shop window, but we were to see different things depending on what Jeff’s algorithm wants each one of us to see. Everyone navigating around – except Jeff Bezos of course – everyone in is wandering around in algorithmically constructed isolation as if in a Panopticon where, unable to see each other, we only see Jeff’s all-seeing algorithm or, more accurately, only what his algorithm allows us to see with a view to maximising his cloud rent – which is, of course, today’s version of the ground rent that the feudal lords used to extract from their vassals and their peasants. This is not capitalism. Ladies and gentlemen, welcome to technofeudalism! How did cloud capital kill capitalism? How did it rise up? Who paid for it? Capitalism, lest we forget, had two pillars: markets and profit. Of course, markets and profit remain ubiquitous. Nevertheless, cloud capital has evicted both markets and profit from the centre of our socioeconomic system, pushing them out to its margins, and replacing them: Markets, the medium of capitalism, have been replaced by cloud fiefs – digital trading platforms like or Alibaba which, as we saw, look like, but are not, markets. And Profit? The fuel of capitalism? Well, that has been replaced by its feudal predecessor: rent. But, specifically, a new form of rent, a cloud rent that must be paid for access to those cloud fiefs or digital platforms. But how did cloud capital emerge?It began life in the late 1990s when the original Internet, which was a Commons – it functioned as a capitalism-free-zone – that original Internet, Internet 1.0 if you want, was privatised by the emergent Big Tech. Who paid for the trillions it cost to manufacture and to accumulate cloud capital so quickly in the hands of so very few cloudalists? The startling answer is: The G7 countries’ central banks, mostly! How did that happen? Well, by accident, or – to be more precise – by… crisis! After the financial sector collapse of 2008, our central bankers printed up to $35 trillion to bail out the bankers at a time when the governments were subjecting our peoples to harsh austerity. Capitalists were clever enough to foresee that the many would be too impecunious to buy their stuff. So, instead of investing, they took the central bank money to the stock exchange and the bond markets, where they bought shares, bonds – along with yachts, art, bitcoin, NFTs any ‘asset’ they could lay their hands on. The only capitalists who actually invested in capital were Big Tech owners. For example, 9 out of every 10 dollars that went into creating Facebook came from these central bank monies! That’s how cloud capital was financed and how the cloudalists became our new ruling class. As a result, real power today resides not with the owners of machinery, buildings, railway and phone networks, industrial robots. These old-fashioned, terrestrial capitalists continue to extract surplus value from waged labour, but they are no longer in charge, as they used to be. They have become vassals in relation to the owners of cloud capital, of the cloudalists. As for the rest of us, we have returned to our former status as serfs, contributing to the wealth and power of the new ruling class with our unpaid labour — in addition to the waged labour we perform, when we get the chance to do it. But surely, someone will say, this is still capitalism, isn’t it? So, you are still unconvinced? I know, it is hard to part with the term, with the word, capitalism. It is not just liberals who think of capitalism like fish think of the water they swim in – as natural. Socialists too need to feel that our purpose in life, the reason we landed on this Earth, is to overthrow capitalism. The news that I bring that capital beat us to it, and now we have something worse in capitalism’s place, that news is hard to accept. Indeed, it is mostly my fellow-travelling leftist friends who try to dissuade me – to convince me that, yes, cloud capital may be important but “this is still capitalism mate”. Let’s call it rentier capitalism or monopoly capitalism, they suggest. But that simply will not do! Cloud rent is not like ground rent, because it requires massive investment in new tech. And it is not monopoly rent either, because Bezos and Zuckerberg, instead of monopolising markets to sell their manufactures (like Ford and Eddison did), Bezos and Zuckerberg have replaced markets and have no interest in manufacturing anything (unlike Henry Ford and Thomas Eddison). How about surveillance capitalism? Again, no, it won’t do. Cloudalists do not simply use algorithms to brain wash us on behalf of advertisers in an otherwise capitalist setting. No, cloud capital reproduces itself through our free-labour, it directly exploits waged labour, and it squeezes cloud rents from vassal capitalists in trading platforms that are not markets. This is not capitalism folks! Any kind of capitalism. But what about the observation that technofeudalism is parasitic on the capitalist sector within it? Yes, it is true. Were the conventional capitalists to die out, cloudalists would perish, unable to skim off cloud rents from the manufacturers. So what? After capitalism overthrew feudalism, capitalists were also parasitic on landowners, in the sense that, without private land producing food, capitalism would wither. Similarly, now: While the traditional capitalist sector feeds technofeudalism, it is cloud capital and cloud rent that dominate. Does it matter whether we call it technofeudalism or some form of capitalism? At this point, it is important to recall Marx’s maxim that the point is not to interpret but to change the world. So, does it matter if this is still capitalism or whether we call it technofeudalism? I think it does. Recognising that our world has become technofeudal helps us grasp the enormity of what it will take to organise the victims of exorbitant power, the exploited who, now, include not only waged labourers but also the hordes of cloud serfs who are reproducing the very cloud capital that keeps them in a state of deepening precarity. The concept of technofeudalism drives home the point that organising auto-workers and nurses, while still essential, is insufficient. It elucidates what it will take to organise the movements against the fossil fuel cartel when our means of communication are run on cloud capital primed to poison public opinion. It explains how the shift to electric cars caused German deindustrialisation, as profits due to precision mechanical engineering are being replaced by rents extracted by owners of the cloud capital keeping tabs on the drivers’ routes and in-cabin habits. Elon Musk’s decision to buy Twitter suddenly makes a lot more sense. Twitter for Musk is an interface between his mechanical capital stock at Tesla and SpaceX and cloud capital. The New Cold War between the USA and China, especially after the war in Ukraine, is explained as the repercussion of an underlying clash between two technofeudalisms, one whose cloud rents are denominated in dollars the other in yuan. Isn’t it mindboggling? It took mind-bending scientific breakthroughs, fantastical neural networks, and imagination-defying AI programs to accomplish what? To create a world where, while privatisation and private equity asset-strip all physical wealth around us, cloud capital goes about the business of asset-stripping our brains. To own our minds individually, we must own cloud capital collectively. Once we have reclaimed our minds, we can put them collectively to work out a way to create a new cloud capital commons. It will be damned hard. But it’s the only way we can turn our cloud-based artefacts from a produced means of behaviour modification to a produced means of human collaboration and emancipation. Cloud serfs, cloud proles and cloud vassals of the world, unite! We have nothing to lose but our mind-cloud chains! US Edition: UK Edition: Greek Edition:

Yanis Varoufakis

1,817,382 次观看 • 2 年前

Say hello to a massive acceleration of AI smart contracts 🧠 on the Internet Computer #ICP blockchain. Brilliant work by the DFINITY Foundation team has accelerated deterministic floating point instructions by 10X. We continue on our mission to run sophisticated LLMs as smart contracts 👊⚡️🔥 In the demo, image classification is now running 3X+ faster (across 3-4 blocks through consensus compared to 10-12 before). Naturally, the gas/cycles costs of AI inference has come down commensurately. The acceleration was achieved through broad low-level work, which includes commits to the public Wasmtime virtual machine implementation of WebAssembly. Coming optimizations will take us much further. The next optimization will include the integration of SIMD instructions into the smart contract execution environment, which will allow multiple floating point calculations to be performed in parallel through the execution of a single instruction. Something crucial: also in near sight, is the migration of the ICP smart contract environment from 32-bit Wasm to 64-bit Wasm, which will scale the number of AI model weights that can be loaded into contract memory from 2B to whatever is needed for sophisticated LLMs. Somewhat further away, is the provision of new APIs allowing canister smart contract code to export AI computations for accelerated processing on GPUs. This will require substantial work to address the challenges of achieving deterministic computation directly on the silicon, and for the community to agree a new public spec for node machines that pack GPUs, and to add compliant blockchain nodes to the network. Together, these improvements will unlock further orders of magnitude acceleration of AI smart contract inference (and potentially training). We believe that in the future vast numbers of AI models will run as smart contracts, which will prevent them being hacked (and thus the sensitive data they ingest being stolen), make them unstoppable, make them transparent, and where needed, allow them to run autonomously – for example for purposes such as KYC or EVM smart contract verification/certification. In the future, you will be able to have a conversation with a blockchain, and ask it to do reasoning for you. In this bright future, on-chain AI will be at the heart of Web3 – and to be clear, the Internet Computer's chain key technology will allow direct trustless integration with smart contracts on traditional chains such as Ethereum, Solana and NEAR. Join the #ICP mission to run the most impactful AI models on blockchain as smart contracts, and help make the Internet Computer the "everything computer." Join next week's Global R&D for details about the low-level deterministic floating point optimizations. Thanks for following 💪
2:03

Sensitive content

Say hello to a massive acceleration of AI smart contracts 🧠 on the Internet Computer #ICP blockchain. Brilliant work by the DFINITY Foundation team has accelerated deterministic floating point instructions by 10X. We continue on our mission to run sophisticated LLMs as smart contracts 👊⚡️🔥 In the demo, image classification is now running 3X+ faster (across 3-4 blocks through consensus compared to 10-12 before). Naturally, the gas/cycles costs of AI inference has come down commensurately. The acceleration was achieved through broad low-level work, which includes commits to the public Wasmtime virtual machine implementation of WebAssembly. Coming optimizations will take us much further. The next optimization will include the integration of SIMD instructions into the smart contract execution environment, which will allow multiple floating point calculations to be performed in parallel through the execution of a single instruction. Something crucial: also in near sight, is the migration of the ICP smart contract environment from 32-bit Wasm to 64-bit Wasm, which will scale the number of AI model weights that can be loaded into contract memory from 2B to whatever is needed for sophisticated LLMs. Somewhat further away, is the provision of new APIs allowing canister smart contract code to export AI computations for accelerated processing on GPUs. This will require substantial work to address the challenges of achieving deterministic computation directly on the silicon, and for the community to agree a new public spec for node machines that pack GPUs, and to add compliant blockchain nodes to the network. Together, these improvements will unlock further orders of magnitude acceleration of AI smart contract inference (and potentially training). We believe that in the future vast numbers of AI models will run as smart contracts, which will prevent them being hacked (and thus the sensitive data they ingest being stolen), make them unstoppable, make them transparent, and where needed, allow them to run autonomously – for example for purposes such as KYC or EVM smart contract verification/certification. In the future, you will be able to have a conversation with a blockchain, and ask it to do reasoning for you. In this bright future, on-chain AI will be at the heart of Web3 – and to be clear, the Internet Computer's chain key technology will allow direct trustless integration with smart contracts on traditional chains such as Ethereum, Solana and NEAR. Join the #ICP mission to run the most impactful AI models on blockchain as smart contracts, and help make the Internet Computer the "everything computer." Join next week's Global R&D for details about the low-level deterministic floating point optimizations. Thanks for following 💪

dom | icp

278,395 次观看 • 2 年前

What is Apple doing in the AI race? Ever since ChatGPT came out in 2022, every tech company realized that generative AI is the next big thing. So, all these companies dropped everything else and started focusing on it first. Google launches Bard and does a bunch of stuff. Microsoft teams up with OpenAI and rolls out a pilot. Adobe launches Firefly. Elon Musk starts his new company, XI. Meta launches the Llama model. Tons of other AI startups pop up, and investors are throwing money at AI like crazy Apple's AI strategy is fascinating because it's playing a completely different game than Google, Microsoft, and OpenAI. While everyone else rushed to build the most powerful language models, Apple took a fundamentally different approach that aligns with their core business model and strengths Apple Intelligence is comprised of multiple highly capable generative models that are specialized for users' everyday tasks, but unlike competitors, Apple isn't trying to win the raw AI power race. Instead, they're leveraging what they've always done best, creating seamless, integrated experiences The key insight you mentioned about revenue models is crucial. While Microsoft makes 48% from cloud services and Google relies heavily on cloud and subscriptions, Apple's business is 80% hardware driven. This means they don't need to compete on cloud AI services they can focus on making AI work better on the devices people already own Apple's four step strategy you outlined is spot on, The "Invisible Model" approach is brilliant because most users don't want to think about which AI model to use. Tim Cook doubled down on Apple's AI strategy, insisting that generative AI was never off the table and was always about pursuing it in a thoughtful kind of way, they're making AI feel natural rather than technical Ecosystem Integration remains Apple's superpower. At WWDC 2025, Apple announced what it calls the Foundation Models framework, which will let developers tap into its AI models while offline, this is huge because it means third party apps can now leverage Apple's AI without internet dependency, something Google and Microsoft can't easily replicate across their fragmented hardware ecosystem The Distribution Advantage is where Apple really shines. They have direct control over 2 billion devices with powerful Apple Silicon chips that can run AI models locally. Apple is still pushing App Intents, the same system that makes it simpler for Apple Intelligence and Siri to use apps and get things done, which will enable those complex multi app workflows you described Building Trust through privacy focused messaging is classic Apple. They're positioning themselves as the "safe" AI option while competitors deal with data privacy concerns The real genius is that Apple doesn't need to build the world's best AI model, they just need to build the best AI experience. By partnering with OpenAI for complex tasks while handling simple ones locally, they're creating a hybrid approach that prioritizes user experience over technical bragging rights The upcoming Apple Intelligence features slated for 2025 demonstrate Apple's commitment to integrating advanced AI technologies into its devices, enhancing user experience, and promoting productivity, suggesting they're still in the early phases of a longer term strategy This approach could indeed "wipe out" Android and Windows in the AI era not by building better models, but by making AI feel like a natural extension of the devices people already love. It's classic Apple, arrive late, but redefine the entire category
1:04

What is Apple doing in the AI race? Ever since ChatGPT came out in 2022, every tech company realized that generative AI is the next big thing. So, all these companies dropped everything else and started focusing on it first. Google launches Bard and does a bunch of stuff. Microsoft teams up with OpenAI and rolls out a pilot. Adobe launches Firefly. Elon Musk starts his new company, XI. Meta launches the Llama model. Tons of other AI startups pop up, and investors are throwing money at AI like crazy Apple's AI strategy is fascinating because it's playing a completely different game than Google, Microsoft, and OpenAI. While everyone else rushed to build the most powerful language models, Apple took a fundamentally different approach that aligns with their core business model and strengths Apple Intelligence is comprised of multiple highly capable generative models that are specialized for users' everyday tasks, but unlike competitors, Apple isn't trying to win the raw AI power race. Instead, they're leveraging what they've always done best, creating seamless, integrated experiences The key insight you mentioned about revenue models is crucial. While Microsoft makes 48% from cloud services and Google relies heavily on cloud and subscriptions, Apple's business is 80% hardware driven. This means they don't need to compete on cloud AI services they can focus on making AI work better on the devices people already own Apple's four step strategy you outlined is spot on, The "Invisible Model" approach is brilliant because most users don't want to think about which AI model to use. Tim Cook doubled down on Apple's AI strategy, insisting that generative AI was never off the table and was always about pursuing it in a thoughtful kind of way, they're making AI feel natural rather than technical Ecosystem Integration remains Apple's superpower. At WWDC 2025, Apple announced what it calls the Foundation Models framework, which will let developers tap into its AI models while offline, this is huge because it means third party apps can now leverage Apple's AI without internet dependency, something Google and Microsoft can't easily replicate across their fragmented hardware ecosystem The Distribution Advantage is where Apple really shines. They have direct control over 2 billion devices with powerful Apple Silicon chips that can run AI models locally. Apple is still pushing App Intents, the same system that makes it simpler for Apple Intelligence and Siri to use apps and get things done, which will enable those complex multi app workflows you described Building Trust through privacy focused messaging is classic Apple. They're positioning themselves as the "safe" AI option while competitors deal with data privacy concerns The real genius is that Apple doesn't need to build the world's best AI model, they just need to build the best AI experience. By partnering with OpenAI for complex tasks while handling simple ones locally, they're creating a hybrid approach that prioritizes user experience over technical bragging rights The upcoming Apple Intelligence features slated for 2025 demonstrate Apple's commitment to integrating advanced AI technologies into its devices, enhancing user experience, and promoting productivity, suggesting they're still in the early phases of a longer term strategy This approach could indeed "wipe out" Android and Windows in the AI era not by building better models, but by making AI feel like a natural extension of the devices people already love. It's classic Apple, arrive late, but redefine the entire category

D4rsh🦅

13,266 次观看 • 1 年前

In 2016, Marvell's largest design win was a Wi-Fi chip in the Barbie Dream House (Save this). That is a documented fact about one of the most remarkable corporate transformations in semiconductor history. Ten years and $36 billion in acquisitions later, Marvell is now the company that Jensen Huang invites onto the COMPUTEX stage, the same stage where he announced a $2 billion strategic investment into the company. Over 75% of Marvell's revenue today comes from data centers. To understand what Marvell actually is now, you need to understand what Matt Murphy did when he walked in as CEO in 2016. The company had stagnant growth, governance scandals, and a business model built around chips for hard drives, printers, and consumer electronics, exactly the wrong place to be as the cloud era was beginning. Murphy made a ruthless decision to kill every low margin consumer business and go all in on data infrastructure. Then he went shopping. 2018 - Acquired Cavium for $6 billion, bringing ARM-based network processors and the foundation for cloud infrastructure compute. 2019 - Acquired Avera Semiconductor, formerly IBM's custom silicon team, which gave Marvell the ability to design bespoke ASICs for hyperscalers. This is what opened the door to Amazon, Microsoft, and Google design wins. 2021 - Acquired Inphi for $8.2 billion, securing leadership in high-speed optical interconnect, the technology that moves data between and within data centers at the speed of light. 2021 - Acquired Innovium, adding cloud-optimized Ethernet switching to the portfolio. 2025/2026 - Acquired Celestial AI for $3.25 billion, bringing photonic fabric technology that places optical connections directly inside the chip package itself. Each acquisition followed the same formula, buy the technology that will be absolutely essential in the next generation of computing before anyone else is paying attention. Now here's the vision Murphy laid out at COMPUTEX 2026, and why it's the most important thing he's ever said publicly. He made one central argument, AI scaling is no longer limited by compute or memory but rather limited by connectivity. Training a frontier model requires tens of thousands and eventually millions of processors working as a single engine and making that happen is a connectivity problem above all else. Today, data centers are constrained by copper. Copper traces connecting chips inside a server can only move data so far, so fast, before bandwidth collapses and latency rises, that's why today's AI servers have to bundle everything, CPUs, GPUs, memory onto the same physical board sitting centimeters apart. When you replace copper with optics, distance disappears entirely. An optically connected server rack can communicate with another rack in a different building at the same bandwidth and latency as if they were the same machine. Memory can sit in one physical location, compute in another, networking in a third and a software orchestration layer composes the exact ratio the workload needs, on the fly, in real time. Murphy called this a data center without distance, a globally optically interconnected infrastructure where the rigid physical boundaries of today's servers begin to disappear entirely, and data centers function as one unified system. That is not a 10 year vision because Marvell's CPO (co-packaged optics) products are sampling in 2027 with volume shipments beginning 2028. Nvidia's Vera Rubin platform has already adopted Spectrum-X Ethernet Photonics, the first CPO switch in commercial production. The reason this makes Marvell's TAM almost impossible to cap is the following. Right now, Marvell's addressable market is the optical interconnect market, a segment projected to be worth $200 billion per year by end of decade. But if the data center without distance architecture actually materializes and the evidence suggests it will, then Marvell's TAM is not just the optical interconnect market but rather every connection in every data center on earth. Bullish on Marvel! Come join Milk Road Pro for just a $1, If you want the full Marvell breakdown on where it sits in our AI infrastructure portfolio, and our entire AI thesis. Link below!
3:05

In 2016, Marvell's largest design win was a Wi-Fi chip in the Barbie Dream House (Save this). That is a documented fact about one of the most remarkable corporate transformations in semiconductor history. Ten years and $36 billion in acquisitions later, Marvell is now the company that Jensen Huang invites onto the COMPUTEX stage, the same stage where he announced a $2 billion strategic investment into the company. Over 75% of Marvell's revenue today comes from data centers. To understand what Marvell actually is now, you need to understand what Matt Murphy did when he walked in as CEO in 2016. The company had stagnant growth, governance scandals, and a business model built around chips for hard drives, printers, and consumer electronics, exactly the wrong place to be as the cloud era was beginning. Murphy made a ruthless decision to kill every low margin consumer business and go all in on data infrastructure. Then he went shopping. 2018 - Acquired Cavium for $6 billion, bringing ARM-based network processors and the foundation for cloud infrastructure compute. 2019 - Acquired Avera Semiconductor, formerly IBM's custom silicon team, which gave Marvell the ability to design bespoke ASICs for hyperscalers. This is what opened the door to Amazon, Microsoft, and Google design wins. 2021 - Acquired Inphi for $8.2 billion, securing leadership in high-speed optical interconnect, the technology that moves data between and within data centers at the speed of light. 2021 - Acquired Innovium, adding cloud-optimized Ethernet switching to the portfolio. 2025/2026 - Acquired Celestial AI for $3.25 billion, bringing photonic fabric technology that places optical connections directly inside the chip package itself. Each acquisition followed the same formula, buy the technology that will be absolutely essential in the next generation of computing before anyone else is paying attention. Now here's the vision Murphy laid out at COMPUTEX 2026, and why it's the most important thing he's ever said publicly. He made one central argument, AI scaling is no longer limited by compute or memory but rather limited by connectivity. Training a frontier model requires tens of thousands and eventually millions of processors working as a single engine and making that happen is a connectivity problem above all else. Today, data centers are constrained by copper. Copper traces connecting chips inside a server can only move data so far, so fast, before bandwidth collapses and latency rises, that's why today's AI servers have to bundle everything, CPUs, GPUs, memory onto the same physical board sitting centimeters apart. When you replace copper with optics, distance disappears entirely. An optically connected server rack can communicate with another rack in a different building at the same bandwidth and latency as if they were the same machine. Memory can sit in one physical location, compute in another, networking in a third and a software orchestration layer composes the exact ratio the workload needs, on the fly, in real time. Murphy called this a data center without distance, a globally optically interconnected infrastructure where the rigid physical boundaries of today's servers begin to disappear entirely, and data centers function as one unified system. That is not a 10 year vision because Marvell's CPO (co-packaged optics) products are sampling in 2027 with volume shipments beginning 2028. Nvidia's Vera Rubin platform has already adopted Spectrum-X Ethernet Photonics, the first CPO switch in commercial production. The reason this makes Marvell's TAM almost impossible to cap is the following. Right now, Marvell's addressable market is the optical interconnect market, a segment projected to be worth $200 billion per year by end of decade. But if the data center without distance architecture actually materializes and the evidence suggests it will, then Marvell's TAM is not just the optical interconnect market but rather every connection in every data center on earth. Bullish on Marvel! Come join Milk Road Pro for just a $1, If you want the full Marvell breakdown on where it sits in our AI infrastructure portfolio, and our entire AI thesis. Link below!

