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Amplify's Gen 2 preview has per-developer cloud sandbox environments which are optimized for faster iteration while developing! ☁️ Deploy high-fidelity cloud sandboxes to work on features independently without disrupting your teammates' environments!

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AWS Amplify

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18,403 Aufrufe • vor 2 Jahren •via X (Twitter)

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Oh my! I'm too hyped

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We are so excited to announce the second generation of AWS Amplify’s fullstack app building capabilities, now in preview! 💻 TypeScript-first DX ☁️ Per-developer cloud sandboxes 👩🏼‍💻 Fullstack Git branch deploys We’d love to hear your feedback 💜
2:19

We are so excited to announce the second generation of AWS Amplify’s fullstack app building capabilities, now in preview! 💻 TypeScript-first DX ☁️ Per-developer cloud sandboxes 👩🏼‍💻 Fullstack Git branch deploys We’d love to hear your feedback 💜

AWS Amplify

137,141 Aufrufe • vor 2 Jahren

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

12,141 Aufrufe • vor 2 Monaten

Agent development is moving forward. While production scaling traditionally required manual infrastructure setup and provisioning sandboxes from scratch, there's now a faster path. Managed Agents in the Gemini API are officially live. Get maximum agentic power with zero infrastructure overhead. One simple API call delivers a managed, remote sandbox for your agent so you can focus entirely on behavior. 📁 Fully customizable with your instructions, tools, and data via and 🌐 Test, iterate, and scale long-horizon tasks natively in the cloud. The hardest part of building agents is now the part you don't have to build. 👇 Get started now:
0:41

Agent development is moving forward. While production scaling traditionally required manual infrastructure setup and provisioning sandboxes from scratch, there's now a faster path. Managed Agents in the Gemini API are officially live. Get maximum agentic power with zero infrastructure overhead. One simple API call delivers a managed, remote sandbox for your agent so you can focus entirely on behavior. 📁 Fully customizable with your instructions, tools, and data via and 🌐 Test, iterate, and scale long-horizon tasks natively in the cloud. The hardest part of building agents is now the part you don't have to build. 👇 Get started now:

Google for Developers

39,252 Aufrufe • vor 1 Monat

If your whole workflow is one Claude Code instance on your MacBook, you're leaving 10x on the table. Here's why. On your laptop, running 2 agents at once means two copies of your code, and you're constantly fighting over which worktree you're on and whether everything's synced. It caps out fast. Cloud agents in VMs let you run as many as you want without worrying about any of that. Each one gets its own machine, so the work never collides. Spin up 10, let them all run, and nothing steps on anything else. Watch full ep below on The Startup Ideas Podcast (SIP) 🧃. Thanks to Ryan Carson for sharing the sauce. Cloud agents are probably the future. So is doing half your work from your phone, which sounds insane until you try it once. Then you don't go back.
44:46

If your whole workflow is one Claude Code instance on your MacBook, you're leaving 10x on the table. Here's why. On your laptop, running 2 agents at once means two copies of your code, and you're constantly fighting over which worktree you're on and whether everything's synced. It caps out fast. Cloud agents in VMs let you run as many as you want without worrying about any of that. Each one gets its own machine, so the work never collides. Spin up 10, let them all run, and nothing steps on anything else. Watch full ep below on The Startup Ideas Podcast (SIP) 🧃. Thanks to Ryan Carson for sharing the sauce. Cloud agents are probably the future. So is doing half your work from your phone, which sounds insane until you try it once. Then you don't go back.