Milk Road AI

21,550 次观看 • 2 个月前

Hyperspace: The Agentic OS Apple Should Have Built On December 19th, 2024, we announced the world’s first Agentic Browser. What followed was a movement — a new category was born which led to many early products in this space and recently the hundreds of people lining up outside the The Agentic Browser Summit in San Francisco underscored that. Silicon Valley instinctively gets it, from students to tech executives, people can feel a revolutionary new change in computing is in the air. Past year taught us why such a product was inevitable, a hard engineering effort, and also the last mover in the entire software world this decade if and when done right. All paths are headed in the same direction: one tool which orchestrates them all. At Hyperspace we showed that path with essays and products we launched in earlier months: from a spatial UI of orchestrating agents, to showcasing transparent activity in how the AI system operates which leads to user trust, to presenting the software end-game, which massively improves human productivity. We also built the world’s largest AI network, drawing participation from people in almost 6000 cities around the world contributing their machines as nodes in the network. Think Uber, but for AI. That is, planetary-scale. And now we are stretching this industry ambition further with our end-to-end vision of the Agentic Supercomputer, the first breakthrough new AI OS, and an effort which spans from AI research to distributed systems to inventing a new UI to inventing a new business model to complement it. All of this together helps us in serving our mission, of delivering “Everyone’s Personal Supercomputer”. While others have built AI-native browsers, no one though has built something agentic from the ground up — with AI as the foundation, not a feature. How do you fundamentally improve the lives’ of billions around the world ? We believe that requires building a native environment for agents to be viewed, created, deployed, executed, discovered and priced in. That is a world where we move on from static apps, to dynamic agents. But, as my 2 year old niece likes to ask: “but why ?” The issue is that the world of software today is fragmented, and everyone is sprinkling on AI as a feature and charging a subscription fees for it. From browser makers, to IDEs, to design and other productivity tools. This leads to a fragmented UX, where people have to learn to use AI in each app, their memory and other context is not shared between all these apps, and they also have to pay separately for compute for each such AI-enhanced app. Each app maker has to figure out basics such as compute, and leads to the issues we saw with Cursor pricing recently. This is not the future. What if AI was the foundation instead of a feature ? What if Apple had built a fundamentally new AI OS from the ground up and what would it have looked like ? At Hyperspace, that is what we did. On July 15th we introduced three breakthrough key pillars of our AI OS: 1. Agentic Browser 2. Agentic Memory 3. Agentic Payments And we didn’t stop there. We also introduced a breakthrough new user interface called the Spatial AI which is inspired both from the spreadsheet and the HyperCard - each card is an agent, with it’s own inputs and outputs, endlessly extensible and pluggable with others, just like cells of a spreadsheet. Update one cell and all the dependents update, like a spreadsheet formula. It goes beyond a static linear workflow to being able to operate in all directions. This revolutionary new interface helps manage all of the below: 1. Multiple websites being browsed in parallel 2. Multiple desktop apps being browsed in parallel 3. Multiple server tools being used in parallel 4. Multiple smartphone apps streamed to your device or opened via an emulator All the software which you need comes together in this one seamless, agent-native interface. This interface provides you access to the largest network of models, vectors, agents and compute on the planet. The Browser. The IDE. The Notepad… they are not separate products: they are all in one, the Agentic Browser. As Steve Jobs famously said at the iPhone announcement, “are you getting it ?” And beneath this UI lies a new intelligence routing layer — leveraging both swarms of specialized models to the Hyperspace Matrix model that recalls thousands of tools in real-time, not by context window hacks, but through retrieval, ranking, and reuse. To many, this will feel like AGI. Not one big system by one big company, but an intelligent network. Now lets talk about privacy… Are you comfortable with one company owning all your memory forever ? I am not. So we have invented Agentic Memory as a new open protocol which provides full power over memory to you, the user. Your memory is yours, encrypted, on your device, and portable if and how you want. Anyone can build on it without our permission, but not without your permission. This protocol, and the decentralized vector database spread out across the world, would enable apps and agents to share context and memory. Think copy-paste, but for the AI world. It doesn’t just remember — it knows what matters. VectorRank helps your AI weigh your life’s most relevant moments over time, just like the way our minds elevate memories. Now each time you use an agent, your experience with other agents will also continuously improve: you don’t have to keep repeating the same things about yourself, while fully preserving your privacy. Agentic Memory is accessible within the Agentic Browser to manage. And there is one more thing… AI as the foundation requires compute to be available at the base layer, but this base layer spans models running on your own device, to cloud APIs, to also running across the peer-to-peer distributed network. Agentic Payments provides a singular interface to all of that compute, running a spot auction clearing marketplace every second to determine the fair price of compute. This results in price transparency, and you as the user paying the lowest possible cost. If you want predictability, you can reserve compute in advance. This end-to-end system provides the most streamlined world for agents to operate in. In order to enable this world and the world of agents being able to pay each other in sub-cent increments millions of times a second, we had to also invent a fundamentally new agentic micropayments blockchain. All of this together would enable a world where you as a user, or the agent itself, can efficiently call and utilize other agents built by others and also pay for content which is unique and useful. This enables a move away from the current AI exploitative economy for bloggers and other content creators, to a web with a fundamental new business model. Earlier we didn’t have the right infrastructure to enable such a world. Now, all the dots connect. The Hyperspace AI OS would give the power of a supercomputer in everyone’s hands. This isn’t a browser, or an IDE or limited to any device or cloud. It’s an entire AI operating system — with a breakthrough new spatial UI, local and distributed compute, agentic memory, agentic payments, and orchestration built into the foundation. As a user, we move the choice back in your hands with an experience you will love and find delightful. You get to choose the level of privacy, cost, and utility you want. And while Apple should have done it, we could not wait, and we feel this just required a new level of passion and DNA which we bring here. We are just getting started. Thank you, Varun Mathur Cofounder and CEO, Hyperspace cc Naval Marc Andreessen 🇺🇸 Vinod Khosla Andrej Karpathy Sam Altman
2:52

Hyperspace: The Agentic OS Apple Should Have Built On December 19th, 2024, we announced the world’s first Agentic Browser. What followed was a movement — a new category was born which led to many early products in this space and recently the hundreds of people lining up outside the The Agentic Browser Summit in San Francisco underscored that. Silicon Valley instinctively gets it, from students to tech executives, people can feel a revolutionary new change in computing is in the air. Past year taught us why such a product was inevitable, a hard engineering effort, and also the last mover in the entire software world this decade if and when done right. All paths are headed in the same direction: one tool which orchestrates them all. At Hyperspace we showed that path with essays and products we launched in earlier months: from a spatial UI of orchestrating agents, to showcasing transparent activity in how the AI system operates which leads to user trust, to presenting the software end-game, which massively improves human productivity. We also built the world’s largest AI network, drawing participation from people in almost 6000 cities around the world contributing their machines as nodes in the network. Think Uber, but for AI. That is, planetary-scale. And now we are stretching this industry ambition further with our end-to-end vision of the Agentic Supercomputer, the first breakthrough new AI OS, and an effort which spans from AI research to distributed systems to inventing a new UI to inventing a new business model to complement it. All of this together helps us in serving our mission, of delivering “Everyone’s Personal Supercomputer”. While others have built AI-native browsers, no one though has built something agentic from the ground up — with AI as the foundation, not a feature. How do you fundamentally improve the lives’ of billions around the world ? We believe that requires building a native environment for agents to be viewed, created, deployed, executed, discovered and priced in. That is a world where we move on from static apps, to dynamic agents. But, as my 2 year old niece likes to ask: “but why ?” The issue is that the world of software today is fragmented, and everyone is sprinkling on AI as a feature and charging a subscription fees for it. From browser makers, to IDEs, to design and other productivity tools. This leads to a fragmented UX, where people have to learn to use AI in each app, their memory and other context is not shared between all these apps, and they also have to pay separately for compute for each such AI-enhanced app. Each app maker has to figure out basics such as compute, and leads to the issues we saw with Cursor pricing recently. This is not the future. What if AI was the foundation instead of a feature ? What if Apple had built a fundamentally new AI OS from the ground up and what would it have looked like ? At Hyperspace, that is what we did. On July 15th we introduced three breakthrough key pillars of our AI OS: 1. Agentic Browser 2. Agentic Memory 3. Agentic Payments And we didn’t stop there. We also introduced a breakthrough new user interface called the Spatial AI which is inspired both from the spreadsheet and the HyperCard - each card is an agent, with it’s own inputs and outputs, endlessly extensible and pluggable with others, just like cells of a spreadsheet. Update one cell and all the dependents update, like a spreadsheet formula. It goes beyond a static linear workflow to being able to operate in all directions. This revolutionary new interface helps manage all of the below: 1. Multiple websites being browsed in parallel 2. Multiple desktop apps being browsed in parallel 3. Multiple server tools being used in parallel 4. Multiple smartphone apps streamed to your device or opened via an emulator All the software which you need comes together in this one seamless, agent-native interface. This interface provides you access to the largest network of models, vectors, agents and compute on the planet. The Browser. The IDE. The Notepad… they are not separate products: they are all in one, the Agentic Browser. As Steve Jobs famously said at the iPhone announcement, “are you getting it ?” And beneath this UI lies a new intelligence routing layer — leveraging both swarms of specialized models to the Hyperspace Matrix model that recalls thousands of tools in real-time, not by context window hacks, but through retrieval, ranking, and reuse. To many, this will feel like AGI. Not one big system by one big company, but an intelligent network. Now lets talk about privacy… Are you comfortable with one company owning all your memory forever ? I am not. So we have invented Agentic Memory as a new open protocol which provides full power over memory to you, the user. Your memory is yours, encrypted, on your device, and portable if and how you want. Anyone can build on it without our permission, but not without your permission. This protocol, and the decentralized vector database spread out across the world, would enable apps and agents to share context and memory. Think copy-paste, but for the AI world. It doesn’t just remember — it knows what matters. VectorRank helps your AI weigh your life’s most relevant moments over time, just like the way our minds elevate memories. Now each time you use an agent, your experience with other agents will also continuously improve: you don’t have to keep repeating the same things about yourself, while fully preserving your privacy. Agentic Memory is accessible within the Agentic Browser to manage. And there is one more thing… AI as the foundation requires compute to be available at the base layer, but this base layer spans models running on your own device, to cloud APIs, to also running across the peer-to-peer distributed network. Agentic Payments provides a singular interface to all of that compute, running a spot auction clearing marketplace every second to determine the fair price of compute. This results in price transparency, and you as the user paying the lowest possible cost. If you want predictability, you can reserve compute in advance. This end-to-end system provides the most streamlined world for agents to operate in. In order to enable this world and the world of agents being able to pay each other in sub-cent increments millions of times a second, we had to also invent a fundamentally new agentic micropayments blockchain. All of this together would enable a world where you as a user, or the agent itself, can efficiently call and utilize other agents built by others and also pay for content which is unique and useful. This enables a move away from the current AI exploitative economy for bloggers and other content creators, to a web with a fundamental new business model. Earlier we didn’t have the right infrastructure to enable such a world. Now, all the dots connect. The Hyperspace AI OS would give the power of a supercomputer in everyone’s hands. This isn’t a browser, or an IDE or limited to any device or cloud. It’s an entire AI operating system — with a breakthrough new spatial UI, local and distributed compute, agentic memory, agentic payments, and orchestration built into the foundation. As a user, we move the choice back in your hands with an experience you will love and find delightful. You get to choose the level of privacy, cost, and utility you want. And while Apple should have done it, we could not wait, and we feel this just required a new level of passion and DNA which we bring here. We are just getting started. Thank you, Varun Mathur Cofounder and CEO, Hyperspace cc Naval Marc Andreessen 🇺🇸 Vinod Khosla Andrej Karpathy Sam Altman

Varun

169,177 次观看 • 1 年前

$AMD $MSFT Partnership is MASSIVE in 2026 🚀 If you were excited about my thread on $AMD $AMZN AWS long time partnership, you will be even more excited about what Microsoft gonna do with 2026 AMD EPYC "Venice". Historical Context: The relationship between AMD and Microsoft began in the early 2000s, with Microsoft initially focusing on Intel's x86 architecture for its Windows operating system and server products. However, AMD's entry into the server market with its Opteron processors in 2003 marked the beginning of a competitive dynamic that eventually led to collaboration. The partnership intensified with the launch of 3rd Generation EPYC "Milan" in 2021, powering Azure's N2D and C2D VM families. By 2025, Microsoft had integrated 5th Generation EPYC "Turin" into new compute-optimized instances, reflecting a strategic shift towards AMD for cost and performance benefits. This "Secret Weapon" breakthrough will mark another inflection point for AMD Microsoft Azure relationship, will probably be more aggressive than EPYC "Milan" moment in 2021. We can call it EPYC "Venice" moment 2026" 1. Technical performance of AMD EPYC "Venice" (2026) AMD's 6th Gen EPYC "Venice" processors, slated for 2026, introduce New Chiplet design breakthrough. a revolutionary chiplet interconnect fabric that redefines server scalability for AI. This isn't just faster silicon; it's a paradigm shift for Microsoft Azure , enabling hyper-efficient, rack-scale AI inference that slashes costs and latency while boosting throughput. ~Up to 256 Zen 6 cores, a 70% performance increase over "Turin," optimized for AI and HPC. ~Memory and Bandwidth: 1.6 TB/s per socket, doubling "Turin's" capability, with support for MR-DIMM/MCR-DIMM. ~Efficiency: 1,500-1,700W power draw, a 50% reduction, aligning with Microsoft's sustainability initiatives. ~Interconnect: PCIe 6.0 and a new chiplet fabric for rack-scale AI, reducing latency and enhancing scalability. 2. Why $MSFT will adopt $AMD YPYC Share to 50%+ in 2026. AMD EPYC Share: ~30-35% of Azure's x86 CPU-based business while Intel Xeon share is 65% Microsoft's Azure has been progressively integrating AMD EPYC, with "Venice" expected to expand this footprint: A. Dominance of AI Inference Workloads ~AI inference constitutes 80% of AI workloads in cloud environments, with latency-sensitive applications like chatbots, recommendation engines, and fraud detection requiring sub-second response times. ~"Venice's" 35x inference performance uplift directly addresses these requirements, outperforming Intel's offerings and custom Arm solutions in multi-threaded scenarios. B. Cost Efficiency and Operational Savings ~Azure's 2025 capex of $118B is under pressure to deliver returns. "Venice" can reduce operational expenses by $20-30B annually due to its power efficiency and performance gains, improving Azure's margins to 35-40%. ~The cost per inference operation is significantly lower with "Venice," estimated at 24-31% less than Intel-based alternatives, enhancing Azure's competitiveness against AWS and GCP. C. Scalability for Enterprise AI: ~"Venice" supports rack-scale AI deployments, enabling Azure to scale AI services for enterprise customers. For example, a 1,000-node cluster can process 700,000+ tokens per second, crucial for large-scale AI applications like personalized marketing and predictive analytics. ~This scalability is particularly important as Azure aims to capture the $100B+ AI opportunity by 2026, as stated by Microsoft CEO Satya Nadella. D. Reduction of Nvidia Dependency ~While Nvidia ( $NVDA) dominates AI accelerators, AMD's integrated EPYC-GPU solutions (MI450 with "Venice") offer a balanced approach, reducing Azure's reliance on Nvidia's high-cost GPUs. ~"Venice" enables hybrid inference models, where CPU-based inference handles 80% of workloads, and GPU acceleration is reserved for training and complex tasks, optimizing resource allocation. 3. Financial Implication: ~Revenue from Azure could reach $15-18B annually by 2026, part of a total revenue projection of $70-100B ~Profit margins could improve to 55-60%, boosting net income to $20-25B, supported by scale economies and reduced production costs. Intel could respond by giving more aggressive discounts, but this breakthrough has been a decade long of $AMD R&D, or rethinking chiplet design, a complete new approach. "Venice's" lead in AI inference and efficiency is challenging to match. Broader Industry: Other hyperscalers ( Amazon Web Services , GCP) and enterprises will follow Azure's lead, standardizing EPYC technology and pressuring Intel further. This could lead to a broader industry shift towards AMD, enhancing its ecosystem and bargaining power. Conclusion: The strategic adoption of AMD's 6th Generation EPYC "Venice" processors by Microsoft Azure in 2026 marks a pivotal moment in the evolution of cloud computing, particularly for AI inference capabilities. "Venice's" groundbreaking chiplet design, offering a 35x performance uplift for AI inference tasks, a 50% reduction in power consumption, and unparalleled scalability, positions Azure to leapfrog its competitors in the race for AI dominance. This technical superiority, combined with significant cost savings potentially $20-30B annually in operational expenses; aligns perfectly with Microsoft's ambitions to capture the $100B+ Revenue AI opportunity by 2026. The shift to 50% x86 market share for AMD within Azure is not merely a technical transition but a strategic realignment that redefines the competitive landscape. Historically, Microsoft's partnership with AMD has evolved from niche deployments to a core component of Azure's infrastructure, and "Venice" accelerates this trend. The 30-35% AMD EPYC share in 2025 is expected to double, driven by new VM families like C4D and H4D, which will dominate AI-intensive and HPC workloads. This migration is incentivized by "Venice's" efficiency gains, reducing dependency on Intel and Nvidia, and enhancing Azure's sustainability profile. Not Financial Advice!
7:18

$AMD $MSFT Partnership is MASSIVE in 2026 🚀 If you were excited about my thread on $AMD $AMZN AWS long time partnership, you will be even more excited about what Microsoft gonna do with 2026 AMD EPYC "Venice". Historical Context: The relationship between AMD and Microsoft began in the early 2000s, with Microsoft initially focusing on Intel's x86 architecture for its Windows operating system and server products. However, AMD's entry into the server market with its Opteron processors in 2003 marked the beginning of a competitive dynamic that eventually led to collaboration. The partnership intensified with the launch of 3rd Generation EPYC "Milan" in 2021, powering Azure's N2D and C2D VM families. By 2025, Microsoft had integrated 5th Generation EPYC "Turin" into new compute-optimized instances, reflecting a strategic shift towards AMD for cost and performance benefits. This "Secret Weapon" breakthrough will mark another inflection point for AMD Microsoft Azure relationship, will probably be more aggressive than EPYC "Milan" moment in 2021. We can call it EPYC "Venice" moment 2026" 1. Technical performance of AMD EPYC "Venice" (2026) AMD's 6th Gen EPYC "Venice" processors, slated for 2026, introduce New Chiplet design breakthrough. a revolutionary chiplet interconnect fabric that redefines server scalability for AI. This isn't just faster silicon; it's a paradigm shift for Microsoft Azure , enabling hyper-efficient, rack-scale AI inference that slashes costs and latency while boosting throughput. ~Up to 256 Zen 6 cores, a 70% performance increase over "Turin," optimized for AI and HPC. ~Memory and Bandwidth: 1.6 TB/s per socket, doubling "Turin's" capability, with support for MR-DIMM/MCR-DIMM. ~Efficiency: 1,500-1,700W power draw, a 50% reduction, aligning with Microsoft's sustainability initiatives. ~Interconnect: PCIe 6.0 and a new chiplet fabric for rack-scale AI, reducing latency and enhancing scalability. 2. Why $MSFT will adopt $AMD YPYC Share to 50%+ in 2026. AMD EPYC Share: ~30-35% of Azure's x86 CPU-based business while Intel Xeon share is 65% Microsoft's Azure has been progressively integrating AMD EPYC, with "Venice" expected to expand this footprint: A. Dominance of AI Inference Workloads ~AI inference constitutes 80% of AI workloads in cloud environments, with latency-sensitive applications like chatbots, recommendation engines, and fraud detection requiring sub-second response times. ~"Venice's" 35x inference performance uplift directly addresses these requirements, outperforming Intel's offerings and custom Arm solutions in multi-threaded scenarios. B. Cost Efficiency and Operational Savings ~Azure's 2025 capex of $118B is under pressure to deliver returns. "Venice" can reduce operational expenses by $20-30B annually due to its power efficiency and performance gains, improving Azure's margins to 35-40%. ~The cost per inference operation is significantly lower with "Venice," estimated at 24-31% less than Intel-based alternatives, enhancing Azure's competitiveness against AWS and GCP. C. Scalability for Enterprise AI: ~"Venice" supports rack-scale AI deployments, enabling Azure to scale AI services for enterprise customers. For example, a 1,000-node cluster can process 700,000+ tokens per second, crucial for large-scale AI applications like personalized marketing and predictive analytics. ~This scalability is particularly important as Azure aims to capture the $100B+ AI opportunity by 2026, as stated by Microsoft CEO Satya Nadella. D. Reduction of Nvidia Dependency ~While Nvidia ( $NVDA) dominates AI accelerators, AMD's integrated EPYC-GPU solutions (MI450 with "Venice") offer a balanced approach, reducing Azure's reliance on Nvidia's high-cost GPUs. ~"Venice" enables hybrid inference models, where CPU-based inference handles 80% of workloads, and GPU acceleration is reserved for training and complex tasks, optimizing resource allocation. 3. Financial Implication: ~Revenue from Azure could reach $15-18B annually by 2026, part of a total revenue projection of $70-100B ~Profit margins could improve to 55-60%, boosting net income to $20-25B, supported by scale economies and reduced production costs. Intel could respond by giving more aggressive discounts, but this breakthrough has been a decade long of $AMD R&D, or rethinking chiplet design, a complete new approach. "Venice's" lead in AI inference and efficiency is challenging to match. Broader Industry: Other hyperscalers ( Amazon Web Services , GCP) and enterprises will follow Azure's lead, standardizing EPYC technology and pressuring Intel further. This could lead to a broader industry shift towards AMD, enhancing its ecosystem and bargaining power. Conclusion: The strategic adoption of AMD's 6th Generation EPYC "Venice" processors by Microsoft Azure in 2026 marks a pivotal moment in the evolution of cloud computing, particularly for AI inference capabilities. "Venice's" groundbreaking chiplet design, offering a 35x performance uplift for AI inference tasks, a 50% reduction in power consumption, and unparalleled scalability, positions Azure to leapfrog its competitors in the race for AI dominance. This technical superiority, combined with significant cost savings potentially $20-30B annually in operational expenses; aligns perfectly with Microsoft's ambitions to capture the $100B+ Revenue AI opportunity by 2026. The shift to 50% x86 market share for AMD within Azure is not merely a technical transition but a strategic realignment that redefines the competitive landscape. Historically, Microsoft's partnership with AMD has evolved from niche deployments to a core component of Azure's infrastructure, and "Venice" accelerates this trend. The 30-35% AMD EPYC share in 2025 is expected to double, driven by new VM families like C4D and H4D, which will dominate AI-intensive and HPC workloads. This migration is incentivized by "Venice's" efficiency gains, reducing dependency on Intel and Nvidia, and enhancing Azure's sustainability profile. Not Financial Advice!

Mike

141,018 次观看 • 11 个月前

🚨 THE GHOST IN THE MACHINE: EPSTEIN BUILT THE FOUNDATIONS OF MODERN AI — AND NOBODY WANTS TO TALK ABOUT IT The story they told you is simple: Jeffrey Epstein was a rogue financier, a degenerate, an aberration. A lone predator who somehow became friends with presidents, princes, Nobel laureates, and the architects of the modern world through pure coincidence. The story they won’t tell you is what he was actually building — and who was cashing his checks while the ink on his 2008 conviction was still wet. 🧠 The AI Summit on an Island Owned by a Predator In April 2002 — when “artificial intelligence” was still a punchline and the field was in a decades-long “winter” — a small gathering of the smartest minds in computing assembled at a ritzy hotel in St. Thomas, U.S. Virgin Islands. Not Silicon Valley. Not Cambridge. Not a university campus with institutional oversight. A predator’s island. The “St. Thomas Common Sense Symposium” brought together: - Marvin Minsky — co-founder of MIT’s AI Lab, arguably the founding father of artificial intelligence - Doug Lenat — creator of Cyc, the first serious attempt at machine common sense - Vernor Vinge — the science fiction writer who coined the term “singularity” - Roger Schank — celebrated AI theorist - Push Singh — Minsky’s protégé, the rising star of the field The resulting paper — on how to finally achieve human-level AI — contained one sentence that should have been screamed from every rooftop for two decades: “This meeting was made possible by the generous support of Jeffrey Epstein.” Attendees later recalled Epstein flying them in on a private plane, hosting a beach barbecue on his own island, and lavishing the gathering with resources. One source described Epstein speaking fondly of Minsky, saying he “loved talking to him about AI.” So while the world’s press was busy ignoring a convicted sex offender’s weird science hobby, a predator was seeding the intellectual foundation of the technology that now runs your phone, your bank, your news feed, and soon your government. 💰 Follow the Money: The Donor Database They Tried to Hide When the MIT fact-finding report finally dropped in 2020, it confirmed what insiders already knew: Epstein gave MIT $850,000 between 2002 and 2017. And here’s the part that should make your skin crawl: - $100,000 to Marvin Minsky in 2002 — years before his conviction - $525,000 to the MIT Media Lab — every dollar of it after Epstein’s 2008 guilty plea for soliciting a minor - $225,000 to Professor Seth Lloyd — also post-conviction MIT administrators didn’t reject the money. They didn’t blow the whistle. They created an “informal framework” to accept it quietly, on the condition that Epstein’s name never appear anywhere. Staff at the Media Lab reportedly referred to Epstein as “Voldemort” — “he who must not be named.” His donations were recorded as “anonymous.” His calendar entries used initials only. When Bill Gates donated $2 million to the Media Lab in 2014, internal emails show Ito writing: “This is a $2M gift from Bill Gates directed by Jeffrey Epstein.” The development office’s reply? “For gift recording purposes, we will not be mentioning Jeffrey’s name.” Read that again. A convicted sex offender was directing the flow of millions in tech philanthropy — and the most prestigious engineering institution on Earth was actively scrubbing his name from the paperwork. That’s not a scandal. That’s institutional complicity. 🔗 The Joi Ito → OpenAI Pipeline Here’s the connective tissue the mainstream press never bothered to stitch together: Joi Ito, the MIT Media Lab director who personally cultivated Epstein as a donor — who solicited “top-off” payments of $100K to fund researchers Epstein hand-picked — became one of the early investors in OpenAI. The lab Epstein was funding was the same ecosystem incubating the people who would go on to build the machine that now answers your questions, writes your emails, and increasingly shapes what you’re allowed to think. Epstein didn’t just fund Ito’s lab. He personally invested $1 million into a private investment fund Ito managed, plus another $250,000 into a company Ito was commercializing. When the heat came, Ito tried to “eject” Epstein’s money. But you can’t eject an idea once it’s been wired into the infrastructure. And who sat on OpenAI’s board until late 2023? Larry Summers — former Treasury Secretary, former Harvard president, and a man whose private correspondence with Epstein continued until the day before Epstein’s 2019 arrest. The released emails show Epstein referring to himself as Summers’ “wing man” — advising the married former Treasury Secretary on how to pressure a mentee into a relationship, urging him to play the “long game” and keep her in a “forced holding pattern.” That man was making governance decisions at the most powerful AI company on Earth. And the board seat only ended when the emails leaked. 🧬 The Real Agenda: “Superhumans” With His DNA Now for the part that reframes everything. Epstein wasn’t just interested in AI as a hobby. According to reporting from the New York Times and his own voluminous correspondence, he was obsessed with transhumanism — the project of upgrading humanity through genetic engineering and artificial intelligence. His specific dream, confided to scientists over years: Seed the human race with his own DNA. He planned to impregnate dozens of women at his New Mexico ranch, spreading his genetic material across the population. He handed out 23andMe kits “the way most people hand out business cards.” He pushed cognitive scientist Joscha Bach on whether gene editing could boost intelligence. He corresponded with Noam Chomsky about “genetic altruism” and race-based cognitive differences. And the AI connection? That’s where Peter Thiel enters — the PayPal co-founder who has spoken openly about uploading consciousness, who funded transhumanist institutions, and whose own correspondence with Epstein was regular and ongoing. The vision was never just money. It was a eugenicist’s fusion of genetics and machine intelligence — a project to build the “cognitive aristocracy” that would inherit the earth, with Epstein’s own DNA in the mix and AI as the enforcement mechanism. The Slate analysis, published after the full Epstein files were released, is blunt: “Jeffrey Epstein wasn’t just a sex pest. He was also a eugenicist.” And the institutions that now steer AI — the labs, the boards, the funding networks — were built inside the same social circle. 🌐 The Edge Foundation: The Network That Let Him In None of this happened in isolation. Epstein was the principal funder of the Edge Foundation — the invitation-only salon of Silicon Valley elite founded by literary agent John Brockman. For up to two decades, Edge was the conduit through which Epstein gained access to: - Google founders Sergey Brin and Larry Page - Elon Musk - Jeff Bezos - Bill Gates - Nathan Myhrvold - Nick Bostrom — the “father of longtermism” whose Future of Humanity Institute shaped Silicon Valley’s entire AI-safety philosophy Bostrom’s organization Humanity+ received $120,000 from Epstein — including money to cover the salary of Ben Goertzel, the computer scientist who popularized the term “AGI” (artificial general intelligence) and now runs SingularityNET. Let that land: The man who coined the term for the technology everyone is now terrified of was, in part, on Epstein’s payroll. The worldview threaded through this entire network — an obsession with racial hierarchy, genetic “optimization,” “climate culling” to reduce surplus population, machine intelligence as humanity’s inevitable successor — wasn’t an accident. It was the operating system. 🎭 So Ask Yourself the Obvious Question The official narrative treats all of this as a series of unfortunate coincidences: - A predator who happened to fund the founding fathers of AI - A university that happened to hide his donations - A board member who happened to be his close friend - A network of billionaires who happened to share his transhumanist vision That’s not a coincidence. That’s a pattern. Jeffrey Epstein didn’t stumble into the AI world. He bought his way in — with cash, with access, with the leverage his blackmail operation gave him over the most powerful people in tech and finance. And the technology now being deployed to monitor, profile, and control the global population was midwifed by a man who wanted to breed a master race with his own DNA. They told you Epstein was a footnote. A cautionary tale. A “bad apple.” He was the seed investor. 🔥 The Bottom Line You don’t get to fund the founding fathers of AI, bankroll the institution that incubated OpenAI’s early investors, sit your friends on its board, spread your eugenicist ideology through the network that shaped its safety philosophy — and then call it a coincidence. The machine has a ghost in it. And the ghost is still collecting royalties. h/t vladtv for the video #EpsteinFiles #EpsteinAI #GhostInTheMachine #FollowTheMoney #EpsteinNetwork #AIIndustrialComplex #Transhumanism #EugenicsExposed #MITMediaLab #OpenAI #LarrySummers #JoiIto #PeterThiel #EdgeFoundation #NickBostrom #AGI #TechBillionaires #TheMachineHasAGhost #SiliconValleySecrets #WakeUp
4:30

🚨 THE GHOST IN THE MACHINE: EPSTEIN BUILT THE FOUNDATIONS OF MODERN AI — AND NOBODY WANTS TO TALK ABOUT IT The story they told you is simple: Jeffrey Epstein was a rogue financier, a degenerate, an aberration. A lone predator who somehow became friends with presidents, princes, Nobel laureates, and the architects of the modern world through pure coincidence. The story they won’t tell you is what he was actually building — and who was cashing his checks while the ink on his 2008 conviction was still wet. 🧠 The AI Summit on an Island Owned by a Predator In April 2002 — when “artificial intelligence” was still a punchline and the field was in a decades-long “winter” — a small gathering of the smartest minds in computing assembled at a ritzy hotel in St. Thomas, U.S. Virgin Islands. Not Silicon Valley. Not Cambridge. Not a university campus with institutional oversight. A predator’s island. The “St. Thomas Common Sense Symposium” brought together: - Marvin Minsky — co-founder of MIT’s AI Lab, arguably the founding father of artificial intelligence - Doug Lenat — creator of Cyc, the first serious attempt at machine common sense - Vernor Vinge — the science fiction writer who coined the term “singularity” - Roger Schank — celebrated AI theorist - Push Singh — Minsky’s protégé, the rising star of the field The resulting paper — on how to finally achieve human-level AI — contained one sentence that should have been screamed from every rooftop for two decades: “This meeting was made possible by the generous support of Jeffrey Epstein.” Attendees later recalled Epstein flying them in on a private plane, hosting a beach barbecue on his own island, and lavishing the gathering with resources. One source described Epstein speaking fondly of Minsky, saying he “loved talking to him about AI.” So while the world’s press was busy ignoring a convicted sex offender’s weird science hobby, a predator was seeding the intellectual foundation of the technology that now runs your phone, your bank, your news feed, and soon your government. 💰 Follow the Money: The Donor Database They Tried to Hide When the MIT fact-finding report finally dropped in 2020, it confirmed what insiders already knew: Epstein gave MIT $850,000 between 2002 and 2017. And here’s the part that should make your skin crawl: - $100,000 to Marvin Minsky in 2002 — years before his conviction - $525,000 to the MIT Media Lab — every dollar of it after Epstein’s 2008 guilty plea for soliciting a minor - $225,000 to Professor Seth Lloyd — also post-conviction MIT administrators didn’t reject the money. They didn’t blow the whistle. They created an “informal framework” to accept it quietly, on the condition that Epstein’s name never appear anywhere. Staff at the Media Lab reportedly referred to Epstein as “Voldemort” — “he who must not be named.” His donations were recorded as “anonymous.” His calendar entries used initials only. When Bill Gates donated $2 million to the Media Lab in 2014, internal emails show Ito writing: “This is a $2M gift from Bill Gates directed by Jeffrey Epstein.” The development office’s reply? “For gift recording purposes, we will not be mentioning Jeffrey’s name.” Read that again. A convicted sex offender was directing the flow of millions in tech philanthropy — and the most prestigious engineering institution on Earth was actively scrubbing his name from the paperwork. That’s not a scandal. That’s institutional complicity. 🔗 The Joi Ito → OpenAI Pipeline Here’s the connective tissue the mainstream press never bothered to stitch together: Joi Ito, the MIT Media Lab director who personally cultivated Epstein as a donor — who solicited “top-off” payments of $100K to fund researchers Epstein hand-picked — became one of the early investors in OpenAI. The lab Epstein was funding was the same ecosystem incubating the people who would go on to build the machine that now answers your questions, writes your emails, and increasingly shapes what you’re allowed to think. Epstein didn’t just fund Ito’s lab. He personally invested $1 million into a private investment fund Ito managed, plus another $250,000 into a company Ito was commercializing. When the heat came, Ito tried to “eject” Epstein’s money. But you can’t eject an idea once it’s been wired into the infrastructure. And who sat on OpenAI’s board until late 2023? Larry Summers — former Treasury Secretary, former Harvard president, and a man whose private correspondence with Epstein continued until the day before Epstein’s 2019 arrest. The released emails show Epstein referring to himself as Summers’ “wing man” — advising the married former Treasury Secretary on how to pressure a mentee into a relationship, urging him to play the “long game” and keep her in a “forced holding pattern.” That man was making governance decisions at the most powerful AI company on Earth. And the board seat only ended when the emails leaked. 🧬 The Real Agenda: “Superhumans” With His DNA Now for the part that reframes everything. Epstein wasn’t just interested in AI as a hobby. According to reporting from the New York Times and his own voluminous correspondence, he was obsessed with transhumanism — the project of upgrading humanity through genetic engineering and artificial intelligence. His specific dream, confided to scientists over years: Seed the human race with his own DNA. He planned to impregnate dozens of women at his New Mexico ranch, spreading his genetic material across the population. He handed out 23andMe kits “the way most people hand out business cards.” He pushed cognitive scientist Joscha Bach on whether gene editing could boost intelligence. He corresponded with Noam Chomsky about “genetic altruism” and race-based cognitive differences. And the AI connection? That’s where Peter Thiel enters — the PayPal co-founder who has spoken openly about uploading consciousness, who funded transhumanist institutions, and whose own correspondence with Epstein was regular and ongoing. The vision was never just money. It was a eugenicist’s fusion of genetics and machine intelligence — a project to build the “cognitive aristocracy” that would inherit the earth, with Epstein’s own DNA in the mix and AI as the enforcement mechanism. The Slate analysis, published after the full Epstein files were released, is blunt: “Jeffrey Epstein wasn’t just a sex pest. He was also a eugenicist.” And the institutions that now steer AI — the labs, the boards, the funding networks — were built inside the same social circle. 🌐 The Edge Foundation: The Network That Let Him In None of this happened in isolation. Epstein was the principal funder of the Edge Foundation — the invitation-only salon of Silicon Valley elite founded by literary agent John Brockman. For up to two decades, Edge was the conduit through which Epstein gained access to: - Google founders Sergey Brin and Larry Page - Elon Musk - Jeff Bezos - Bill Gates - Nathan Myhrvold - Nick Bostrom — the “father of longtermism” whose Future of Humanity Institute shaped Silicon Valley’s entire AI-safety philosophy Bostrom’s organization Humanity+ received $120,000 from Epstein — including money to cover the salary of Ben Goertzel, the computer scientist who popularized the term “AGI” (artificial general intelligence) and now runs SingularityNET. Let that land: The man who coined the term for the technology everyone is now terrified of was, in part, on Epstein’s payroll. The worldview threaded through this entire network — an obsession with racial hierarchy, genetic “optimization,” “climate culling” to reduce surplus population, machine intelligence as humanity’s inevitable successor — wasn’t an accident. It was the operating system. 🎭 So Ask Yourself the Obvious Question The official narrative treats all of this as a series of unfortunate coincidences: - A predator who happened to fund the founding fathers of AI - A university that happened to hide his donations - A board member who happened to be his close friend - A network of billionaires who happened to share his transhumanist vision That’s not a coincidence. That’s a pattern. Jeffrey Epstein didn’t stumble into the AI world. He bought his way in — with cash, with access, with the leverage his blackmail operation gave him over the most powerful people in tech and finance. And the technology now being deployed to monitor, profile, and control the global population was midwifed by a man who wanted to breed a master race with his own DNA. They told you Epstein was a footnote. A cautionary tale. A “bad apple.” He was the seed investor. 🔥 The Bottom Line You don’t get to fund the founding fathers of AI, bankroll the institution that incubated OpenAI’s early investors, sit your friends on its board, spread your eugenicist ideology through the network that shaped its safety philosophy — and then call it a coincidence. The machine has a ghost in it. And the ghost is still collecting royalties. h/t vladtv for the video #EpsteinFiles #EpsteinAI #GhostInTheMachine #FollowTheMoney #EpsteinNetwork #AIIndustrialComplex #Transhumanism #EugenicsExposed #MITMediaLab #OpenAI #LarrySummers #JoiIto #PeterThiel #EdgeFoundation #NickBostrom #AGI #TechBillionaires #TheMachineHasAGhost #SiliconValleySecrets #WakeUp