GREG ISENBERG

138,986 Aufrufe • vor 5 Tagen

Google just made every $50K master's degree look like a scam. They dropped "Google Skills" - 3,000+ AI courses from DeepMind, Cloud, and Google Education in one platform. And it's 100% FREE for Google Cloud users. The same content universities charge $60K for: - DeepMind's actual AI research training - 700+ hands-on labs with real cloud environments - Gemini Code Assist built INTO the learning - Direct hiring paths at 150+ companies While everyone's drowning in student debt, smart people are getting: ✓ Skills that actually get you hired ✓ Certificates employers recognize (82% hiring preference) ✓ Zero cost if you have Google Cloud ✓ Or $29/month vs $1,600/month for Udacity The kicker? 26 million people completed courses BEFORE this consolidation. You're competing against people learning AI from the team that BUILT Gemini. How to actually use this (not just browse): 1. Start with "AI Essentials" - no coding required 2. Use the hands-on labs (this is where 90% quit) 3. Get skill badges - they show up on LinkedIn 4. Target Google Cloud certification - top 2 highest paying IT certs 5. Join the 150-company hiring consortium The education industrial complex is panicking because anyone can now: → Learn from DeepMind researchers directly → Practice with $500 in free Cloud credits → Get hired without a degree One person's $60K tuition = 2,070 months of Google Skills. Let that sink in. Comment "SKILLS" and I'll send you: ✓ The exact learning path that gets you hired fastest ✓ Which certifications actually pay ✓ How to access everything free Your competition is still applying to universities. Time to eat their lunch.
0:56

Google just made every $50K master's degree look like a scam. They dropped "Google Skills" - 3,000+ AI courses from DeepMind, Cloud, and Google Education in one platform. And it's 100% FREE for Google Cloud users. The same content universities charge $60K for: - DeepMind's actual AI research training - 700+ hands-on labs with real cloud environments - Gemini Code Assist built INTO the learning - Direct hiring paths at 150+ companies While everyone's drowning in student debt, smart people are getting: ✓ Skills that actually get you hired ✓ Certificates employers recognize (82% hiring preference) ✓ Zero cost if you have Google Cloud ✓ Or $29/month vs $1,600/month for Udacity The kicker? 26 million people completed courses BEFORE this consolidation. You're competing against people learning AI from the team that BUILT Gemini. How to actually use this (not just browse): 1. Start with "AI Essentials" - no coding required 2. Use the hands-on labs (this is where 90% quit) 3. Get skill badges - they show up on LinkedIn 4. Target Google Cloud certification - top 2 highest paying IT certs 5. Join the 150-company hiring consortium The education industrial complex is panicking because anyone can now: → Learn from DeepMind researchers directly → Practice with $500 in free Cloud credits → Get hired without a degree One person's $60K tuition = 2,070 months of Google Skills. Let that sink in. Comment "SKILLS" and I'll send you: ✓ The exact learning path that gets you hired fastest ✓ Which certifications actually pay ✓ How to access everything free Your competition is still applying to universities. Time to eat their lunch.

Nozz

568,898 Aufrufe • vor 9 Monaten

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

270,625 Aufrufe • vor 2 Monaten

How is CI/CD changing because of AI, and what are sensible deployment practices more teams should be doing? Robert Erez is a CI/CD expert, and also my former teammate at Skype. Timestamps: 00:00 Intro 02:09 Canary deployments at Skype 05:01 Joining at Octopus Deploy 06:15 Continuous deployment 10:26 Why Kubernetes won 15:51 Kubernetes on-prem 18:50 How GitOps works 25:00 The uses and limitations of GitOps 31:04 The rise of platform teams 35:51 How AI is changing CI/CD 39:49 Progressive delivery explained 47:31 Rollbacks and roll-forwards 50:14 Feature flags 54:32 How development environments are evolving 57:40 Cloud development environments (CDEs) 1:03:45 Self-hosting CI/CD 1:09:25 Getting started with progressive delivery 1:11:15 Book recommendations Brought to you by: • Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. • WorkOS – everything you need to make your app enterprise ready • turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable. Three interesting thoughts from Rob: 1. Roll forward, never backwards. When a system has state – which typically means it uses databases – then doing a rollback can leave the code talking to a schema that’s no longer in sync. Rob’s advice is to not treat a failure in v2 as a trip back to v1, but rather as a push to v3 with the fix in it. 2. GitOps isn’t actually about Git. None of the four pillars of GitOps – 1) declarative, 2) versioned and immutable, 3) pulled, not pushed, 4) continuously reconciled – require Git, although Git can work under these constraints. Yet, the term ‘GitOps’ has made the industry dogmatic about cramming everything into a repo – even things like secrets that absolutely shouldn’t be there! 3. There’s a trend of ephemeral environments replacing test/staging environments across the industry. Companies used to have a few testers fighting over a handful of static test environments, but today, it’s trivial to spin up a full environment, per-feature branch, pre-merge. This is an “ephemeral” environment for evaluating that things work, which is then torn down once something is merged. It helps speed up the feedback process.
1:15:35