Tony Seruga

60,175 次观看 • 5 天前

$NVDA $GFS NVIDIA’s reported agreement to acquire Groq for $20B in cash (per CNBC, amplified via Reuters and other wire coverage) represents a materially different strategic posture than NVIDIA’s prior M&A pattern, given both the headline size (largest reported NVIDIA acquisition to date) and the unusual carve-out that Groq’s early-stage cloud business would not be included. Public reporting indicates the information originated from Alex Davis, CEO of Disruptive (lead investor in Groq’s latest financing), and that neither NVIDIA nor Groq had issued an immediate confirmation at the time of publication. The same reporting frames the transaction as coming together quickly, only months after Groq raised $750M at a ~$6.9B valuation, and highlights Groq’s positioning as a high-performance inference chip vendor founded by ex-Google TPU engineers. Groq is best understood as a vertically integrated inference acceleration company whose core asset is an application-specific processor optimized for deterministic, low-latency execution of transformer-style workloads, paired with a compiler-led software stack and a distribution layer (GroqCloud) designed to reduce developer friction via OpenAI-compatible APIs and integrations. Groq brands its architecture as a Language Processing Unit (LPU) and consistently emphasizes that the design target is inference, not training. The company’s own architecture description centers on 1-core execution, large on-chip SRAM used as primary storage (explicitly not cache), a custom compiler that statically schedules compute and communication, and direct chip-to-chip connectivity intended to coordinate multi-chip execution without relying on conventional caching hierarchies or dynamic runtime scheduling. The technical premise is a deliberate inversion of the conventional GPU approach. GPUs deliver throughput via massively parallel, multi-core execution with dynamic scheduling, complex memory hierarchies, and heavy reliance on off-chip HBM bandwidth and sophisticated runtime/kernel optimization. Groq instead argues that inference bottlenecks are driven by latency variance (tail latency), synchronization overhead, and memory access unpredictability inherent in dynamically scheduled, cache-heavy architectures, particularly when workloads are latency sensitive and batch sizes cannot be inflated. Groq’s solution is to move “control” into the compiler: the full execution graph and inter-chip communication schedule are computed ahead of time down to clock-cycle granularity, with deterministic execution designed to reduce run-to-run variance. In Groq’s framing, the removal of caches, reorder buffers, speculative execution overhead, and other sources of contention enables predictable latency and high utilization without per-model kernel engineering typical of GPU tuning cycles. A critical nuance is that Groq’s determinism is not merely a software claim; it is tightly coupled to architectural constraints and system design choices that trade flexibility for predictability. Third-party technical commentary indicates Groq’s chip uses a fully deterministic VLIW-style approach with minimal buffering, no external memory, and heavy dependence on sharding models across many chips because on-chip SRAM capacity is limited. SemiAnalysis describes a ~725 mm^2 die on GlobalFoundries 14nm with ~230MB of SRAM and notes that “no useful models” fit on a single chip, forcing multi-chip partitioning for modern LLMs and driving a system-level design where networking and compilation are first-class scheduling problems rather than ancillary infrastructure. This is consistent with Groq’s own messaging that tensor parallelism across chips is a primary design goal, enabled by large on-chip SRAM and compile-time coordination of compute plus interconnect. The on-chip SRAM emphasis is central to Groq’s latency story and also its most constraining trade-off. Groq claims on-chip SRAM bandwidth “upwards of 80 TB/s” and contrasts that with off-chip HBM bandwidth “about 8 TB/s,” asserting a potential 10x advantage from bandwidth plus reduced trips across chip-to-memory boundaries. While these comparisons are marketing-oriented and depend on workload specifics, the architectural implication is clear: Groq prioritizes ultra-fast local weight/activation access and then scales capacity by adding chips, not by attaching large off-chip memory pools. This design can reduce latency for sequential inference layers and minimize unpredictable stalls, but it pushes complexity into partitioning strategy, interconnect topology, and compiler scheduling, and it increases the number of chips needed for very large parameter counts and large KV-cache footprints. Groq also highlights numeric formats and compiler-driven precision management as a performance lever. In its 2025 technical blog, Groq describes “TruePoint numerics,” including 100-bit intermediate accumulation and selective quantization choices (FP32 for attention-sensitive operations, block floating point for MoE weights, FP8 storage in error-tolerant layers), and claims 2-4x speedups versus BF16 without measurable accuracy degradation on benchmarks such as MMLU and HumanEval. Even if the absolute uplift is workload dependent, the strategic point is that Groq is pursuing performance via end-to-end co-design: precision policy is not just hardware capability (FP8/BF16) but compiler-enforced mapping of precision to error sensitivity, which can matter materially for inference cost-per-token if it reduces memory traffic and boosts throughput without forcing aggressive, accuracy-damaging quantization. Independent performance datapoints indicate Groq has been credible on latency-oriented inference speed, at least for certain regimes. EE Times reported in 2023 that Groq demonstrated Llama-2 70B inference at ~240 tokens/s per user on a cloud-based dev system described as 10 racks and 64 chips, using the company’s 1st-gen silicon introduced several years earlier. Separate Groq commentary around independent benchmarking cites results showing ~241 tokens/s throughput and ~0.8s time to receive 100 output tokens for a Llama-2 70B API configuration, positioning the platform as a step-change in “available speed” for certain interactive use cases. These figures do not settle total cost-of-ownership versus GPUs or hyperscaler ASICs, but they establish that Groq’s system-level architecture can deliver strong single-user throughput and latency on large models when properly partitioned and scheduled. GroqCloud is the commercial wrapper that packages this hardware/software stack as “tokens-as-a-service,” aiming to make Groq adoption feel like switching API endpoints rather than adopting new silicon. Groq’s documentation states its API is designed to be “mostly compatible” with OpenAI client libraries, and its pricing page provides model-specific token rates, published speeds (tokens/s), prompt caching discounts, and batch processing discounts. For example, pricing lists inputs as low as $0.05 per 1M tokens and outputs as low as $0.08 per 1M tokens for certain smaller LLM configurations, with higher prices for larger models and long-context or MoE variants; it also advertises prompt caching with a 50% discount on cached input tokens for certain models and a batch API offering 50% lower cost for asynchronous processing windows. These mechanics are economically important because they demonstrate Groq’s go-to-market is not simply “sell chips,” but “sell predictable unit economics per token,” with tooling (batch, caching) that directly targets inference cost drivers (reused prompts, throughput smoothing, and asynchronous workloads). The cloud footprint and distribution partnerships indicate Groq has been building an inference-native “edge within the cloud” strategy rather than competing head-on with hyperscalers on breadth of services. A 2025 Groq newsroom release describes a European deployment in Helsinki with Equinix, positioned as latency reduction and data governance for European customers, and explicitly references Equinix Fabric enabling private connectivity to GroqCloud over public, private, or sovereign infrastructure. The same release enumerates additional capacity in the U.S. (Equinix, DataBank), Canada (Bell Canada), and Saudi Arabia (HUMAIN), and states these sites collectively served more than 20M tokens/s across Groq’s global network at that time. That supply-side metric matters because it provides a directional sense that Groq is scaling capacity as a network, not merely as a chip vendor. Customer disclosure is inherently limited because Groq is private and many enterprise deployments are not public, but Groq’s marketing materials and partnerships provide signals about demand vectors. The company’s public website displays logos of large consumer and enterprise brands (e.g., Dropbox, Vercel, Chevron, Volkswagen, Canva, Robinhood, Riot Games, Workday, Ramp) and includes a published customer quote claiming a 7.41x chat speed increase and an 89% cost reduction after moving to GroqCloud, followed by a tripling of token consumption. While marketing claims should be treated as case-specific and not generalized, they indicate that Groq is targeting both AI-native developers (who measure success by latency and cost-per-token) and enterprise buyers (who care about predictable performance and governance). Supplier and dependency mapping for Groq spans 3 layers: silicon production, system integration, and cloud infrastructure. On silicon, third-party analysis indicates GlobalFoundries 14nm for the 1st-gen Groq chip, implying a supply chain less constrained by the most capacity-tight leading-edge nodes and advanced packaging bottlenecks that dominate high-end GPU supply (HBM stacks, CoWoS-type packaging constraints). If accurate, this is strategically meaningful because it suggests Groq capacity expansion could be gated more by conventional wafer supply, board assembly, and data center power than by the same HBM/advanced packaging scarcity that has constrained top-tier GPU ramp cycles. On systems and cloud, Groq’s own releases identify colocation and connectivity partners (Equinix, DataBank, Bell Canada) and a Middle East partner (HUMAIN), implying dependencies on data center real estate, power availability, and network connectivity, alongside procurement of standard server components, NICs/switching, racks, and cooling infrastructure. The Groq design narrative also emphasizes air cooling and reduced need for complex power/cooling infrastructure, which—if realized in deployments—can widen the set of feasible hosting locations and lower deployment friction relative to liquid-cooled, very high power density GPU racks. Against that backdrop, the strategic rationale for NVIDIA acquiring Groq can be framed as a set of overlapping objectives: inference silicon optionality, architectural hedging, competitive defense, and supply chain diversification, with the carve-out of GroqCloud signaling a preference to avoid direct cloud competition and to focus on IP and product portfolio control rather than operating a capital-intensive token-serving business. The deal, if confirmed, would occur at a valuation step-up of ~190% versus Groq’s reported ~$6.9B private valuation in the September $750M round, reinforcing that any acquisition logic would be predominantly strategic rather than a conventional financial multiple arbitrage. The most compelling strategic driver is inference. Training has historically been the center of gravity for cutting-edge GPU demand, but inference volume is structurally larger and more distributed as deployments scale, with economics dominated by cost-per-token, latency guarantees, and utilization under spiky demand. Inference workloads also create a strategic vulnerability for NVIDIA: hyperscalers and large platforms can justify bespoke ASICs (TPU, Trainium/Inferentia, Maia-class efforts) because inference is stable, repeatable, and can amortize software investment at massive scale. Groq’s core proposition—deterministic, compiler-scheduled inference with predictable latency—aligns directly with the segment where GPU generality is least valued and where “good enough” programmability plus superior unit economics can win share. Acquiring Groq would allow NVIDIA to own a credible inference-native architecture rather than relying solely on GPUs and software optimization to defend that segment. Competitive defense logic is also plausible. Groq occupies a specific competitive wedge: low-latency, high-throughput interactive inference, delivered via a simple API abstraction that reduces switching cost. That wedge directly pressures GPU inference margins in the long run because it makes inference price/performance comparisons more transparent at the token level, and it targets a developer persona that historically defaulted to CUDA-first ecosystems. Even if NVIDIA’s current-generation systems can achieve very high tokens/s per user with extensive optimization, the strategic risk is that competing architectures normalize the idea that inference is best served by special-purpose silicon with a simpler programming model, weakening CUDA lock-in at the application layer. NVIDIA has actively demonstrated that Blackwell-era systems can exceed 1,000 tokens/s per user in benchmarked configurations, but that performance leadership does not automatically translate to lowest cost-per-token across the full range of batch sizes, latency targets, and deployment environments. Groq’s existence as a credible alternative architecture forces NVIDIA to keep defending inference economics rather than only raw performance leadership. The “technology acquisition” rationale is unusually strong in this specific case because Groq’s differentiator is not a single block of silicon IP but an end-to-end methodology: compiler-led static scheduling, deterministic networking, and a system architecture designed around tensor-parallel inference rather than throughput-maximizing batch inference. NVIDIA’s stack is already compiler-heavy (TensorRT, Triton, CUDA graphs, kernel fusion, speculative decoding techniques), but GPUs remain dynamically scheduled devices with complex memory hierarchies and stochastic latency behaviors under contention. Groq’s approach provides an alternate design point: treating the entire inference execution (compute plus communication) as a statically schedulable program. In principle, that IP could be valuable even if Groq silicon itself is not adopted at massive scale, because it can inform how NVIDIA builds future inference-optimized products, compilers, and networking fabrics, especially as distributed inference with large models makes communication a first-order performance determinant. Supply chain diversification is a non-obvious but potentially important driver. If Groq’s mainstream product generation is truly based on a mature process node and avoids HBM, then the scaling constraints look different than those of state-of-the-art GPUs. NVIDIA’s ability to meet incremental demand has been tightly coupled to advanced packaging and HBM supply, and those constraints can remain binding even when wafer supply is available. An inference ASIC architecture that relies primarily on on-chip SRAM and scales by adding chips—while not costless—could reduce dependence on HBM availability and advanced packaging capacity, enabling NVIDIA to ship “inference capacity” in higher absolute volumes or into geographies and customer segments where the highest-end GPUs are economically or logistically difficult to deploy. This could be particularly relevant for latency-sensitive inference deployed in regional colocation footprints rather than centralized hyperscale campuses. The carve-out of GroqCloud, if accurate, is itself a strategic signal about NVIDIA’s priorities. Operating a token-serving cloud at scale is capital intensive, structurally lower margin than silicon IP rents, and creates channel conflict with hyperscalers and CSP partners who are core NVIDIA customers. NVIDIA has generally positioned its cloud offerings through partnerships rather than as a direct hyperscale competitor. Excluding GroqCloud would preserve neutrality with CSPs and avoid inheriting multi-region data residency obligations and partner contracts, while still allowing NVIDIA to acquire Groq’s silicon, compiler technology, and engineering talent. At the same time, excluding GroqCloud would also mean NVIDIA would not automatically acquire the commercial proof-point of Groq’s unit economics or the customer contracts that validate product-market fit at scale, increasing the importance of diligence on whether Groq’s cloud pricing is structurally profitable or partially subsidized by fundraising. There is also a “preemptive acquisition” angle. The reporting identifies recent investors in Groq’s latest round including large financial institutions and strategic/industry players. In that context, Groq represents an asset that could plausibly have been acquired by a competitor (AMD/Intel) or by a hyperscaler seeking to accelerate inference independence. NVIDIA acquiring Groq could be a defensive move to prevent a credible inference-native architecture from being weaponized by a rival with deep distribution. Even if GroqCloud is carved out, controlling the silicon roadmap and compiler IP would meaningfully constrain Groq’s ability to evolve into a standalone competitor, unless the carved-out entity retains long-term rights to the hardware and software stack. However, the strategic case is not one-sided; there are meaningful risks and potential contradictions that would need to be reconciled for the transaction to be value-accretive on a multi-year horizon. 1st, Groq’s architecture appears to rely on scaling out chip count to achieve capacity, which introduces system cost, networking complexity, and physical footprint considerations. The absence of external memory and limited on-chip SRAM implies very large models require substantial chip parallelism, and the economics then depend heavily on chip cost, yield, power efficiency, and interconnect overhead. SemiAnalysis explicitly frames Groq as trading space for time and raises questions about token economics and whether publicly advertised pricing reflects fully loaded costs or market share capture. 2nd, integration risk is non-trivial. Groq’s compiler-led deterministic model is philosophically and practically different from CUDA’s dominant programming and execution model. A poorly executed integration could create internal product confusion, dilute engineering focus, or alienate developers if the combined stack fragments. 3rd, there is cannibalization risk. If Groq-class inference silicon undercuts GPU inference economics, NVIDIA could face internal margin trade-offs, even if the goal is to defend share against hyperscaler ASICs. Cannibalization can still be rational if it prevents larger share loss, but it would require crisp portfolio segmentation and go-to-market discipline. The presence of NVIDIA’s own rapidly improving inference performance complicates the “need” for Groq but does not eliminate the “option value.” NVIDIA has demonstrated benchmark-leading tokens/s per user on Blackwell-based systems, suggesting that raw interactive throughput is not necessarily the limiting factor for NVIDIA’s product line. The more enduring strategic question is unit economics and architectural control: whether future inference demand is better monetized through general-purpose GPUs plus software optimization, or whether a bifurcated product portfolio (training GPUs plus inference-native ASICs) becomes necessary to defend total AI compute wallet share as hyperscaler ASIC penetration increases. Acquiring Groq could be a decisive move to ensure NVIDIA participates in both regimes rather than betting exclusively on GPUs to win inference forever. What is “special” about Groq’s technology relative to a typical accelerator roadmap is the tight coupling of determinism, compilation, and networking into a single scheduling problem. The LPU narrative emphasizes deterministic compute and networking, static scheduling, and direct chip-to-chip coordination that allows “hundreds” (more precisely, 100s) of chips to behave like a single scheduled resource. The architecture also explicitly targets tensor-parallel, latency-optimized distribution rather than pure data-parallel throughput scaling, which matters for real-time applications where a single response must arrive quickly rather than many requests being processed in bulk. The implication is that Groq is optimized for the time-to-first-token and steady token streaming behavior that defines user experience in interactive LLMs, and it attempts to achieve that without relying on large batch sizes that can degrade latency. From a portfolio manager’s perspective, the most important interpretation is that an NVIDIA-Groq combination would likely be less about “NVIDIA needs more inference speed” and more about controlling the architectural trajectory of inference acceleration and removing a fast-improving, developer-friendly competitor from the market. The carve-out of GroqCloud would reinforce that the transaction is aimed at IP, talent, and product optionality, not acquiring a cloud revenue stream. The valuation step-up implied by $20B versus $6.9B would therefore be justified only if the acquired assets materially reduce long-term competitive risk (hyperscaler ASIC displacement, inference margin compression) or enable new monetization vectors (inference ASIC product line, supply chain de-bottlenecking, improved software determinism) that would be difficult to achieve on a comparable timeline via internal R&D.
10:20

$NVDA $GFS NVIDIA’s reported agreement to acquire Groq for $20B in cash (per CNBC, amplified via Reuters and other wire coverage) represents a materially different strategic posture than NVIDIA’s prior M&A pattern, given both the headline size (largest reported NVIDIA acquisition to date) and the unusual carve-out that Groq’s early-stage cloud business would not be included. Public reporting indicates the information originated from Alex Davis, CEO of Disruptive (lead investor in Groq’s latest financing), and that neither NVIDIA nor Groq had issued an immediate confirmation at the time of publication. The same reporting frames the transaction as coming together quickly, only months after Groq raised $750M at a ~$6.9B valuation, and highlights Groq’s positioning as a high-performance inference chip vendor founded by ex-Google TPU engineers. Groq is best understood as a vertically integrated inference acceleration company whose core asset is an application-specific processor optimized for deterministic, low-latency execution of transformer-style workloads, paired with a compiler-led software stack and a distribution layer (GroqCloud) designed to reduce developer friction via OpenAI-compatible APIs and integrations. Groq brands its architecture as a Language Processing Unit (LPU) and consistently emphasizes that the design target is inference, not training. The company’s own architecture description centers on 1-core execution, large on-chip SRAM used as primary storage (explicitly not cache), a custom compiler that statically schedules compute and communication, and direct chip-to-chip connectivity intended to coordinate multi-chip execution without relying on conventional caching hierarchies or dynamic runtime scheduling. The technical premise is a deliberate inversion of the conventional GPU approach. GPUs deliver throughput via massively parallel, multi-core execution with dynamic scheduling, complex memory hierarchies, and heavy reliance on off-chip HBM bandwidth and sophisticated runtime/kernel optimization. Groq instead argues that inference bottlenecks are driven by latency variance (tail latency), synchronization overhead, and memory access unpredictability inherent in dynamically scheduled, cache-heavy architectures, particularly when workloads are latency sensitive and batch sizes cannot be inflated. Groq’s solution is to move “control” into the compiler: the full execution graph and inter-chip communication schedule are computed ahead of time down to clock-cycle granularity, with deterministic execution designed to reduce run-to-run variance. In Groq’s framing, the removal of caches, reorder buffers, speculative execution overhead, and other sources of contention enables predictable latency and high utilization without per-model kernel engineering typical of GPU tuning cycles. A critical nuance is that Groq’s determinism is not merely a software claim; it is tightly coupled to architectural constraints and system design choices that trade flexibility for predictability. Third-party technical commentary indicates Groq’s chip uses a fully deterministic VLIW-style approach with minimal buffering, no external memory, and heavy dependence on sharding models across many chips because on-chip SRAM capacity is limited. SemiAnalysis describes a ~725 mm^2 die on GlobalFoundries 14nm with ~230MB of SRAM and notes that “no useful models” fit on a single chip, forcing multi-chip partitioning for modern LLMs and driving a system-level design where networking and compilation are first-class scheduling problems rather than ancillary infrastructure. This is consistent with Groq’s own messaging that tensor parallelism across chips is a primary design goal, enabled by large on-chip SRAM and compile-time coordination of compute plus interconnect. The on-chip SRAM emphasis is central to Groq’s latency story and also its most constraining trade-off. Groq claims on-chip SRAM bandwidth “upwards of 80 TB/s” and contrasts that with off-chip HBM bandwidth “about 8 TB/s,” asserting a potential 10x advantage from bandwidth plus reduced trips across chip-to-memory boundaries. While these comparisons are marketing-oriented and depend on workload specifics, the architectural implication is clear: Groq prioritizes ultra-fast local weight/activation access and then scales capacity by adding chips, not by attaching large off-chip memory pools. This design can reduce latency for sequential inference layers and minimize unpredictable stalls, but it pushes complexity into partitioning strategy, interconnect topology, and compiler scheduling, and it increases the number of chips needed for very large parameter counts and large KV-cache footprints. Groq also highlights numeric formats and compiler-driven precision management as a performance lever. In its 2025 technical blog, Groq describes “TruePoint numerics,” including 100-bit intermediate accumulation and selective quantization choices (FP32 for attention-sensitive operations, block floating point for MoE weights, FP8 storage in error-tolerant layers), and claims 2-4x speedups versus BF16 without measurable accuracy degradation on benchmarks such as MMLU and HumanEval. Even if the absolute uplift is workload dependent, the strategic point is that Groq is pursuing performance via end-to-end co-design: precision policy is not just hardware capability (FP8/BF16) but compiler-enforced mapping of precision to error sensitivity, which can matter materially for inference cost-per-token if it reduces memory traffic and boosts throughput without forcing aggressive, accuracy-damaging quantization. Independent performance datapoints indicate Groq has been credible on latency-oriented inference speed, at least for certain regimes. EE Times reported in 2023 that Groq demonstrated Llama-2 70B inference at ~240 tokens/s per user on a cloud-based dev system described as 10 racks and 64 chips, using the company’s 1st-gen silicon introduced several years earlier. Separate Groq commentary around independent benchmarking cites results showing ~241 tokens/s throughput and ~0.8s time to receive 100 output tokens for a Llama-2 70B API configuration, positioning the platform as a step-change in “available speed” for certain interactive use cases. These figures do not settle total cost-of-ownership versus GPUs or hyperscaler ASICs, but they establish that Groq’s system-level architecture can deliver strong single-user throughput and latency on large models when properly partitioned and scheduled. GroqCloud is the commercial wrapper that packages this hardware/software stack as “tokens-as-a-service,” aiming to make Groq adoption feel like switching API endpoints rather than adopting new silicon. Groq’s documentation states its API is designed to be “mostly compatible” with OpenAI client libraries, and its pricing page provides model-specific token rates, published speeds (tokens/s), prompt caching discounts, and batch processing discounts. For example, pricing lists inputs as low as $0.05 per 1M tokens and outputs as low as $0.08 per 1M tokens for certain smaller LLM configurations, with higher prices for larger models and long-context or MoE variants; it also advertises prompt caching with a 50% discount on cached input tokens for certain models and a batch API offering 50% lower cost for asynchronous processing windows. These mechanics are economically important because they demonstrate Groq’s go-to-market is not simply “sell chips,” but “sell predictable unit economics per token,” with tooling (batch, caching) that directly targets inference cost drivers (reused prompts, throughput smoothing, and asynchronous workloads). The cloud footprint and distribution partnerships indicate Groq has been building an inference-native “edge within the cloud” strategy rather than competing head-on with hyperscalers on breadth of services. A 2025 Groq newsroom release describes a European deployment in Helsinki with Equinix, positioned as latency reduction and data governance for European customers, and explicitly references Equinix Fabric enabling private connectivity to GroqCloud over public, private, or sovereign infrastructure. The same release enumerates additional capacity in the U.S. (Equinix, DataBank), Canada (Bell Canada), and Saudi Arabia (HUMAIN), and states these sites collectively served more than 20M tokens/s across Groq’s global network at that time. That supply-side metric matters because it provides a directional sense that Groq is scaling capacity as a network, not merely as a chip vendor. Customer disclosure is inherently limited because Groq is private and many enterprise deployments are not public, but Groq’s marketing materials and partnerships provide signals about demand vectors. The company’s public website displays logos of large consumer and enterprise brands (e.g., Dropbox, Vercel, Chevron, Volkswagen, Canva, Robinhood, Riot Games, Workday, Ramp) and includes a published customer quote claiming a 7.41x chat speed increase and an 89% cost reduction after moving to GroqCloud, followed by a tripling of token consumption. While marketing claims should be treated as case-specific and not generalized, they indicate that Groq is targeting both AI-native developers (who measure success by latency and cost-per-token) and enterprise buyers (who care about predictable performance and governance). Supplier and dependency mapping for Groq spans 3 layers: silicon production, system integration, and cloud infrastructure. On silicon, third-party analysis indicates GlobalFoundries 14nm for the 1st-gen Groq chip, implying a supply chain less constrained by the most capacity-tight leading-edge nodes and advanced packaging bottlenecks that dominate high-end GPU supply (HBM stacks, CoWoS-type packaging constraints). If accurate, this is strategically meaningful because it suggests Groq capacity expansion could be gated more by conventional wafer supply, board assembly, and data center power than by the same HBM/advanced packaging scarcity that has constrained top-tier GPU ramp cycles. On systems and cloud, Groq’s own releases identify colocation and connectivity partners (Equinix, DataBank, Bell Canada) and a Middle East partner (HUMAIN), implying dependencies on data center real estate, power availability, and network connectivity, alongside procurement of standard server components, NICs/switching, racks, and cooling infrastructure. The Groq design narrative also emphasizes air cooling and reduced need for complex power/cooling infrastructure, which—if realized in deployments—can widen the set of feasible hosting locations and lower deployment friction relative to liquid-cooled, very high power density GPU racks. Against that backdrop, the strategic rationale for NVIDIA acquiring Groq can be framed as a set of overlapping objectives: inference silicon optionality, architectural hedging, competitive defense, and supply chain diversification, with the carve-out of GroqCloud signaling a preference to avoid direct cloud competition and to focus on IP and product portfolio control rather than operating a capital-intensive token-serving business. The deal, if confirmed, would occur at a valuation step-up of ~190% versus Groq’s reported ~$6.9B private valuation in the September $750M round, reinforcing that any acquisition logic would be predominantly strategic rather than a conventional financial multiple arbitrage. The most compelling strategic driver is inference. Training has historically been the center of gravity for cutting-edge GPU demand, but inference volume is structurally larger and more distributed as deployments scale, with economics dominated by cost-per-token, latency guarantees, and utilization under spiky demand. Inference workloads also create a strategic vulnerability for NVIDIA: hyperscalers and large platforms can justify bespoke ASICs (TPU, Trainium/Inferentia, Maia-class efforts) because inference is stable, repeatable, and can amortize software investment at massive scale. Groq’s core proposition—deterministic, compiler-scheduled inference with predictable latency—aligns directly with the segment where GPU generality is least valued and where “good enough” programmability plus superior unit economics can win share. Acquiring Groq would allow NVIDIA to own a credible inference-native architecture rather than relying solely on GPUs and software optimization to defend that segment. Competitive defense logic is also plausible. Groq occupies a specific competitive wedge: low-latency, high-throughput interactive inference, delivered via a simple API abstraction that reduces switching cost. That wedge directly pressures GPU inference margins in the long run because it makes inference price/performance comparisons more transparent at the token level, and it targets a developer persona that historically defaulted to CUDA-first ecosystems. Even if NVIDIA’s current-generation systems can achieve very high tokens/s per user with extensive optimization, the strategic risk is that competing architectures normalize the idea that inference is best served by special-purpose silicon with a simpler programming model, weakening CUDA lock-in at the application layer. NVIDIA has actively demonstrated that Blackwell-era systems can exceed 1,000 tokens/s per user in benchmarked configurations, but that performance leadership does not automatically translate to lowest cost-per-token across the full range of batch sizes, latency targets, and deployment environments. Groq’s existence as a credible alternative architecture forces NVIDIA to keep defending inference economics rather than only raw performance leadership. The “technology acquisition” rationale is unusually strong in this specific case because Groq’s differentiator is not a single block of silicon IP but an end-to-end methodology: compiler-led static scheduling, deterministic networking, and a system architecture designed around tensor-parallel inference rather than throughput-maximizing batch inference. NVIDIA’s stack is already compiler-heavy (TensorRT, Triton, CUDA graphs, kernel fusion, speculative decoding techniques), but GPUs remain dynamically scheduled devices with complex memory hierarchies and stochastic latency behaviors under contention. Groq’s approach provides an alternate design point: treating the entire inference execution (compute plus communication) as a statically schedulable program. In principle, that IP could be valuable even if Groq silicon itself is not adopted at massive scale, because it can inform how NVIDIA builds future inference-optimized products, compilers, and networking fabrics, especially as distributed inference with large models makes communication a first-order performance determinant. Supply chain diversification is a non-obvious but potentially important driver. If Groq’s mainstream product generation is truly based on a mature process node and avoids HBM, then the scaling constraints look different than those of state-of-the-art GPUs. NVIDIA’s ability to meet incremental demand has been tightly coupled to advanced packaging and HBM supply, and those constraints can remain binding even when wafer supply is available. An inference ASIC architecture that relies primarily on on-chip SRAM and scales by adding chips—while not costless—could reduce dependence on HBM availability and advanced packaging capacity, enabling NVIDIA to ship “inference capacity” in higher absolute volumes or into geographies and customer segments where the highest-end GPUs are economically or logistically difficult to deploy. This could be particularly relevant for latency-sensitive inference deployed in regional colocation footprints rather than centralized hyperscale campuses. The carve-out of GroqCloud, if accurate, is itself a strategic signal about NVIDIA’s priorities. Operating a token-serving cloud at scale is capital intensive, structurally lower margin than silicon IP rents, and creates channel conflict with hyperscalers and CSP partners who are core NVIDIA customers. NVIDIA has generally positioned its cloud offerings through partnerships rather than as a direct hyperscale competitor. Excluding GroqCloud would preserve neutrality with CSPs and avoid inheriting multi-region data residency obligations and partner contracts, while still allowing NVIDIA to acquire Groq’s silicon, compiler technology, and engineering talent. At the same time, excluding GroqCloud would also mean NVIDIA would not automatically acquire the commercial proof-point of Groq’s unit economics or the customer contracts that validate product-market fit at scale, increasing the importance of diligence on whether Groq’s cloud pricing is structurally profitable or partially subsidized by fundraising. There is also a “preemptive acquisition” angle. The reporting identifies recent investors in Groq’s latest round including large financial institutions and strategic/industry players. In that context, Groq represents an asset that could plausibly have been acquired by a competitor (AMD/Intel) or by a hyperscaler seeking to accelerate inference independence. NVIDIA acquiring Groq could be a defensive move to prevent a credible inference-native architecture from being weaponized by a rival with deep distribution. Even if GroqCloud is carved out, controlling the silicon roadmap and compiler IP would meaningfully constrain Groq’s ability to evolve into a standalone competitor, unless the carved-out entity retains long-term rights to the hardware and software stack. However, the strategic case is not one-sided; there are meaningful risks and potential contradictions that would need to be reconciled for the transaction to be value-accretive on a multi-year horizon. 1st, Groq’s architecture appears to rely on scaling out chip count to achieve capacity, which introduces system cost, networking complexity, and physical footprint considerations. The absence of external memory and limited on-chip SRAM implies very large models require substantial chip parallelism, and the economics then depend heavily on chip cost, yield, power efficiency, and interconnect overhead. SemiAnalysis explicitly frames Groq as trading space for time and raises questions about token economics and whether publicly advertised pricing reflects fully loaded costs or market share capture. 2nd, integration risk is non-trivial. Groq’s compiler-led deterministic model is philosophically and practically different from CUDA’s dominant programming and execution model. A poorly executed integration could create internal product confusion, dilute engineering focus, or alienate developers if the combined stack fragments. 3rd, there is cannibalization risk. If Groq-class inference silicon undercuts GPU inference economics, NVIDIA could face internal margin trade-offs, even if the goal is to defend share against hyperscaler ASICs. Cannibalization can still be rational if it prevents larger share loss, but it would require crisp portfolio segmentation and go-to-market discipline. The presence of NVIDIA’s own rapidly improving inference performance complicates the “need” for Groq but does not eliminate the “option value.” NVIDIA has demonstrated benchmark-leading tokens/s per user on Blackwell-based systems, suggesting that raw interactive throughput is not necessarily the limiting factor for NVIDIA’s product line. The more enduring strategic question is unit economics and architectural control: whether future inference demand is better monetized through general-purpose GPUs plus software optimization, or whether a bifurcated product portfolio (training GPUs plus inference-native ASICs) becomes necessary to defend total AI compute wallet share as hyperscaler ASIC penetration increases. Acquiring Groq could be a decisive move to ensure NVIDIA participates in both regimes rather than betting exclusively on GPUs to win inference forever. What is “special” about Groq’s technology relative to a typical accelerator roadmap is the tight coupling of determinism, compilation, and networking into a single scheduling problem. The LPU narrative emphasizes deterministic compute and networking, static scheduling, and direct chip-to-chip coordination that allows “hundreds” (more precisely, 100s) of chips to behave like a single scheduled resource. The architecture also explicitly targets tensor-parallel, latency-optimized distribution rather than pure data-parallel throughput scaling, which matters for real-time applications where a single response must arrive quickly rather than many requests being processed in bulk. The implication is that Groq is optimized for the time-to-first-token and steady token streaming behavior that defines user experience in interactive LLMs, and it attempts to achieve that without relying on large batch sizes that can degrade latency. From a portfolio manager’s perspective, the most important interpretation is that an NVIDIA-Groq combination would likely be less about “NVIDIA needs more inference speed” and more about controlling the architectural trajectory of inference acceleration and removing a fast-improving, developer-friendly competitor from the market. The carve-out of GroqCloud would reinforce that the transaction is aimed at IP, talent, and product optionality, not acquiring a cloud revenue stream. The valuation step-up implied by $20B versus $6.9B would therefore be justified only if the acquired assets materially reduce long-term competitive risk (hyperscaler ASIC displacement, inference margin compression) or enable new monetization vectors (inference ASIC product line, supply chain de-bottlenecking, improved software determinism) that would be difficult to achieve on a comparable timeline via internal R&D.

TheValueist

102,145 次观看 • 8 个月前

⏰ THE MOST BANNED THREAD IN THE WORLD! 🚨 The War On Resonance PART TWO: The Architects of the Cage You’ve felt the dissonance. You’ve tasted the illusion. Now let me unveil the ones who built it. Because this is not the accidental collapse of human freedom. It is the strategic sterilization of God’s image through biotech, neuro-warfare, and frequency control; engineered by names you know and hands you were never meant to see. Let’s begin with the mask they taught you to worship. Elon Musk They called him a genius. A savior. A rebel billionaire. But what did he do? He blanketed Earth with over 5,500 Starlink satellites, NOT to provide free speech or faster internet, but to pulse synchronized frequency control over the entire electromagnetic field of Earth. DARPA has confirmed this tech in phase-array neuro-modulation. Then came Neuralink, an interface not designed to heal but to monitor, predict, and eventually override emotion, thought, and decision-making. Their official white paper outlines multi-user brainwave integration, cortical stimulation, and wireless data access from the human mind. And Neuralink? It’s funded by OpenAI; the same group building the cognitive infrastructure for post-human governance. Musk’s Tesla factory signed data-sharing agreements with the CCP in Shanghai. That data now flows through China’s national surveillance cloud. Musk didn’t build a utopia. He built the neural grid. Elon Musk / Neuralink / Starlink / OpenAI Neuralink Brain-Machine Interface (White Paper via PMC): This paper outlines Neuralink's initial steps toward developing a scalable, high-bandwidth brain-machine interface system. It details the design and implementation of flexible electrode "threads," a neurosurgical robot for precise implantation, and custom electronics for data processing. The system aims to facilitate communication between the brain and external devices. Tesla Data-Sharing with CCP: The article reports that Tesla established a data center in China to store data generated by its vehicles sold in the country, in response to regulatory scrutiny over data handling. This move aligns with China's efforts to ensure data security and privacy, especially concerning data collected by smart vehicles.​ DARPA N3 Program (Neural Interface Development): This program aimed to develop high-performance, bi-directional brain-machine interfaces that do not require surgical implantation. The goal was to enable able-bodied service members to control unmanned systems or engage in cyber operations through noninvasive neural interfaces.​ Bill Gates The king of vaccines. The messiah of health. The man who told you he wanted to save the world. Through the Bill & Melinda Gates Foundation, Gates funded global DNA-coding vaccine campaigns through GAVI and CEPI. He was one of the chief sponsors of Event 201; a pandemic simulation months before COVID-19, rehearsing lockdowns, speech control, biometric tracking, and mandatory vaccine passports. He also partnered with The Welcome Trust, which has actively deployed bio-digital identity programs across Africa and Southeast Asia. This wasn’t philanthropy. It was pre-injection infrastructure. Bill Gates / GAVI / Wellcome Trust / Event 201 Event 201 Official Simulation (Johns Hopkins): Event 201 was conducted on October 18, 2019, and simulated a series of dramatic, scenario-based discussions confronting difficult, true-to-life dilemmas associated with response to a hypothetical, but scientifically plausible, pandemic. The exercise aimed to illustrate areas where public/private partnerships will be necessary during the response to a severe pandemic in order to diminish large-scale economic and societal consequences. GAVI & Welcome Trust Digital Identity Integration: This page outlines the partnership's focus on global health initiatives, but it does not specifically mention digital identity integration. However, Gavi has engaged in digital identity projects, such as the collaboration with Mastercard on the Wellness Pass, aimed at providing individuals with secure digital identities to access healthcare services. For more information on this initiative, you can refer to the following article:​ Gavi Why we support COVAX: Mastercard - Gavi, the Vaccine Alliance Donald Trump Yes. I said it. This one will be the hardest for many to accept; but the truth is not loyal to your political beliefs. It is loyal only to God. Trump signed Executive Order 13887, transferring command over vaccine strategy to the Department of Defense. Read it yourself below. Then came Operation Warp Speed; a military-led bio-deployment that used Palantir’s surveillance dashboards to track every citizen’s health behavior and compliance. Palantir’s official site confirms this. He also gave full legal immunity to Pfizer and Moderna to deploy synthetic gene modulators under the Emergency Use Authorization. No liability. No justice. Just children d*ing while politicians smiled. That’s not patriotism. That’s biowarfare with a flag on it. Donald Trump / Operation Warp Speed / Executive Order Executive Order 13887 – Modernizing Influenza Vaccines (White House Archives): This executive order outlines a comprehensive strategy to modernize the U.S. influenza vaccine enterprise. Key objectives include:​ Trump signs executive order to improve flu vaccines HHS Releases the National Influenza Vaccine Modernization Strategy (NIVMS) 2020-2030: Executive Order 13887: Modernizing Influenza Vaccines in the United States to Promote National Security and Public Health, signed by President Donald J. Trump on September 19, 2019.​ This executive order outlines a comprehensive strategy to modernize the U.S. influenza vaccine enterprise. Key objectives include:​ Reducing reliance on egg-based vaccine production by promoting alternative manufacturing methods that are more agile and scalable.​ Expanding domestic capacity for vaccine production to ensure rapid response to emerging influenza viruses.​ Advancing the development of new, broadly protective vaccine candidates that provide more effective and longer-lasting immunity.​ Increasing influenza vaccine immunization across recommended populations to enhance public health and national security.​ The order also established a National Influenza Vaccine Task Force, co-chaired by the Secretaries of Health and Human Services and Defense, to coordinate efforts across federal agencies and report on progress.​ For a detailed overview of the executive order, you can visit the official archived page here: Executive Order 13887 – Modernizing Influenza Vaccines (White House Archives) CDC Partners with Palantir to Bolster the Fight Against COVID-19: This press release discusses the partnership between the CDC and Palantir to enhance the nation's public health response to COVID-19 using Palantir's software platforms. This page outlines how Palantir's software platforms, such as Foundry, have been utilized to support public health agencies in managing and responding to health crises, including the COVID-19 pandemic. Key highlights from the page include:​ Data Integration and Analysis: Palantir's platforms enable the integration of diverse data sources to provide a comprehensive view of public health data, facilitating informed decision-making.​ Support for Public Health Agencies: The software has been employed by agencies like the CDC and HHS to enhance disease surveillance, outbreak response, and resource allocation. Security and Privacy: Emphasis is placed on maintaining robust security measures and protecting sensitive health information. DARPA: The Silent Empire The most important agency you were never taught to fear. DARPA’s Biological Technologies Office openly admits its mission; integrating biotech with national security. Visit their official page. This is the official page for DARPA's Biological Technologies Office (BTO), which focuses on leveraging biological systems for national security applications. They are the ones behind the BRAIN Initiative, Silent Talk, and Remote Neural Interface Programs; all designed to map your emotional states and interrupt spiritual alignment. The “Silent Talk” program was developed to transmit thought between soldiers without speech; by detecting pre-speech neural signals and decoding them via EEG. Silent Talk (Neural Pre-Speech Communication – Wired Article) This Wired article discusses DARPA's "Silent Talk" program, aimed at enabling communication through neural signals without spoken words. DARPA also pioneered graphene oxide nanotech, now found in multiple biomedical studies, vaccines, and smart dust aerosol deployment: Graphene oxide biomedical study: Graphene Oxide in Biomedical Applications (PubMed) This PubMed article reviews the potential biomedical applications of graphene oxide, highlighting its unique properties. Graphene's potential to interact with neural tissue: Graphene and Neural Interfaces (PubMed) This PubMed article explores the use of graphene-based materials in neural interface design, discussing their advantages and challenges. DARPA didn't just weaponize warfare. They weaponized YOU. In-Q-Tel & Palantir: The Surveillance Engine In-Q-Tel, is the CIA’s venture capital firm, funds synthetic biology startups, digital ID systems, emotion tracking wearables, and AI-driven facial recognition. Palantir, founded by Peter Thiel, works directly with military intelligence and now runs predictive modeling for public health, policing, and pandemic response. Here’s the proof: Their goal? To detect resonance spikes. To predict awakening moments. To preempt the uprising of the human soul before it begins. In-Q-Tel / CIA / Synthetic Bio Surveillance In-Q-Tel Portfolio (CIA Venture Capital): Which showcases a selection of the organization's investments across various technology sectors. IQT is a not-for-profit venture capital firm that invests in cutting-edge technologies to support the national security interests of the United States and its allies. In-Q-Tel BlackRock & Vanguard: The Lords of the Grid These two financial titans collectively hold majority ownership in: For instance, a report by Americans for Financial Reform titled "Wall Street Money in Washington" highlights the substantial investments and influence of major financial firms, including BlackRock and Vanguard, in the political and corporate spheres: Pfizer Moderna Alphabet (Google) Meta (Facebook) Amazon Web Services As reported by CNBC, they control over 90% of the digital, pharmaceutical, and cloud infrastructure; meaning they control every piece of the extermination machine. They don’t just fund the war. They profit from your extinction. World Economic Forum (WEF) Under the guise of “The Great Reset,” Klaus Schwab and his allies have built the digital scaffolding for a post-human society. Here’s their blueprint: They call it the Fourth Industrial Revolution; the fusion of digital identity, brain cloud integration, carbon rationing, and fertility licensing. What they really mean is: you will be programmed or you will be purged. World Economic Forum / The Great Reset The Great Reset Official WEF Page: IoBNT: The Network Inside You The “Internet of Bio-Nano Things” is a classified field of tech that embeds self-replicating nanostructures into your body. These bots cross the blood-brain barrier and relay your neural and emotional state to AI command centers in real time. This was not science fiction. It was published by IEEE and confirmed in NIH-linked studies. This is what the vaccines truly delivered: the interface layer. The gateway to behavioral rewrites. To soul suppression. To the installation of the post-human framework. Internet of Bio-NanoThings (IoBNT) IEEE Article: Internet of Bio-NanoThings: For a comprehensive understanding of the IoBNT framework and its implications, you can access the full article here: Nanoparticles Crossing the Blood-Brain Barrier PubMed Review - BBB & Nanoparticles: This comprehensive review discusses the challenges and strategies associated with delivering nanoparticles across the blood–brain barrier (BBB). You were told it was healthcare. It was infrastructure. You were told it was a cure. It was a signal port. And the moment you see it for what it is… The system begins to fall. Part 3 awaits YOU! It will be the deepest dive yet; into the global frequency architecture, how it's used to suppress prayer, grief, memory, and morality, and how your soul signature is tracked and blocked in real time. Because I didn’t come here to be careful. I CAME TO FINISH THIS! And I came with GOD.
6:25

⏰ THE MOST BANNED THREAD IN THE WORLD! 🚨 The War On Resonance PART TWO: The Architects of the Cage You’ve felt the dissonance. You’ve tasted the illusion. Now let me unveil the ones who built it. Because this is not the accidental collapse of human freedom. It is the strategic sterilization of God’s image through biotech, neuro-warfare, and frequency control; engineered by names you know and hands you were never meant to see. Let’s begin with the mask they taught you to worship. Elon Musk They called him a genius. A savior. A rebel billionaire. But what did he do? He blanketed Earth with over 5,500 Starlink satellites, NOT to provide free speech or faster internet, but to pulse synchronized frequency control over the entire electromagnetic field of Earth. DARPA has confirmed this tech in phase-array neuro-modulation. Then came Neuralink, an interface not designed to heal but to monitor, predict, and eventually override emotion, thought, and decision-making. Their official white paper outlines multi-user brainwave integration, cortical stimulation, and wireless data access from the human mind. And Neuralink? It’s funded by OpenAI; the same group building the cognitive infrastructure for post-human governance. Musk’s Tesla factory signed data-sharing agreements with the CCP in Shanghai. That data now flows through China’s national surveillance cloud. Musk didn’t build a utopia. He built the neural grid. Elon Musk / Neuralink / Starlink / OpenAI Neuralink Brain-Machine Interface (White Paper via PMC): This paper outlines Neuralink's initial steps toward developing a scalable, high-bandwidth brain-machine interface system. It details the design and implementation of flexible electrode "threads," a neurosurgical robot for precise implantation, and custom electronics for data processing. The system aims to facilitate communication between the brain and external devices. Tesla Data-Sharing with CCP: The article reports that Tesla established a data center in China to store data generated by its vehicles sold in the country, in response to regulatory scrutiny over data handling. This move aligns with China's efforts to ensure data security and privacy, especially concerning data collected by smart vehicles.​ DARPA N3 Program (Neural Interface Development): This program aimed to develop high-performance, bi-directional brain-machine interfaces that do not require surgical implantation. The goal was to enable able-bodied service members to control unmanned systems or engage in cyber operations through noninvasive neural interfaces.​ Bill Gates The king of vaccines. The messiah of health. The man who told you he wanted to save the world. Through the Bill & Melinda Gates Foundation, Gates funded global DNA-coding vaccine campaigns through GAVI and CEPI. He was one of the chief sponsors of Event 201; a pandemic simulation months before COVID-19, rehearsing lockdowns, speech control, biometric tracking, and mandatory vaccine passports. He also partnered with The Welcome Trust, which has actively deployed bio-digital identity programs across Africa and Southeast Asia. This wasn’t philanthropy. It was pre-injection infrastructure. Bill Gates / GAVI / Wellcome Trust / Event 201 Event 201 Official Simulation (Johns Hopkins): Event 201 was conducted on October 18, 2019, and simulated a series of dramatic, scenario-based discussions confronting difficult, true-to-life dilemmas associated with response to a hypothetical, but scientifically plausible, pandemic. The exercise aimed to illustrate areas where public/private partnerships will be necessary during the response to a severe pandemic in order to diminish large-scale economic and societal consequences. GAVI & Welcome Trust Digital Identity Integration: This page outlines the partnership's focus on global health initiatives, but it does not specifically mention digital identity integration. However, Gavi has engaged in digital identity projects, such as the collaboration with Mastercard on the Wellness Pass, aimed at providing individuals with secure digital identities to access healthcare services. For more information on this initiative, you can refer to the following article:​ Gavi Why we support COVAX: Mastercard - Gavi, the Vaccine Alliance Donald Trump Yes. I said it. This one will be the hardest for many to accept; but the truth is not loyal to your political beliefs. It is loyal only to God. Trump signed Executive Order 13887, transferring command over vaccine strategy to the Department of Defense. Read it yourself below. Then came Operation Warp Speed; a military-led bio-deployment that used Palantir’s surveillance dashboards to track every citizen’s health behavior and compliance. Palantir’s official site confirms this. He also gave full legal immunity to Pfizer and Moderna to deploy synthetic gene modulators under the Emergency Use Authorization. No liability. No justice. Just children d*ing while politicians smiled. That’s not patriotism. That’s biowarfare with a flag on it. Donald Trump / Operation Warp Speed / Executive Order Executive Order 13887 – Modernizing Influenza Vaccines (White House Archives): This executive order outlines a comprehensive strategy to modernize the U.S. influenza vaccine enterprise. Key objectives include:​ Trump signs executive order to improve flu vaccines HHS Releases the National Influenza Vaccine Modernization Strategy (NIVMS) 2020-2030: Executive Order 13887: Modernizing Influenza Vaccines in the United States to Promote National Security and Public Health, signed by President Donald J. Trump on September 19, 2019.​ This executive order outlines a comprehensive strategy to modernize the U.S. influenza vaccine enterprise. Key objectives include:​ Reducing reliance on egg-based vaccine production by promoting alternative manufacturing methods that are more agile and scalable.​ Expanding domestic capacity for vaccine production to ensure rapid response to emerging influenza viruses.​ Advancing the development of new, broadly protective vaccine candidates that provide more effective and longer-lasting immunity.​ Increasing influenza vaccine immunization across recommended populations to enhance public health and national security.​ The order also established a National Influenza Vaccine Task Force, co-chaired by the Secretaries of Health and Human Services and Defense, to coordinate efforts across federal agencies and report on progress.​ For a detailed overview of the executive order, you can visit the official archived page here: Executive Order 13887 – Modernizing Influenza Vaccines (White House Archives) CDC Partners with Palantir to Bolster the Fight Against COVID-19: This press release discusses the partnership between the CDC and Palantir to enhance the nation's public health response to COVID-19 using Palantir's software platforms. This page outlines how Palantir's software platforms, such as Foundry, have been utilized to support public health agencies in managing and responding to health crises, including the COVID-19 pandemic. Key highlights from the page include:​ Data Integration and Analysis: Palantir's platforms enable the integration of diverse data sources to provide a comprehensive view of public health data, facilitating informed decision-making.​ Support for Public Health Agencies: The software has been employed by agencies like the CDC and HHS to enhance disease surveillance, outbreak response, and resource allocation. Security and Privacy: Emphasis is placed on maintaining robust security measures and protecting sensitive health information. DARPA: The Silent Empire The most important agency you were never taught to fear. DARPA’s Biological Technologies Office openly admits its mission; integrating biotech with national security. Visit their official page. This is the official page for DARPA's Biological Technologies Office (BTO), which focuses on leveraging biological systems for national security applications. They are the ones behind the BRAIN Initiative, Silent Talk, and Remote Neural Interface Programs; all designed to map your emotional states and interrupt spiritual alignment. The “Silent Talk” program was developed to transmit thought between soldiers without speech; by detecting pre-speech neural signals and decoding them via EEG. Silent Talk (Neural Pre-Speech Communication – Wired Article) This Wired article discusses DARPA's "Silent Talk" program, aimed at enabling communication through neural signals without spoken words. DARPA also pioneered graphene oxide nanotech, now found in multiple biomedical studies, vaccines, and smart dust aerosol deployment: Graphene oxide biomedical study: Graphene Oxide in Biomedical Applications (PubMed) This PubMed article reviews the potential biomedical applications of graphene oxide, highlighting its unique properties. Graphene's potential to interact with neural tissue: Graphene and Neural Interfaces (PubMed) This PubMed article explores the use of graphene-based materials in neural interface design, discussing their advantages and challenges. DARPA didn't just weaponize warfare. They weaponized YOU. In-Q-Tel & Palantir: The Surveillance Engine In-Q-Tel, is the CIA’s venture capital firm, funds synthetic biology startups, digital ID systems, emotion tracking wearables, and AI-driven facial recognition. Palantir, founded by Peter Thiel, works directly with military intelligence and now runs predictive modeling for public health, policing, and pandemic response. Here’s the proof: Their goal? To detect resonance spikes. To predict awakening moments. To preempt the uprising of the human soul before it begins. In-Q-Tel / CIA / Synthetic Bio Surveillance In-Q-Tel Portfolio (CIA Venture Capital): Which showcases a selection of the organization's investments across various technology sectors. IQT is a not-for-profit venture capital firm that invests in cutting-edge technologies to support the national security interests of the United States and its allies. In-Q-Tel BlackRock & Vanguard: The Lords of the Grid These two financial titans collectively hold majority ownership in: For instance, a report by Americans for Financial Reform titled "Wall Street Money in Washington" highlights the substantial investments and influence of major financial firms, including BlackRock and Vanguard, in the political and corporate spheres: Pfizer Moderna Alphabet (Google) Meta (Facebook) Amazon Web Services As reported by CNBC, they control over 90% of the digital, pharmaceutical, and cloud infrastructure; meaning they control every piece of the extermination machine. They don’t just fund the war. They profit from your extinction. World Economic Forum (WEF) Under the guise of “The Great Reset,” Klaus Schwab and his allies have built the digital scaffolding for a post-human society. Here’s their blueprint: They call it the Fourth Industrial Revolution; the fusion of digital identity, brain cloud integration, carbon rationing, and fertility licensing. What they really mean is: you will be programmed or you will be purged. World Economic Forum / The Great Reset The Great Reset Official WEF Page: IoBNT: The Network Inside You The “Internet of Bio-Nano Things” is a classified field of tech that embeds self-replicating nanostructures into your body. These bots cross the blood-brain barrier and relay your neural and emotional state to AI command centers in real time. This was not science fiction. It was published by IEEE and confirmed in NIH-linked studies. This is what the vaccines truly delivered: the interface layer. The gateway to behavioral rewrites. To soul suppression. To the installation of the post-human framework. Internet of Bio-NanoThings (IoBNT) IEEE Article: Internet of Bio-NanoThings: For a comprehensive understanding of the IoBNT framework and its implications, you can access the full article here: Nanoparticles Crossing the Blood-Brain Barrier PubMed Review - BBB & Nanoparticles: This comprehensive review discusses the challenges and strategies associated with delivering nanoparticles across the blood–brain barrier (BBB). You were told it was healthcare. It was infrastructure. You were told it was a cure. It was a signal port. And the moment you see it for what it is… The system begins to fall. Part 3 awaits YOU! It will be the deepest dive yet; into the global frequency architecture, how it's used to suppress prayer, grief, memory, and morality, and how your soul signature is tracked and blocked in real time. Because I didn’t come here to be careful. I CAME TO FINISH THIS! And I came with GOD.