How is CI/CD changing because of AI, and what are sensible deployment practices more teams should be doing? Robert Erez is a CI/CD expert, and also my former teammate at Skype. Timestamps: 00:00 Intro 02:09 Canary deployments at Skype 05:01 Joining at Octopus Deploy 06:15 Continuous deployment 10:26 Why Kubernetes won 15:51 Kubernetes on-prem 18:50 How GitOps works 25:00 The uses and limitations of GitOps 31:04 The rise of platform teams 35:51 How AI is changing CI/CD 39:49 Progressive delivery explained 47:31 Rollbacks and roll-forwards 50:14 Feature flags 54:32 How development environments are evolving 57:40 Cloud development environments (CDEs) 1:03:45 Self-hosting CI/CD 1:09:25 Getting started with progressive delivery 1:11:15 Book recommendations Brought to you by: • Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. • WorkOS – everything you need to make your app enterprise ready • turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable. Three interesting thoughts from Rob: 1. Roll forward, never backwards. When a system has state – which typically means it uses databases – then doing a rollback can leave the code talking to a schema that’s no longer in sync. Rob’s advice is to not treat a failure in v2 as a trip back to v1, but rather as a push to v3 with the fix in it. 2. GitOps isn’t actually about Git. None of the four pillars of GitOps – 1) declarative, 2) versioned and immutable, 3) pulled, not pushed, 4) continuously reconciled – require Git, although Git can work under these constraints. Yet, the term ‘GitOps’ has made the industry dogmatic about cramming everything into a repo – even things like secrets that absolutely shouldn’t be there! 3. There’s a trend of ephemeral environments replacing test/staging environments across the industry. Companies used to have a few testers fighting over a handful of static test environments, but today, it’s trivial to spin up a full environment, per-feature branch, pre-merge. This is an “ephemeral” environment for evaluating that things work, which is then torn down once something is merged. It helps speed up the feedback process.

Gergely Orosz

19,307 Aufrufe • vor 1 Monat

QVAC SDK 0.10.0 is now live, bringing advanced local compute capabilities and specialized hardware optimization directly to your device Key Features and Updates: - Image-to-Image Diffusion: Transform and edit images using simple prompts with 100% local compute—no cloud uploads or external servers required - Dynamic Tooling & KV Cache Management:Your local LLM now receives a tailored toolbox for every interaction, with automatic KV cache clearing to maintain high-speed inference - Doctor CLI: A new diagnostic tool that analyzes your hardware and memory, providing specific instructions on how to optimize your GPU for local AI - Suspend & Resume API: Specifically designed for mobile environments, this allows apps to pause P2P swarms and RAG workspaces to meet background rules without losing model state - GPT-OSS Compatibility: Added support for the latest GPT-OSS models loaded externally, expanding the range of open-source intelligence available on the platform Build the future of private, unstoppable AI:
1:11