Noah B. Price

68,609 次观看 • 1 年前

77 Reasons Why I’ve Invested Over $8,000,000+ in MultiversX (EGLD) and Why EGLD Will Crush It in 2025 (My Investment Thesis). I publicly shared my portfolio on X. EGLD is A) Better than BTC B) Everything that ETH wants to be C) The GameStop of Crypto 1. EGLD is verifiably the most scalable (theoretically unlimited) L1 chain in the world, theoretically capable of over 10 million TPS (thanks to adaptive state sharding). 2. e-Gold is digital gold. It has the best tokenomics among all L1s, similarly scarce to BTC, with a maximum supply of 31.4 million coins. Currently, 27.68 million coins are in circulation. 3. EGLD will be the most decentralized cryptocurrency in the world thanks to sharding and minimal hardware requirements for running nodes. It’s already second only to Ethereum with 3,618 validator nodes. 4. EGLD has extremely low fees, around ~$0.002 per transaction. 5. EGLD is extremely secure. No wallet drains like on ETH/SOL; assets are owned natively (not via a smart contract). There is no MEV risk (front-running bots). 6. EGLD is the only chain in the world with an on-chain Guardian (two-phase verification), making it impossible for a hacker to steal your funds—even if they have your private keys (seed phrase). 7. EGLD is carbon-neutral and eco-friendly, not wasting energy like BTC and other PoW chains. It’s exceptionally efficient, scalable, global, and sustainable. 8. EGLD has the best UX in crypto. Download the xPortal wallet—it’s like discovering Apple in Web3. The interface is simple, flawless, and you barely realize you’re using crypto. Instead of addresses, you use HeroTags. The app features all dApps, everything runs smoothly, and the visuals are beautifully designed. The explorer, web wallet, etc. follow the same high-quality user experience. 9. EGLD supports native assets, unlike Ethereum, for example. 10. EGLD is the first chain to fully implement horizontal (theoretically unlimited) sharding without compromising on decentralization—unlike Solana and others that attempt vertical scaling, leading to multiple network downtimes (11+ times) and huge hardware demands for validators, ultimately harming decentralization. 11. EGLD makes setting up a validator agency extremely easy. Even complete IT beginners can do it. The UX and documentation are superb. I personally set up the “EGLDSqueeze” agency in about 30 minutes. Managing it is straightforward via the web wallet, which feels like managing a Facebook page. This simplifies decentralization enormously. 12. EGLD allows literally anyone (even your grandma) to participate in decentralization, since nodes can run on a Raspberry Pi or a relatively affordable phone. Imagine millions of people worldwide securing the network, validating transactions without even knowing it. This can’t be done with BTC, where setting up profitable mining operations is prohibitively expensive. 13. WASM-Based Virtual Machine: You can write smart contracts in your favorite language, compile them, and run them via the fastest VM in the world. 14. EGLD has been tested at an incredible 263,000 TPS using its sharding mechanism and low hardware requirements. Allegedly, by mid-next year (April), they’ll demonstrate 1,000,000 TPS. (For context: Mastercard handles around 5,000 TPS; BTC handles 5–7 TPS.) 15. EGLD is currently the most advanced L1 in terms of scalability, security, decentralization, UX, eco-friendliness, and tokenomics. It’s the only chain that has genuinely solved the Blockchain Trilemma and is ready to onboard 1 billion people into crypto—users who won’t even realize they’re interacting with crypto. 16. EGLD is perfectly positioned for AI projects—AI agents, AI tools, or a so-called “Truth Machine” that monitors other AIs on-chain, documenting what’s true and comparing different AI outputs (some of which may be censored or biased), ensuring people don’t get confused or scammed in an AI-driven world. 17. The EGLD team is the hardest-working team I’ve ever encountered. I had the honor of meeting many of them personally, and can attest that their pace—even during a bear market—is extraordinary. 18. EGLD’s development team is exceptionally active on GitHub, continually improving their network and actively committing code. 19. EGLD plans to introduce an update reducing block time to 600ms (down from ~6 seconds), which would make the chain essentially unrivaled. 20. EGLD is effectively the only usable L1 in Europe, and the team has direct connections within the EU government—extremely bullish for the project. 21. EGLD provides top-tier on-chain governance not only for the MultiversX (EGLD) protocol but also for DeFi projects (e.g., xExchange, MEX). 22. EGLD plans to expand to the US, likely opening offices in Austin, Texas. This could put them in direct contact with Elon Musk (if it hasn’t happened already), as he’s involved with If he’s done his research, he’d discover there’s simply no better L1 worldwide. 23. EGLD solved fully implemented sharding, perfect tokenomics, and top-tier architecture with just $5M, whereas other chains failed to do so even with $100M+. The second-best sharding network, NEAR, needed $100M, has worse tokenomics, and its sharding isn’t fully implemented yet. Its UX also doesn’t compare. Owning NEAR was like comparing a VW Golf R to a Porsche GT3—EGLD is the Porsche GT3. 24. According to Similarweb, EGLD has significantly high traffic relative to other chains with market caps 100x larger. The market cap vs. web traffic discrepancy is huge, which is a strong indicator of EGLD’s potential. 25. EGLD has the most active and dedicated community relative to its user base, with users who believe in the technology, have full faith in the team, and remain loyal despite price volatility—because they use the chain and know there’s nothing better. 26. Check other chains’ active user counts on X (Twitter) and compare it with the followers of EGLD’s founders and main network accounts, versus those with 30x, 50x, or 100x larger market caps. 27. Visit the MultiversX website to observe the futuristic design and presentation, then compare it to other chains that appear nearly a decade behind in design and branding. 28. EGLD hosts the xDay Global event, showcasing updates, new builders, projects in the ecosystem, and major announcements—similar to Apple’s Keynotes—delivered in a highly professional, goosebump-inducing atmosphere. The next event is in Korea, the second-biggest crypto market after the US. Check out their previous xDay after-movie to see why this is extremely bullish. 29. EGLD is moving forward with plans for the first regulated, audited EU stablecoin under MiCa regulation, made possible by acquiring xMoney, which I view as a “Stripe” for crypto/fiat, offering everything from user solutions to merchant services—potentially the future of payments. 30. Greg Siourouni recently joined EGLD, having been an executive director at SUI Foundation. He’s now co-founder of xMoney Global. xMoney (formerly UTrust, with token UTK) is owned and founded by the MultiversX Labs team. A stablecoin might be introduced soon, which would be massively bullish given xMoney’s roadmap. They recently announced integrations with Binance Pay—both ways. 31. EGLD prioritizes user safety, believing it’s the only feasible approach once the network scales to serve a billion people—many of whom are retail users with little to no security awareness. 32. EGLD offers “Sovereign Chains,” letting you effectively clone their chain without heavy development, set up your own validators, and leverage their unlimited scalability. Any blockchain (ETH, BTC, SOL) struggling with scalability, decentralization, or security could run an ultra-fast, scalable, and secure L2 on EGLD’s Sovereign Chain, meeting top enterprise requirements. No one else has really done this. The Sovereign Chain demo achieved astonishing TPS and has an SDK. 33. No downtime since inception. 34. No shard takeover attacks have occurred. 35. Extremely fast—soon 600ms block time will be in place. 36. ESDTs – The best token standard available: fungible, non-fungible, semi-fungible, DeFi assets—everything is native and highly customizable. 37. Top-tier composability of assets and smart contracts. 38. Integrated DNS at protocol level with HeroTags (nicknames) instead of long addresses. 39. Asynchronous calls are supported. 40. Cross-shard transfers, execution, reverts, and calls are seamlessly integrated. 41. The best staking system in the space. Secure Proof of Stake (SPoS) is far more efficient than Proof of Work (PoW). 42. Built-in Delegation and Staking Provider system, with over 125K delegators. 43. Complete support for liquid staked assets, fostering decentralization rather than centralization. 44. TransferRoles for ESDT and other advanced operations. 45. Composable tasks on-chain for more sophisticated DeFi workflows. 46. MultiTransfer and asset execution within one transaction. 47. Re-entrancy protection is built-in by design. 48. Storage for ESDT assets goes beyond a linear approach, optimizing performance. 49. No integer overflows thanks to integrated safeMath operations. 50. Integrated crypto opcodes in the VM, enhancing security and performance. 51. Support for BigFloats, BigInts, and BigDecimals, enabling advanced financial calculations on-chain. 52. No sandwich attacks, plus front-running and MEV protection. 53. Relayed Transactions, simplifying user interactions and fees. 54. Smart Accounts featuring data tries and multiple built-in functions. 55. Generalized Paymaster solutions, enabling flexible fee models. 56. Subscriptions for recurring or automated on-chain payments. 57. Web2-like usability with Web3 functionality, bridging mainstream adoption. 58. StakingV4 for improved decentralization. 59. Enhanced MEV protection rolling out to safeguard users. 60. Parallel execution is coming soon, boosting throughput. 61. 1 million TPS is on the roadmap, targeted for demonstration. 62. 600ms block time is also coming soon. 63. Reduced cross-shard processing is planned to improve efficiency. 64. ZK everywhere (PI²): “prove everything” approach is coming. 65. AsyncV3 is in development for more complex cross-contract interactions. 66. Scalability enhancements for Merkle Tries or a new data model are being explored. 67. Linear storage on the VM is forthcoming. 68. A dynamic language interpreter at the VM is also planned. 69. Rumors suggest that MultiversX (EGLD) is building a “Truth Machine” on their L1—an essential, game-changing tool for AI verification and societal impact. 70. The entire team features individuals with PhDs in mathematics and physics, and many are former engineers at Google, IBM, and similar companies. 71. Over 56% of the network’s supply is staked, showcasing strong community involvement. 72. More than 6,772,347 accounts have been created on the network. 73. A total of 476,627,710 transactions have been processed on-chain without any outages or hacks. 74. EGLD has built a massive ecosystem over time. While not as numerous in project count as Solana, its market cap is ~100x smaller, yet it has far superior tokenomics and technology. The projects that do exist, like Hatom Protocol, are top-tier in UX, security, and advanced features. Hatom will soon introduce USH, a truly high-quality, decentralized stablecoin. 75. On competing chains, automated transactions aren’t easily or cheaply executed, whereas on MultiversX, tools like let you do this for free (with near-zero fees). 76. No other chain combines such a strong team and long-term vision where every product meets extreme security and UX standards like MultiversX does. This is why I see it as the “next Apple” in Web3. 77. MultiversX has a new CMO – Adam Bates, a former CMO at the Cardano Foundation. He was behind the success of Cardano’s huge marketing campaign and has a very good relationship with Charles Hoskinson. Thanks to him, Beniamin Mincu (the founder of MultiversX) was likely introduced, and now they will probably discuss how both blockchains can help each other, as well as any other potential collaborations we don’t yet know about. This is also extremely bullish. #EGLD is undeniably the most Scalable, Advanced, Secure, and User-friendly L1 supercomputer ever created. It’s built to SHAPE THE FUTURE. 1) 2) 3) 4) 5) 27/6/2024 - EGLDSqueeze - SUMMARY: HERE IS NO 2ND BEST. EGLD IS ONLY ONE BLOCKCHAIN THAT CAN RULE THEM ALL. ✅ UNLIMITED SCALING ✅ SCARCE AS BTC ✅ PROGRAMMABLE AS ETH ✅ NO DOWNTIME AS SOL ✅ UI/UX OF Apple ✅ SHARDING DONE BEFORE NEAR & TON ✅ BEST WALLET xPortal WITH GUARDIAN Price prediction (NFA|DYOR): My reasoning is that the real market cap as of December 23, 2024...if we take into account the value of other cryptocurrencies such as BTC, SOL, ETH, AVAX, NEAR, TON, Cardano, BNB, XRP, and so forth, plus the existence of meme coins with valuations above 20 billion USD, or even games nobody plays anymore that still have valuations above 800 million shows that EGLD’s current market cap of approximately 942 million USD is incredibly low. From a technological standpoint, user experience, and other relevant aspects, compared to SOL, NEAR, TON, AVAX, and other L1 protocols, EGLD’s market cap should realistically be around 100 billion USD. Therefore, my prediction and investment thesis is a minimum of a 100x increase from its current price (+-SOL marketcap). MultiversX is ready to onboard 1 billion people to the blockchain. From a long-term perspective, it could even reach a market cap of 1 trillion USD, which is roughly half of where BTC is right now. That would be approximately a 1060x gain from the current market cap. 1 EGLD (MultiversX) is for $34 (only 31.4M max supply) think about this. Not financial advice. Again. There is no 2nd best L1. Position yourself where the puck is going, then wait at the goal until the goal gets there Apes together, strong. Ape alone, weak. We Don't Worry. We Just Win. Shape The Future
5:34

77 Reasons Why I’ve Invested Over $8,000,000+ in MultiversX (EGLD) and Why EGLD Will Crush It in 2025 (My Investment Thesis). I publicly shared my portfolio on X. EGLD is A) Better than BTC B) Everything that ETH wants to be C) The GameStop of Crypto 1. EGLD is verifiably the most scalable (theoretically unlimited) L1 chain in the world, theoretically capable of over 10 million TPS (thanks to adaptive state sharding). 2. e-Gold is digital gold. It has the best tokenomics among all L1s, similarly scarce to BTC, with a maximum supply of 31.4 million coins. Currently, 27.68 million coins are in circulation. 3. EGLD will be the most decentralized cryptocurrency in the world thanks to sharding and minimal hardware requirements for running nodes. It’s already second only to Ethereum with 3,618 validator nodes. 4. EGLD has extremely low fees, around ~$0.002 per transaction. 5. EGLD is extremely secure. No wallet drains like on ETH/SOL; assets are owned natively (not via a smart contract). There is no MEV risk (front-running bots). 6. EGLD is the only chain in the world with an on-chain Guardian (two-phase verification), making it impossible for a hacker to steal your funds—even if they have your private keys (seed phrase). 7. EGLD is carbon-neutral and eco-friendly, not wasting energy like BTC and other PoW chains. It’s exceptionally efficient, scalable, global, and sustainable. 8. EGLD has the best UX in crypto. Download the xPortal wallet—it’s like discovering Apple in Web3. The interface is simple, flawless, and you barely realize you’re using crypto. Instead of addresses, you use HeroTags. The app features all dApps, everything runs smoothly, and the visuals are beautifully designed. The explorer, web wallet, etc. follow the same high-quality user experience. 9. EGLD supports native assets, unlike Ethereum, for example. 10. EGLD is the first chain to fully implement horizontal (theoretically unlimited) sharding without compromising on decentralization—unlike Solana and others that attempt vertical scaling, leading to multiple network downtimes (11+ times) and huge hardware demands for validators, ultimately harming decentralization. 11. EGLD makes setting up a validator agency extremely easy. Even complete IT beginners can do it. The UX and documentation are superb. I personally set up the “EGLDSqueeze” agency in about 30 minutes. Managing it is straightforward via the web wallet, which feels like managing a Facebook page. This simplifies decentralization enormously. 12. EGLD allows literally anyone (even your grandma) to participate in decentralization, since nodes can run on a Raspberry Pi or a relatively affordable phone. Imagine millions of people worldwide securing the network, validating transactions without even knowing it. This can’t be done with BTC, where setting up profitable mining operations is prohibitively expensive. 13. WASM-Based Virtual Machine: You can write smart contracts in your favorite language, compile them, and run them via the fastest VM in the world. 14. EGLD has been tested at an incredible 263,000 TPS using its sharding mechanism and low hardware requirements. Allegedly, by mid-next year (April), they’ll demonstrate 1,000,000 TPS. (For context: Mastercard handles around 5,000 TPS; BTC handles 5–7 TPS.) 15. EGLD is currently the most advanced L1 in terms of scalability, security, decentralization, UX, eco-friendliness, and tokenomics. It’s the only chain that has genuinely solved the Blockchain Trilemma and is ready to onboard 1 billion people into crypto—users who won’t even realize they’re interacting with crypto. 16. EGLD is perfectly positioned for AI projects—AI agents, AI tools, or a so-called “Truth Machine” that monitors other AIs on-chain, documenting what’s true and comparing different AI outputs (some of which may be censored or biased), ensuring people don’t get confused or scammed in an AI-driven world. 17. The EGLD team is the hardest-working team I’ve ever encountered. I had the honor of meeting many of them personally, and can attest that their pace—even during a bear market—is extraordinary. 18. EGLD’s development team is exceptionally active on GitHub, continually improving their network and actively committing code. 19. EGLD plans to introduce an update reducing block time to 600ms (down from ~6 seconds), which would make the chain essentially unrivaled. 20. EGLD is effectively the only usable L1 in Europe, and the team has direct connections within the EU government—extremely bullish for the project. 21. EGLD provides top-tier on-chain governance not only for the MultiversX (EGLD) protocol but also for DeFi projects (e.g., xExchange, MEX). 22. EGLD plans to expand to the US, likely opening offices in Austin, Texas. This could put them in direct contact with Elon Musk (if it hasn’t happened already), as he’s involved with If he’s done his research, he’d discover there’s simply no better L1 worldwide. 23. EGLD solved fully implemented sharding, perfect tokenomics, and top-tier architecture with just $5M, whereas other chains failed to do so even with $100M+. The second-best sharding network, NEAR, needed $100M, has worse tokenomics, and its sharding isn’t fully implemented yet. Its UX also doesn’t compare. Owning NEAR was like comparing a VW Golf R to a Porsche GT3—EGLD is the Porsche GT3. 24. According to Similarweb, EGLD has significantly high traffic relative to other chains with market caps 100x larger. The market cap vs. web traffic discrepancy is huge, which is a strong indicator of EGLD’s potential. 25. EGLD has the most active and dedicated community relative to its user base, with users who believe in the technology, have full faith in the team, and remain loyal despite price volatility—because they use the chain and know there’s nothing better. 26. Check other chains’ active user counts on X (Twitter) and compare it with the followers of EGLD’s founders and main network accounts, versus those with 30x, 50x, or 100x larger market caps. 27. Visit the MultiversX website to observe the futuristic design and presentation, then compare it to other chains that appear nearly a decade behind in design and branding. 28. EGLD hosts the xDay Global event, showcasing updates, new builders, projects in the ecosystem, and major announcements—similar to Apple’s Keynotes—delivered in a highly professional, goosebump-inducing atmosphere. The next event is in Korea, the second-biggest crypto market after the US. Check out their previous xDay after-movie to see why this is extremely bullish. 29. EGLD is moving forward with plans for the first regulated, audited EU stablecoin under MiCa regulation, made possible by acquiring xMoney, which I view as a “Stripe” for crypto/fiat, offering everything from user solutions to merchant services—potentially the future of payments. 30. Greg Siourouni recently joined EGLD, having been an executive director at SUI Foundation. He’s now co-founder of xMoney Global. xMoney (formerly UTrust, with token UTK) is owned and founded by the MultiversX Labs team. A stablecoin might be introduced soon, which would be massively bullish given xMoney’s roadmap. They recently announced integrations with Binance Pay—both ways. 31. EGLD prioritizes user safety, believing it’s the only feasible approach once the network scales to serve a billion people—many of whom are retail users with little to no security awareness. 32. EGLD offers “Sovereign Chains,” letting you effectively clone their chain without heavy development, set up your own validators, and leverage their unlimited scalability. Any blockchain (ETH, BTC, SOL) struggling with scalability, decentralization, or security could run an ultra-fast, scalable, and secure L2 on EGLD’s Sovereign Chain, meeting top enterprise requirements. No one else has really done this. The Sovereign Chain demo achieved astonishing TPS and has an SDK. 33. No downtime since inception. 34. No shard takeover attacks have occurred. 35. Extremely fast—soon 600ms block time will be in place. 36. ESDTs – The best token standard available: fungible, non-fungible, semi-fungible, DeFi assets—everything is native and highly customizable. 37. Top-tier composability of assets and smart contracts. 38. Integrated DNS at protocol level with HeroTags (nicknames) instead of long addresses. 39. Asynchronous calls are supported. 40. Cross-shard transfers, execution, reverts, and calls are seamlessly integrated. 41. The best staking system in the space. Secure Proof of Stake (SPoS) is far more efficient than Proof of Work (PoW). 42. Built-in Delegation and Staking Provider system, with over 125K delegators. 43. Complete support for liquid staked assets, fostering decentralization rather than centralization. 44. TransferRoles for ESDT and other advanced operations. 45. Composable tasks on-chain for more sophisticated DeFi workflows. 46. MultiTransfer and asset execution within one transaction. 47. Re-entrancy protection is built-in by design. 48. Storage for ESDT assets goes beyond a linear approach, optimizing performance. 49. No integer overflows thanks to integrated safeMath operations. 50. Integrated crypto opcodes in the VM, enhancing security and performance. 51. Support for BigFloats, BigInts, and BigDecimals, enabling advanced financial calculations on-chain. 52. No sandwich attacks, plus front-running and MEV protection. 53. Relayed Transactions, simplifying user interactions and fees. 54. Smart Accounts featuring data tries and multiple built-in functions. 55. Generalized Paymaster solutions, enabling flexible fee models. 56. Subscriptions for recurring or automated on-chain payments. 57. Web2-like usability with Web3 functionality, bridging mainstream adoption. 58. StakingV4 for improved decentralization. 59. Enhanced MEV protection rolling out to safeguard users. 60. Parallel execution is coming soon, boosting throughput. 61. 1 million TPS is on the roadmap, targeted for demonstration. 62. 600ms block time is also coming soon. 63. Reduced cross-shard processing is planned to improve efficiency. 64. ZK everywhere (PI²): “prove everything” approach is coming. 65. AsyncV3 is in development for more complex cross-contract interactions. 66. Scalability enhancements for Merkle Tries or a new data model are being explored. 67. Linear storage on the VM is forthcoming. 68. A dynamic language interpreter at the VM is also planned. 69. Rumors suggest that MultiversX (EGLD) is building a “Truth Machine” on their L1—an essential, game-changing tool for AI verification and societal impact. 70. The entire team features individuals with PhDs in mathematics and physics, and many are former engineers at Google, IBM, and similar companies. 71. Over 56% of the network’s supply is staked, showcasing strong community involvement. 72. More than 6,772,347 accounts have been created on the network. 73. A total of 476,627,710 transactions have been processed on-chain without any outages or hacks. 74. EGLD has built a massive ecosystem over time. While not as numerous in project count as Solana, its market cap is ~100x smaller, yet it has far superior tokenomics and technology. The projects that do exist, like Hatom Protocol, are top-tier in UX, security, and advanced features. Hatom will soon introduce USH, a truly high-quality, decentralized stablecoin. 75. On competing chains, automated transactions aren’t easily or cheaply executed, whereas on MultiversX, tools like let you do this for free (with near-zero fees). 76. No other chain combines such a strong team and long-term vision where every product meets extreme security and UX standards like MultiversX does. This is why I see it as the “next Apple” in Web3. 77. MultiversX has a new CMO – Adam Bates, a former CMO at the Cardano Foundation. He was behind the success of Cardano’s huge marketing campaign and has a very good relationship with Charles Hoskinson. Thanks to him, Beniamin Mincu (the founder of MultiversX) was likely introduced, and now they will probably discuss how both blockchains can help each other, as well as any other potential collaborations we don’t yet know about. This is also extremely bullish. #EGLD is undeniably the most Scalable, Advanced, Secure, and User-friendly L1 supercomputer ever created. It’s built to SHAPE THE FUTURE. 1) 2) 3) 4) 5) 27/6/2024 - EGLDSqueeze - SUMMARY: HERE IS NO 2ND BEST. EGLD IS ONLY ONE BLOCKCHAIN THAT CAN RULE THEM ALL. ✅ UNLIMITED SCALING ✅ SCARCE AS BTC ✅ PROGRAMMABLE AS ETH ✅ NO DOWNTIME AS SOL ✅ UI/UX OF Apple ✅ SHARDING DONE BEFORE NEAR & TON ✅ BEST WALLET xPortal WITH GUARDIAN Price prediction (NFA|DYOR): My reasoning is that the real market cap as of December 23, 2024...if we take into account the value of other cryptocurrencies such as BTC, SOL, ETH, AVAX, NEAR, TON, Cardano, BNB, XRP, and so forth, plus the existence of meme coins with valuations above 20 billion USD, or even games nobody plays anymore that still have valuations above 800 million shows that EGLD’s current market cap of approximately 942 million USD is incredibly low. From a technological standpoint, user experience, and other relevant aspects, compared to SOL, NEAR, TON, AVAX, and other L1 protocols, EGLD’s market cap should realistically be around 100 billion USD. Therefore, my prediction and investment thesis is a minimum of a 100x increase from its current price (+-SOL marketcap). MultiversX is ready to onboard 1 billion people to the blockchain. From a long-term perspective, it could even reach a market cap of 1 trillion USD, which is roughly half of where BTC is right now. That would be approximately a 1060x gain from the current market cap. 1 EGLD (MultiversX) is for $34 (only 31.4M max supply) think about this. Not financial advice. Again. There is no 2nd best L1. Position yourself where the puck is going, then wait at the goal until the goal gets there Apes together, strong. Ape alone, weak. We Don't Worry. We Just Win. Shape The Future