QVAC SDK 0.10.0 is now live, bringing advanced local compute capabilities and specialized hardware optimization directly to your device Key Features and Updates: - Image-to-Image Diffusion: Transform and edit images using simple prompts with 100% local compute—no cloud uploads or external servers required - Dynamic Tooling & KV Cache Management:Your local LLM now receives a tailored toolbox for every interaction, with automatic KV cache clearing to maintain high-speed inference - Doctor CLI: A new diagnostic tool that analyzes your hardware and memory, providing specific instructions on how to optimize your GPU for local AI - Suspend & Resume API: Specifically designed for mobile environments, this allows apps to pause P2P swarms and RAG workspaces to meet background rules without losing model state - GPT-OSS Compatibility: Added support for the latest GPT-OSS models loaded externally, expanding the range of open-source intelligence available on the platform Build the future of private, unstoppable AI:

QVAC

34,508 Aufrufe • vor 2 Monaten

JENSEN HUANG UNVEILED A $3,000 SUPERCOMPUTER THAT REPLACES $1,900/MONTH IN CLOUD GPUS WITH $2 IN ELECTRICITY. IT FITS ON YOUR DESK a developer was paying $1,900 a month renting a100 and h100 cloud gpus. he bought the dgx spark, set it up in an afternoon, and his monthly bill dropped from $1,900 to $2 in electricity the box runs 128gb of unified memory and 1 petaflop of ai compute. a 4090 gives you 24gb of vram and taps out at 30b models. the spark loads 70b at full precision and stretches to 200b without breaking a sweat break-even hit in 6 weeks. after that the $1,890 he used to wire to a rental company every month stayed in his business. first year that's $22,000 back install ollama, change one line in your code, point it at localhost instead of the cloud. nothing else changes. nothing leaves your machine and nothing costs money per request cloud gpu costs aren't getting cheaper and rate limits keep getting tighter. the people who pulled their ai workloads onto a box on their desk in 2026 are going to look very far ahead in 2028 bookmark this and read the article below
0:33

JENSEN HUANG UNVEILED A $3,000 SUPERCOMPUTER THAT REPLACES $1,900/MONTH IN CLOUD GPUS WITH $2 IN ELECTRICITY. IT FITS ON YOUR DESK a developer was paying $1,900 a month renting a100 and h100 cloud gpus. he bought the dgx spark, set it up in an afternoon, and his monthly bill dropped from $1,900 to $2 in electricity the box runs 128gb of unified memory and 1 petaflop of ai compute. a 4090 gives you 24gb of vram and taps out at 30b models. the spark loads 70b at full precision and stretches to 200b without breaking a sweat break-even hit in 6 weeks. after that the $1,890 he used to wire to a rental company every month stayed in his business. first year that's $22,000 back install ollama, change one line in your code, point it at localhost instead of the cloud. nothing else changes. nothing leaves your machine and nothing costs money per request cloud gpu costs aren't getting cheaper and rate limits keep getting tighter. the people who pulled their ai workloads onto a box on their desk in 2026 are going to look very far ahead in 2028 bookmark this and read the article below

starmex

141,376 Aufrufe • vor 2 Monaten

Over the past few months, our Midnight program has consistently delivered strong results. We're now routinely flying longer, faster & higher as we expand the aircraft’s operational envelope. Recent flights have exceeded 55 miles, more than 30 minutes of flight time, altitudes up to our 10,000-foot service ceiling, and speeds in excess of 150 miles per hour. These missions are providing valuable certification data, while closely mirroring the majority of mission profiles we expect in early urban deployment environments. Our next flight test phase with Midnight will be piloted VTOL, including full transition flights with our type certification intent four-blade aft propellers. Unlike the CTOL envelope that we are now close to completing testing on, the VTOL and transition flight regimes are not new for Midnight, as we have previously completed an extensive flight test campaign with Midnight without a pilot onboard. Our progression from non-piloted VTOL to piloted CTOL to piloted VTOL & CTOL campaigns is optimized for safety and disciplined execution that builds our readiness for commercial operations. The momentum continues to grow, and the team continues to deliver.
0:20