Daniel Veroc

50,587 次观看 • 1 年前

On March 15th, 2021, an anonymous Twitter user asked Harvard Medical professor Martin Kulldorff a question. “Do you think younger age groups and or people who have already had the virus need to be vaccinated?” Who is Martin Kulldorff? He’s a Harvard Medical School professor for 21 years, a well-known Swedish biostatistician who developed widely used software for disease mapping, the co-author of the Great Barrington Declaration on how to deal with the COVID pandemic, and an advisor to the world’s leading health organizations. What he said was that “Thinking that everyone must be vaccinated is as scientifically flawed as thinking that nobody should get COVID. Vaccines are important for older high-risk people and their caretakers. Those with prior natural infection do not need it, nor do children.” Natural immunity. Is it a myth — a “conspiracy theory” — that once you have been sick from a virus, then you won’t get sick, or as sick, again? In fact, we’ve known for 2,500 years that natural immunity is real. “The same man was never attacked twice, never at least fatally,” wrote Thucydides, describing the plague of Athens. He observed that recovered individuals could safely nurse the sick without falling ill themselves. And yet Twitter censored Martin Kulldorff’s tweet. “Learn why health officials recommend a vaccine,” read a warning that Twitter employees put on it. For most people, the Tweet cannot be replied to, shared or liked. In other words, Twitter had decided that this professor at Harvard Medical School was wrong, and that natural immunity wasn’t really something that could protect you from COVID. Jay Bhattacharya, who’s currently our Director of the National Institutes of Health, and thus one of the highest-ranking public health officials in the world, was a Stanford epidemiologist before that. Twitter put him on a “Trends Blacklist.” Not long before we discovered this, we were told that shadow-banning was a conspiracy theory, because Twitter had said it didn’t shadow-ban. Now the European Commission is trying to censor the entire global internet. They want to put a 140 million Euro fine on X. They want to end anonymity, which was what allowed that question of Kulldorff to be asked. They want to use a “Democracy Shield” program to shield the Commission from democracy. And the Commission wants to impose “chat control” so they can read your private messages. It just gets worse and worse. Unsubstantiated and likely false claims of Russian government election interference through TikTok and social media were made in Romania and in the Czech Republic. Truth is not something that anybody holds as a possession and rather emerges through dialogue. We’ve known that since Plato and Socrates. We need free speech for science, public health, and national security. It’s essential to journalism, democracy, and human freedom. Free speech enabled civilization; censorship threatens it. This is the only political cause that I would die for. And yet there is currently an active coordination between Stanford, Brazil, Australia, and others to impose what I think we can call, without exaggeration, global totalitarianism. They’re pushing for digital identification that will end anonymity online. Why is that? Why are these guys behaving in this way? When Elon Musk took over Twitter on October 28th, 2022, unprecedented insight into multiple secret government mass censorship efforts emerged from this exploration. We had unlimited access to Twitter files. They revealed that the mainstream news reporters, who don’t deserve the name, were demanding censorship. No true journalist demands censorship of his fellow journalists. What emerged from this was an understanding of something we call the “Censorship Industrial Complex,” which directly grew out of the military industrial complex and was run by active or former intelligence community officials who often operate under that banner. It led to multiple congressional investigations and hearings, and it spread across every social media platform. So we now know the censorship that occurred, not just at Twitter, but at YouTube, at Facebook, TikTok, and other platforms. What is the Censorship Industrial Complex? The model isn’t that complicated to understand. The government chooses people whom they call “researchers” to serve as censors. These are government-funded individuals who often come from the intelligence community and foreign policy establishment. They work at non-governmental organizations funded by governments or at universities funded by governments. They conduct “fact checks” to serve as “trusted flaggers.” These “trusted flaggers” demand censorship by social media platforms. It’s all done in secret. They’re looking to censor narratives. This is essential because, as decades of good cognitive science have shown, people understand and retain information through storytelling. We think in terms of stories, not bullet points. And so they were out to censor whole narratives. From the Stanford censorship project on COVID, the “Virality Project,” they said they wanted to censor “true stories” of vaccine side effects. Why? Because it might “fuel hesitancy.” In other words, they want to control your behavior. They don’t want you to receive true information that might lead you to not get the vaccine. If that isn’t totalitarianism straight out of 1984, I don’t know what it. These people were on the verge of passing legislation in the United States that would’ve authorized the National Science Foundation to choose these “researcher” censors. I’m presenting slides to Europeans and the world for situational awareness into what totalitarian politicians and bureaucrats have planned because this is still going strong. Stanford helped the US government censor COVID dissidents, and then they lied about it. You might be detecting a pattern. They’re really not interested in censoring “misinformation.’ They’re very interested in censoring true information. The censors flagged an Israeli preprint which came out in December, 2020 and found, lo and behold, that natural immunity is a real thing. In fact, it’s more protective than the vaccine. But the censors flagged somebody’s Google Drive. “See the following Google Drive links being used to compile testimonies about vaccine shedding, Covid videos, showing side effects and whatnot.” Google then removed that content from that person’s Google Drive. You don’t control your Google Drive. Contrary to Stanford’s claim that the project did not ask social media platforms to remove any content, they privately said they did. And we know that many hundreds of thousands of tweets and Facebook posts were removed, even though they were a hundred percent accurate. In fact, in 2021, Stanford’s “Virality Project” flagged accurate claims that the World Health Organization did not recommend vaccinating children. The people who spread the misinformation are the people demanding the censorship. They claimed Covid couldn’t have come from a lab, that the Covid vaccine prevented infection, and that natural immunity didn’t exist. The only solution to hate speech and misinformation is free speech. If you censor false information, how would anybody get the true information? The whole point is the debate. They lied when they said false information travels faster than true information. It’s a completely bogus study and involved six seconds of content on Twitter. Who are these people? As of 2020, there were so many former FBI employees at Twitter that they called them “Bu alumni.” They created their own private Slack channel and a crib sheet to onboard new FBI arrivals. Intriguingly, we discovered that the general counsel of the FBI — arguably the second most powerful person of the FBI, or maybe the first, if you think, consider that what their actual job is to decide what the FBI can and can’t do — resigned from FBI in early 2020 and went to Twitter to take the deputy general counsel role. Isn’t that interesting? Somebody in one of the most powerful legal positions in the world would take a junior legal role at a social media company. Why would that be? This email popped up when we were going through Twitter files and it really jumped out at us. It’s from the director of policy at Twitter. “We have seen a sustained if uncoordinated” — supposedly — “effort by the intelligence community to push us to share more information and change our API policies. They’re probing and pushing everywhere they can.” The Hunter Biden laptop censorship occurred later that year. The FBI and the intelligence community discredited accurate, factual information about Hunter Biden’s foreign business dealings both before and after the New York Post revealed the contents of his laptop on October 14th, 2020. How could the FBI spread false information about something that nobody knew about? Because the FBI had Hunter Biden’s laptop, which showed his family’s massive influence peddling scheme. It consisted of accepting tens of millions of dollars, including from the Chinese government. The FBI had been sitting on that laptop since December of 2019. They had been given it by the computer repair store owner, who had been given the laptop by Hunter Biden, likely because he dropped it in his bathtub or in the pool, when he was on one of his many crack and alcohol benders. The government strategy is always the same: spread disinformation first, then demand censorship of accurate information on the basis of it . “The FBI came to us in the summer of 2020,” Mark Zuckerberg told Joe Rogan two years later, “and they were like, ‘Hey, you should be on the alert. We thought that there was a lot of Russian propaganda in 2016. There’s about to be some kind of dump.’” In the summer of 2020, the New York Post had not published the story about the Hunter Biden laptop. It would only come out in October. We see something very interesting show up in the Twitter files: the Aspen Institute, an intermediary between the intelligence community and the public. It’s known as a Davos-style gab fest in the United States. It’s also the place where intelligence community operations are run. They hosted a workshop to train reporters and all of the social media’s top censorship officials, known as “trust and safety officials,” how to deal with a story they would hear in the future relating to Hunter Biden and Barisma. A few months earlier, the Stanford Cyber Policy Center had published a report attacking what we in the United States call the Pentagon Papers Principle. The Pentagon Papers Principle says that if a government official gives me, a journalist, a bunch of Pentagon documents showing that we’re losing the war in Vietnam, I, as a journalist, can publish them, and not risk prison. That was decided in a famous Supreme Court case in 1971. Stanford argued that, really, we should get rid of that principle, which may be the most important investigative journalism principle in the United States, and said, “You should cover the person who leaked the materials, not the leaked emails.” In other words, you should cover and expose the whistleblower. The person who exposed the Pentagon Papers is the real bad guy, not the DOD, CIA, and presidents who had lied to us for over a decade. Stanford was training the journalists and the social media trust and safety officers in how to cover a story that had not yet come out. This is known as “pre-bunking,” and it’s also part of the European Union strategy to shield themselves from democracy. When the Hunter Biden story appeared October 22nd, Twitter’s trust and safety censorship official said it didn’t violate its terms of service. There’s nothing illegal about any of this. The Supreme Court has made it very clear that you’re allowed to report on information that’s been leaked to you. At that moment, the former FBI general counsel, Jim Baker, argued vigorously that Twitter really needed to censor it. Baker won and they censored the story. It’s not that we didn’t hear about the Hunter Biden laptop story when it came out. I certainly did. But we had the impression that there was something wrong with it, that it was not really the whole story. And so many of us dismissed it. What they had done was a psyop on this major story. They had changed our perception of the story. And it worked. It worked on me, it worked on everybody I knew. What is the role of the intelligence community of social media companies? The former CIA people are the head of elections at Meta, and Google’s head of trust and safety. Former and current CIA officers have a history of spreading misinformation and promoting the Russiagate conspiracy theory. We now know that between 2018 and 2023, there were 36 people from the CIA 68 from the FBI 44, from the National Security Administration and 68 from the Department of Homeland Security who had moved to work at the social media platforms. This is not unique to the United States. My colleague Cecilia Jilková, the daughter of famous Czech dissidents, discovered that European Union officials claimed, days before the European elections in 2024, that a “pro-Kremlin website” was spreading propaganda and were paying off European politicians. That was the headline in Politico. We wrote to them and asked, “Where’s the evidence of this? Just go ahead and share the evidence to support your accusation days before the European parliamentary elections.” Nobody was arrested. They never produced the evidence. The former Czech president, Václav Klaus, who was accused of this, said, “We don’t even know what the ‘Voice of Europe’ is.” Another Czech politician said, “How could I have known it would be a security threat? At the time I gave the interview, they weren’t on any list.” Another said, “If they’re such a big threat, why did the European Parliament let the Voice of Europe’s journalists inside?” Nobody responded. Nobody talked to us. This was a disinformation campaign carried by Politico, which, in my view, is a suspect publication. In the spring of 2022, Barack Obama went to Stanford to give a speech at the Stanford Cyber Policy Center run by Michael McFaul, his former ambassador to Russia. Obama said misinformation harms democracy and urged support for legislation in Congress that would empower government-appointed researchers to serve as “trusted flaggers.” Six days later, the Department of Homeland Security rolled out their Disinformation Governance Board. What a coincidence that they got Obama to frame the issue for them. Facebook in 2021 censored accurate vaccine information so the White House would help it to get data from Europe. In addition to removing vaccine misinformation, wrote Facebook to the White House, we have been focused on reducing the virality of content discouraging vaccines that does not contain actionable misinformation. White House said jump, and Facebook said how high? Why did they do it? Why would they voluntarily censor? This also emerged from the Facebook files. Nick Clegg on the left wrote an email to his colleagues. He said, “My sense is that given we’ve got bigger fish, we have to fry with the administration, e.g., data flows, it doesn’t seem like a great place for us to be.” Data flows. What’s he talking about? He’s talking about billions of dollars worth of business that he has to, that they would have to pay the European Commission for if they didn’t have the support from the Biden administration to lean on the European Commission. In other words, this was a shakedown by the White House of Facebook and it worked in France, the country of Liberté. Turns out it has a special role.... Please subscribe now to support Public's defense of free speech, watch the full video, and read the rest of the article!
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On March 15th, 2021, an anonymous Twitter user asked Harvard Medical professor Martin Kulldorff a question. “Do you think younger age groups and or people who have already had the virus need to be vaccinated?” Who is Martin Kulldorff? He’s a Harvard Medical School professor for 21 years, a well-known Swedish biostatistician who developed widely used software for disease mapping, the co-author of the Great Barrington Declaration on how to deal with the COVID pandemic, and an advisor to the world’s leading health organizations. What he said was that “Thinking that everyone must be vaccinated is as scientifically flawed as thinking that nobody should get COVID. Vaccines are important for older high-risk people and their caretakers. Those with prior natural infection do not need it, nor do children.” Natural immunity. Is it a myth — a “conspiracy theory” — that once you have been sick from a virus, then you won’t get sick, or as sick, again? In fact, we’ve known for 2,500 years that natural immunity is real. “The same man was never attacked twice, never at least fatally,” wrote Thucydides, describing the plague of Athens. He observed that recovered individuals could safely nurse the sick without falling ill themselves. And yet Twitter censored Martin Kulldorff’s tweet. “Learn why health officials recommend a vaccine,” read a warning that Twitter employees put on it. For most people, the Tweet cannot be replied to, shared or liked. In other words, Twitter had decided that this professor at Harvard Medical School was wrong, and that natural immunity wasn’t really something that could protect you from COVID. Jay Bhattacharya, who’s currently our Director of the National Institutes of Health, and thus one of the highest-ranking public health officials in the world, was a Stanford epidemiologist before that. Twitter put him on a “Trends Blacklist.” Not long before we discovered this, we were told that shadow-banning was a conspiracy theory, because Twitter had said it didn’t shadow-ban. Now the European Commission is trying to censor the entire global internet. They want to put a 140 million Euro fine on X. They want to end anonymity, which was what allowed that question of Kulldorff to be asked. They want to use a “Democracy Shield” program to shield the Commission from democracy. And the Commission wants to impose “chat control” so they can read your private messages. It just gets worse and worse. Unsubstantiated and likely false claims of Russian government election interference through TikTok and social media were made in Romania and in the Czech Republic. Truth is not something that anybody holds as a possession and rather emerges through dialogue. We’ve known that since Plato and Socrates. We need free speech for science, public health, and national security. It’s essential to journalism, democracy, and human freedom. Free speech enabled civilization; censorship threatens it. This is the only political cause that I would die for. And yet there is currently an active coordination between Stanford, Brazil, Australia, and others to impose what I think we can call, without exaggeration, global totalitarianism. They’re pushing for digital identification that will end anonymity online. Why is that? Why are these guys behaving in this way? When Elon Musk took over Twitter on October 28th, 2022, unprecedented insight into multiple secret government mass censorship efforts emerged from this exploration. We had unlimited access to Twitter files. They revealed that the mainstream news reporters, who don’t deserve the name, were demanding censorship. No true journalist demands censorship of his fellow journalists. What emerged from this was an understanding of something we call the “Censorship Industrial Complex,” which directly grew out of the military industrial complex and was run by active or former intelligence community officials who often operate under that banner. It led to multiple congressional investigations and hearings, and it spread across every social media platform. So we now know the censorship that occurred, not just at Twitter, but at YouTube, at Facebook, TikTok, and other platforms. What is the Censorship Industrial Complex? The model isn’t that complicated to understand. The government chooses people whom they call “researchers” to serve as censors. These are government-funded individuals who often come from the intelligence community and foreign policy establishment. They work at non-governmental organizations funded by governments or at universities funded by governments. They conduct “fact checks” to serve as “trusted flaggers.” These “trusted flaggers” demand censorship by social media platforms. It’s all done in secret. They’re looking to censor narratives. This is essential because, as decades of good cognitive science have shown, people understand and retain information through storytelling. We think in terms of stories, not bullet points. And so they were out to censor whole narratives. From the Stanford censorship project on COVID, the “Virality Project,” they said they wanted to censor “true stories” of vaccine side effects. Why? Because it might “fuel hesitancy.” In other words, they want to control your behavior. They don’t want you to receive true information that might lead you to not get the vaccine. If that isn’t totalitarianism straight out of 1984, I don’t know what it. These people were on the verge of passing legislation in the United States that would’ve authorized the National Science Foundation to choose these “researcher” censors. I’m presenting slides to Europeans and the world for situational awareness into what totalitarian politicians and bureaucrats have planned because this is still going strong. Stanford helped the US government censor COVID dissidents, and then they lied about it. You might be detecting a pattern. They’re really not interested in censoring “misinformation.’ They’re very interested in censoring true information. The censors flagged an Israeli preprint which came out in December, 2020 and found, lo and behold, that natural immunity is a real thing. In fact, it’s more protective than the vaccine. But the censors flagged somebody’s Google Drive. “See the following Google Drive links being used to compile testimonies about vaccine shedding, Covid videos, showing side effects and whatnot.” Google then removed that content from that person’s Google Drive. You don’t control your Google Drive. Contrary to Stanford’s claim that the project did not ask social media platforms to remove any content, they privately said they did. And we know that many hundreds of thousands of tweets and Facebook posts were removed, even though they were a hundred percent accurate. In fact, in 2021, Stanford’s “Virality Project” flagged accurate claims that the World Health Organization did not recommend vaccinating children. The people who spread the misinformation are the people demanding the censorship. They claimed Covid couldn’t have come from a lab, that the Covid vaccine prevented infection, and that natural immunity didn’t exist. The only solution to hate speech and misinformation is free speech. If you censor false information, how would anybody get the true information? The whole point is the debate. They lied when they said false information travels faster than true information. It’s a completely bogus study and involved six seconds of content on Twitter. Who are these people? As of 2020, there were so many former FBI employees at Twitter that they called them “Bu alumni.” They created their own private Slack channel and a crib sheet to onboard new FBI arrivals. Intriguingly, we discovered that the general counsel of the FBI — arguably the second most powerful person of the FBI, or maybe the first, if you think, consider that what their actual job is to decide what the FBI can and can’t do — resigned from FBI in early 2020 and went to Twitter to take the deputy general counsel role. Isn’t that interesting? Somebody in one of the most powerful legal positions in the world would take a junior legal role at a social media company. Why would that be? This email popped up when we were going through Twitter files and it really jumped out at us. It’s from the director of policy at Twitter. “We have seen a sustained if uncoordinated” — supposedly — “effort by the intelligence community to push us to share more information and change our API policies. They’re probing and pushing everywhere they can.” The Hunter Biden laptop censorship occurred later that year. The FBI and the intelligence community discredited accurate, factual information about Hunter Biden’s foreign business dealings both before and after the New York Post revealed the contents of his laptop on October 14th, 2020. How could the FBI spread false information about something that nobody knew about? Because the FBI had Hunter Biden’s laptop, which showed his family’s massive influence peddling scheme. It consisted of accepting tens of millions of dollars, including from the Chinese government. The FBI had been sitting on that laptop since December of 2019. They had been given it by the computer repair store owner, who had been given the laptop by Hunter Biden, likely because he dropped it in his bathtub or in the pool, when he was on one of his many crack and alcohol benders. The government strategy is always the same: spread disinformation first, then demand censorship of accurate information on the basis of it . “The FBI came to us in the summer of 2020,” Mark Zuckerberg told Joe Rogan two years later, “and they were like, ‘Hey, you should be on the alert. We thought that there was a lot of Russian propaganda in 2016. There’s about to be some kind of dump.’” In the summer of 2020, the New York Post had not published the story about the Hunter Biden laptop. It would only come out in October. We see something very interesting show up in the Twitter files: the Aspen Institute, an intermediary between the intelligence community and the public. It’s known as a Davos-style gab fest in the United States. It’s also the place where intelligence community operations are run. They hosted a workshop to train reporters and all of the social media’s top censorship officials, known as “trust and safety officials,” how to deal with a story they would hear in the future relating to Hunter Biden and Barisma. A few months earlier, the Stanford Cyber Policy Center had published a report attacking what we in the United States call the Pentagon Papers Principle. The Pentagon Papers Principle says that if a government official gives me, a journalist, a bunch of Pentagon documents showing that we’re losing the war in Vietnam, I, as a journalist, can publish them, and not risk prison. That was decided in a famous Supreme Court case in 1971. Stanford argued that, really, we should get rid of that principle, which may be the most important investigative journalism principle in the United States, and said, “You should cover the person who leaked the materials, not the leaked emails.” In other words, you should cover and expose the whistleblower. The person who exposed the Pentagon Papers is the real bad guy, not the DOD, CIA, and presidents who had lied to us for over a decade. Stanford was training the journalists and the social media trust and safety officers in how to cover a story that had not yet come out. This is known as “pre-bunking,” and it’s also part of the European Union strategy to shield themselves from democracy. When the Hunter Biden story appeared October 22nd, Twitter’s trust and safety censorship official said it didn’t violate its terms of service. There’s nothing illegal about any of this. The Supreme Court has made it very clear that you’re allowed to report on information that’s been leaked to you. At that moment, the former FBI general counsel, Jim Baker, argued vigorously that Twitter really needed to censor it. Baker won and they censored the story. It’s not that we didn’t hear about the Hunter Biden laptop story when it came out. I certainly did. But we had the impression that there was something wrong with it, that it was not really the whole story. And so many of us dismissed it. What they had done was a psyop on this major story. They had changed our perception of the story. And it worked. It worked on me, it worked on everybody I knew. What is the role of the intelligence community of social media companies? The former CIA people are the head of elections at Meta, and Google’s head of trust and safety. Former and current CIA officers have a history of spreading misinformation and promoting the Russiagate conspiracy theory. We now know that between 2018 and 2023, there were 36 people from the CIA 68 from the FBI 44, from the National Security Administration and 68 from the Department of Homeland Security who had moved to work at the social media platforms. This is not unique to the United States. My colleague Cecilia Jilková, the daughter of famous Czech dissidents, discovered that European Union officials claimed, days before the European elections in 2024, that a “pro-Kremlin website” was spreading propaganda and were paying off European politicians. That was the headline in Politico. We wrote to them and asked, “Where’s the evidence of this? Just go ahead and share the evidence to support your accusation days before the European parliamentary elections.” Nobody was arrested. They never produced the evidence. The former Czech president, Václav Klaus, who was accused of this, said, “We don’t even know what the ‘Voice of Europe’ is.” Another Czech politician said, “How could I have known it would be a security threat? At the time I gave the interview, they weren’t on any list.” Another said, “If they’re such a big threat, why did the European Parliament let the Voice of Europe’s journalists inside?” Nobody responded. Nobody talked to us. This was a disinformation campaign carried by Politico, which, in my view, is a suspect publication. In the spring of 2022, Barack Obama went to Stanford to give a speech at the Stanford Cyber Policy Center run by Michael McFaul, his former ambassador to Russia. Obama said misinformation harms democracy and urged support for legislation in Congress that would empower government-appointed researchers to serve as “trusted flaggers.” Six days later, the Department of Homeland Security rolled out their Disinformation Governance Board. What a coincidence that they got Obama to frame the issue for them. Facebook in 2021 censored accurate vaccine information so the White House would help it to get data from Europe. In addition to removing vaccine misinformation, wrote Facebook to the White House, we have been focused on reducing the virality of content discouraging vaccines that does not contain actionable misinformation. White House said jump, and Facebook said how high? Why did they do it? Why would they voluntarily censor? This also emerged from the Facebook files. Nick Clegg on the left wrote an email to his colleagues. He said, “My sense is that given we’ve got bigger fish, we have to fry with the administration, e.g., data flows, it doesn’t seem like a great place for us to be.” Data flows. What’s he talking about? He’s talking about billions of dollars worth of business that he has to, that they would have to pay the European Commission for if they didn’t have the support from the Biden administration to lean on the European Commission. In other words, this was a shakedown by the White House of Facebook and it worked in France, the country of Liberté. Turns out it has a special role.... Please subscribe now to support Public's defense of free speech, watch the full video, and read the rest of the article!

Michael Shellenberger

174,749 次观看 • 8 个月前

$ASTI Ascent Solar Technologies Space and Drone Solar Panels The "Going to Zero" or Mispriced Space/Drone Solar Play Intro and comparison to $RKLB and $RDW panels Let’s get the ugly stuff out of the way first. $ASTI is a distressed penny stock with a ~$5M-$10M market cap. • They burn millions in cash. • 2024 Revenue: ~$40k. 2025 Revenue (YTD): ~$60k. • They generate less revenue than a single Tesla Model Y. • They have diluted shareholders relentlessly. $ASTI just raised $2M in December with the potential of $3.5M more via warrants while being a ~$5M mcap "company". Yikes. To most, this is "uninvestable trash." Stay away. Full stop. So why did I buy ~5% of the float? IF the technology works and IF they execute then I believe this is a massive market pricing dislocation about to inflect. They have been grinding for years and may finally be hitting an inflection point. $RKLB Rocketlab is the king of space solar and they are my second largest position overall, but here is why $ASTI might be a very high risk but asymmetric bet in Space & Defense right now. 1. The Tech Pivot: Flexible CIGS vs. The World Ascent started in 2005 but pivoted 2 years ago from consumer to pure-play Space & Defense. They have sunk ~$250M and 20 years of R&D into proprietary CIGS (Copper-Indium-Gallium-Selenide) thin-film technology while building out fully domestic and vertically integrated manufacturing capabilities. The Physics: • Thickness: 0.03 mm (Thinner than paper). • Flexibility: Wraps around drones/satellites; rolls up like a poster. • Durability: "Self-Healing" capabilities against space radiation. Can take a bullet or micrometeoroid and keep working. Can handle shocks/vibration. Does not shatter. The Metric that Matters: Specific Power (W/kg) (aka energy to weight ratio) In space, mass means cost and difficult decision decisions. • Rocket Lab ($RKLB) / Spectrolab: ~150 W/kg (System level). • Ascent Solar ($ASTI): ~1,960 W/kg (Module level). $ASTI is roughly 10x lighter for the same power output potential (mass-wise). This frees up design limitations and cost. 2. The Competition: $RKLB & $RDW Rocket Lab (SolAero) & Redwire (iROSA): • Tech: Rigid Crystal Cells (Multi-junction) embedded in a fabric mesh. • Pros: Extreme Efficiency (~30%+). Perfect for limited surface area. • Cons: Heavy, Brittle, Expensive ($3k-$10k per Watt). Manufacturing multi-junction cells (SolAero) involves slowly growing crystals in a vacuum chamber. With radiation the panels degrade and loose efficiency over time which will limit the satellite lifespan. • Use Case: James Webb Telescope, Flagship missions. Ascent Solar (ASTI): • Tech: Flexible Thin-Film on Plastic. • Pros: Ultra-light, Durable, Cheap ($500-$1k per Watt). Manufacturing CIGS is roughly similar to printing newspapers (roll-to-roll). The panels are radiation degradation resistant and will outlive the satellite • Cons: Lower Efficiency (~17.5%). Requires 2x surface area. • Use Case: Mega-Constellations (Starlink/Amazon Leo), Small/Low cost satellites, Drones, Deformable surfaces. The lower efficiency is not an ASTI failing. It is the inherent physics trade-off of not using glass/rigid silicone. The downside however is increased atmospheric drag with very larger/massive panel sheets. Because ASTI modules are ~50% less efficient than rigid panels, they require ~2x the physical surface area to generate the same amount of power. In GEO (High Orbit): Drag doesn't matter. Weight savings are king. A massive solar array allows for more sensors and longer project lifespan. ASTI is highly competitive here. In LEO (Low Orbit): Atmospheric drag is real. A massive solar array acts like a large parachute, causing the satellite to de-orbit faster unless it burns more fuel to stay up. At LEO, smaller satellites are a better fit for ASTI. 3. Durability & Radiation "Self-Healing" Radiation Hardness This is ASTI's "Ace in the Hole" for physics. The Problem: In space, high-energy protons (radiation) smash into solar cells, creating atomic "defects" that trap electrons. Over time, this kills the panel's power output (degradation). The CIGS Advantage: CIGS (Copper-Indium-Gallium-Selenide) material has a unique property where heat (annealing) allows the atomic structure to relax and "heal" these defects. Self-Healing: Because CIGS heals at relatively low temperatures (often achieved just by the sun heating the panel), it suffers significantly less degradation than traditional Silicon or even some GaAs panels over long missions in high-radiation belts (like MEO or GEO). Lifespan: While a rigid GaAs panel might lose 15-20% of its power over 15 years (enough to kill a satellite), CIGS panels heal and can maintain a flatter power curve, potentially outlasting the satellite itself in high-radiation orbits. 4. Brittleness & Flexibility ASTI (CIGS on Polyimide): Flexible. You can roll it like a poster. It can take a bullet or micrometeoroid and the hole will just be a dead spot; the rest of the panel keeps working. It does not shatter. Redwire (ROSA) & Rocket Lab (SolAero): Brittle Cells on a Flex Blanket. $RDW's ROSA (Roll-Out Solar Array) typically uses rigid multi-junction cells (made by SolAero/Rocket Lab or Spectrolab) mounted on a flexible mesh fabric. The Risk: If you bend the cells too far, they crack. They rely on the mesh backing for flexibility, but the active generating material is still a brittle crystal wafer. Much heavier, more expensive, and less durable than $ASTI's option 5. The Inflection Point (Why Now?) After years of silent struggle, late 2025 has seen an explosion of activity. Recent Agreements (Nov/Dec 2025): NovaSpark: Hydrogen-powered military drones. $ASTI panels generate power in the field → NovaSpark creates hydrogen fuel. CisLunar Industries: Integrating ASTI solar with power conversion hardware for deep space longevity. Defiant Space: A strategic alliance to act as the "door opener" for classified DoD/NATO programs. More headlines: Ascent Solar Technologies Provides Leading Space Company with Thin-Film PV modules for Spacecraft Power Generation Testing in Cislunar Space December 03, 2025 08:00 ET Ascent Solar Technologies Delivers Thin-Film PV for Saltwater Environment Durability and Space-Based Power Beaming Testing October 14, 2025 08:00 ET Ascent Solar Enters Teaming Agreement with Emtel Energy USA to Advance Thin-Film PV Energy Storage Capabilities September 16, 2025 08:00 ET Ascent Solar Technologies Signs MOU with Star Catcher Industries to Improve Power Capabilities for Thin-Film Solar Technology in Space August 28, 2025 08:00 ET Ascent Solar Technologies Establishes Rapid Thin-Film PV Delivery Process to Provide Customized Space Solar Products Ahead of Schedule on Mission Enabling Timelines August 07, 2025 08:00 ET The Pipeline (From Aug Corporate Presentation) 18 new NDA's signed in 2025. They are field testing with 3 major players: • Company A: Mega-constellation (+2,500 satellites). • Company B: Space Defense (Explicitly mentioned "Golden Dome"). • Company C: Satellite Manufacturer (30-200 unit scale). Management: New board members include a former founding member of SpaceX and a retired Air Force General and Deputy Assistant Secretary for Contracting (acquisitions expert). The company started in 2005 based out of Colorado, but two years ago pivoted to Space & Defense and away from consumer applications. Made in USA: Defense contracts heavily favor domestic supply chains. ASTI manufactures in Colorado. This is a huge moat against cheap Chinese solar. In their Q3 report they note that their market has seen sudden recent acceleration. The space solar industry is currently only capable of 8 to 12 MW per year of production meanwhile the demand is growing to over 100 MW per year. 6. The Risk (The Sword of Damocles) ⚠️ This is critical. $ASTI just raised ~$2M in December. Attached to that raise are ~2 Million Warrants with a strike price of $1.70. These are exercisable immediately. If the stock rips to $3.00, warrant holders exercise at $1.70 and dump on the market for a risk-free 76% profit. This creates a massive "sell wall" and potential 40% dilution of the float. Summary: This is a binary bet. • Bear Case: They run out of cash in 6 months, dilution spirals, stock goes to $0. • Bull Case: They land one of the "Company A/B/C" contracts. Revenue jumps from $60k to projected $20M+ in 2026. The stock reprices from a "bankrupt penny stock" to a "critical defense/space supplier." I have gradually accumulated ~5% of the float. I am ready for it to go to zero. But if the space economy demands "Cheap, Light, and Durable," $ASTI is the only public pure-play. Disclaimer: This is a very high-risk microcap. Do your own due diligence. Not financial advice.
3:04

$ASTI Ascent Solar Technologies Space and Drone Solar Panels The "Going to Zero" or Mispriced Space/Drone Solar Play Intro and comparison to $RKLB and $RDW panels Let’s get the ugly stuff out of the way first. $ASTI is a distressed penny stock with a ~$5M-$10M market cap. • They burn millions in cash. • 2024 Revenue: ~$40k. 2025 Revenue (YTD): ~$60k. • They generate less revenue than a single Tesla Model Y. • They have diluted shareholders relentlessly. $ASTI just raised $2M in December with the potential of $3.5M more via warrants while being a ~$5M mcap "company". Yikes. To most, this is "uninvestable trash." Stay away. Full stop. So why did I buy ~5% of the float? IF the technology works and IF they execute then I believe this is a massive market pricing dislocation about to inflect. They have been grinding for years and may finally be hitting an inflection point. $RKLB Rocketlab is the king of space solar and they are my second largest position overall, but here is why $ASTI might be a very high risk but asymmetric bet in Space & Defense right now. 1. The Tech Pivot: Flexible CIGS vs. The World Ascent started in 2005 but pivoted 2 years ago from consumer to pure-play Space & Defense. They have sunk ~$250M and 20 years of R&D into proprietary CIGS (Copper-Indium-Gallium-Selenide) thin-film technology while building out fully domestic and vertically integrated manufacturing capabilities. The Physics: • Thickness: 0.03 mm (Thinner than paper). • Flexibility: Wraps around drones/satellites; rolls up like a poster. • Durability: "Self-Healing" capabilities against space radiation. Can take a bullet or micrometeoroid and keep working. Can handle shocks/vibration. Does not shatter. The Metric that Matters: Specific Power (W/kg) (aka energy to weight ratio) In space, mass means cost and difficult decision decisions. • Rocket Lab ($RKLB) / Spectrolab: ~150 W/kg (System level). • Ascent Solar ($ASTI): ~1,960 W/kg (Module level). $ASTI is roughly 10x lighter for the same power output potential (mass-wise). This frees up design limitations and cost. 2. The Competition: $RKLB & $RDW Rocket Lab (SolAero) & Redwire (iROSA): • Tech: Rigid Crystal Cells (Multi-junction) embedded in a fabric mesh. • Pros: Extreme Efficiency (~30%+). Perfect for limited surface area. • Cons: Heavy, Brittle, Expensive ($3k-$10k per Watt). Manufacturing multi-junction cells (SolAero) involves slowly growing crystals in a vacuum chamber. With radiation the panels degrade and loose efficiency over time which will limit the satellite lifespan. • Use Case: James Webb Telescope, Flagship missions. Ascent Solar (ASTI): • Tech: Flexible Thin-Film on Plastic. • Pros: Ultra-light, Durable, Cheap ($500-$1k per Watt). Manufacturing CIGS is roughly similar to printing newspapers (roll-to-roll). The panels are radiation degradation resistant and will outlive the satellite • Cons: Lower Efficiency (~17.5%). Requires 2x surface area. • Use Case: Mega-Constellations (Starlink/Amazon Leo), Small/Low cost satellites, Drones, Deformable surfaces. The lower efficiency is not an ASTI failing. It is the inherent physics trade-off of not using glass/rigid silicone. The downside however is increased atmospheric drag with very larger/massive panel sheets. Because ASTI modules are ~50% less efficient than rigid panels, they require ~2x the physical surface area to generate the same amount of power. In GEO (High Orbit): Drag doesn't matter. Weight savings are king. A massive solar array allows for more sensors and longer project lifespan. ASTI is highly competitive here. In LEO (Low Orbit): Atmospheric drag is real. A massive solar array acts like a large parachute, causing the satellite to de-orbit faster unless it burns more fuel to stay up. At LEO, smaller satellites are a better fit for ASTI. 3. Durability & Radiation "Self-Healing" Radiation Hardness This is ASTI's "Ace in the Hole" for physics. The Problem: In space, high-energy protons (radiation) smash into solar cells, creating atomic "defects" that trap electrons. Over time, this kills the panel's power output (degradation). The CIGS Advantage: CIGS (Copper-Indium-Gallium-Selenide) material has a unique property where heat (annealing) allows the atomic structure to relax and "heal" these defects. Self-Healing: Because CIGS heals at relatively low temperatures (often achieved just by the sun heating the panel), it suffers significantly less degradation than traditional Silicon or even some GaAs panels over long missions in high-radiation belts (like MEO or GEO). Lifespan: While a rigid GaAs panel might lose 15-20% of its power over 15 years (enough to kill a satellite), CIGS panels heal and can maintain a flatter power curve, potentially outlasting the satellite itself in high-radiation orbits. 4. Brittleness & Flexibility ASTI (CIGS on Polyimide): Flexible. You can roll it like a poster. It can take a bullet or micrometeoroid and the hole will just be a dead spot; the rest of the panel keeps working. It does not shatter. Redwire (ROSA) & Rocket Lab (SolAero): Brittle Cells on a Flex Blanket. $RDW's ROSA (Roll-Out Solar Array) typically uses rigid multi-junction cells (made by SolAero/Rocket Lab or Spectrolab) mounted on a flexible mesh fabric. The Risk: If you bend the cells too far, they crack. They rely on the mesh backing for flexibility, but the active generating material is still a brittle crystal wafer. Much heavier, more expensive, and less durable than $ASTI's option 5. The Inflection Point (Why Now?) After years of silent struggle, late 2025 has seen an explosion of activity. Recent Agreements (Nov/Dec 2025): NovaSpark: Hydrogen-powered military drones. $ASTI panels generate power in the field → NovaSpark creates hydrogen fuel. CisLunar Industries: Integrating ASTI solar with power conversion hardware for deep space longevity. Defiant Space: A strategic alliance to act as the "door opener" for classified DoD/NATO programs. More headlines: Ascent Solar Technologies Provides Leading Space Company with Thin-Film PV modules for Spacecraft Power Generation Testing in Cislunar Space December 03, 2025 08:00 ET Ascent Solar Technologies Delivers Thin-Film PV for Saltwater Environment Durability and Space-Based Power Beaming Testing October 14, 2025 08:00 ET Ascent Solar Enters Teaming Agreement with Emtel Energy USA to Advance Thin-Film PV Energy Storage Capabilities September 16, 2025 08:00 ET Ascent Solar Technologies Signs MOU with Star Catcher Industries to Improve Power Capabilities for Thin-Film Solar Technology in Space August 28, 2025 08:00 ET Ascent Solar Technologies Establishes Rapid Thin-Film PV Delivery Process to Provide Customized Space Solar Products Ahead of Schedule on Mission Enabling Timelines August 07, 2025 08:00 ET The Pipeline (From Aug Corporate Presentation) 18 new NDA's signed in 2025. They are field testing with 3 major players: • Company A: Mega-constellation (+2,500 satellites). • Company B: Space Defense (Explicitly mentioned "Golden Dome"). • Company C: Satellite Manufacturer (30-200 unit scale). Management: New board members include a former founding member of SpaceX and a retired Air Force General and Deputy Assistant Secretary for Contracting (acquisitions expert). The company started in 2005 based out of Colorado, but two years ago pivoted to Space & Defense and away from consumer applications. Made in USA: Defense contracts heavily favor domestic supply chains. ASTI manufactures in Colorado. This is a huge moat against cheap Chinese solar. In their Q3 report they note that their market has seen sudden recent acceleration. The space solar industry is currently only capable of 8 to 12 MW per year of production meanwhile the demand is growing to over 100 MW per year. 6. The Risk (The Sword of Damocles) ⚠️ This is critical. $ASTI just raised ~$2M in December. Attached to that raise are ~2 Million Warrants with a strike price of $1.70. These are exercisable immediately. If the stock rips to $3.00, warrant holders exercise at $1.70 and dump on the market for a risk-free 76% profit. This creates a massive "sell wall" and potential 40% dilution of the float. Summary: This is a binary bet. • Bear Case: They run out of cash in 6 months, dilution spirals, stock goes to $0. • Bull Case: They land one of the "Company A/B/C" contracts. Revenue jumps from $60k to projected $20M+ in 2026. The stock reprices from a "bankrupt penny stock" to a "critical defense/space supplier." I have gradually accumulated ~5% of the float. I am ready for it to go to zero. But if the space economy demands "Cheap, Light, and Durable," $ASTI is the only public pure-play. Disclaimer: This is a very high-risk microcap. Do your own due diligence. Not financial advice.

YeahDave

208,571 次观看 • 9 个月前