Over the past few months, our Midnight program has consistently delivered strong results. We're now routinely flying longer, faster & higher as we expand the aircraft’s operational envelope. Recent flights have exceeded 55 miles, more than 30 minutes of flight time, altitudes up to our 10,000-foot service ceiling, and speeds in excess of 150 miles per hour. These missions are providing valuable certification data, while closely mirroring the majority of mission profiles we expect in early urban deployment environments. Our next flight test phase with Midnight will be piloted VTOL, including full transition flights with our type certification intent four-blade aft propellers. Unlike the CTOL envelope that we are now close to completing testing on, the VTOL and transition flight regimes are not new for Midnight, as we have previously completed an extensive flight test campaign with Midnight without a pilot onboard. Our progression from non-piloted VTOL to piloted CTOL to piloted VTOL & CTOL campaigns is optimized for safety and disciplined execution that builds our readiness for commercial operations. The momentum continues to grow, and the team continues to deliver.

Adam Goldstein

590,542 Aufrufe • vor 8 Monaten

The latest MLX has a CUDA back-end! To get started: pip install "mlx[cuda]" With the same codebase you can develop locally, run your model on Apple silicon, or in the cloud on Nvidia GPUs. MLX is designed around Apple silicon - which has a unified memory architecture. It uses the same design with CUDA. So there's no need to move arrays around from CPU memory to GPU memory. Note, this is early days - some operations are missing and performance is still being optimized. But it's already quite fast for Transformer training, text generation, and more! Here's a demo using mlx-lm to generate text with Llama 3 8B (bf16) on an A100:
0:20

The latest MLX has a CUDA back-end! To get started: pip install "mlx[cuda]" With the same codebase you can develop locally, run your model on Apple silicon, or in the cloud on Nvidia GPUs. MLX is designed around Apple silicon - which has a unified memory architecture. It uses the same design with CUDA. So there's no need to move arrays around from CPU memory to GPU memory. Note, this is early days - some operations are missing and performance is still being optimized. But it's already quite fast for Transformer training, text generation, and more! Here's a demo using mlx-lm to generate text with Llama 3 8B (bf16) on an A100:

Awni Hannun

42,761 Aufrufe • vor 1 Jahr

Introducing Workshop: cloud + on-device agentic AI. And to celebrate, we're giving away $250k in Google Gemini AI credits. (details below). The future of AI work is neither cloud-based nor local. It's both. In Workshop Cloud, you can use agents powered by frontier models like Claude and/or open source models like Z.ai's GLM-5 to build internal tools, dashboards, and AI web apps. Or, breeze through tasks like managing your Google and Meta Ads. In Workshop Desktop, you can do all the same right on your computer, plus make desktop apps, mobile apps, and 3D creations. Our favorite part? You can power the full agent experience with local models like Qwen 3.5 family on your computer. Fully offline. 2026 is the year in which local models for agentic tasks will become viable for mainstream use. But the setup for tools like OpenClaw is like setting up Linux from scratch on your computer. Workshop Desktop is one-click to install on Windows, Mac, and Linux. It recommends which open source model you should use for your hardware and lets you download and run it right in the app. And its agent harness allows you to chat, create websites, build personal utilities, and analyze data. 100% offline. Or multitask with AI models in the cloud while running other agent threads locally. Start in Workshop Cloud when you want flexibility and speed. Download your project and continue in Workshop Desktop when you want local files, privacy, and/or better performance on large code bases. Publish from either. The agent tooling space is maturing and discerning users have come to expect a lot from their tools. We've packed Workshop with features to help you 10x your productivity. - Native support for skills - Autocompaction for seamless context management - Built-in AI for your apps - Dozens of connectors, like Google Drive, Big Query, and Supabase - dbt integration to ground your dashboards in your semantic layer - Native Github integration - Private app deployment - ... and more (+ we're shipping super fast) To access the free credit offer, RT this post and reply with "Workshop". Make sure you are following us so we can DM you the instructions to redeem. - First 100 to RT + comment get $500 in credits. - Everyone else gets up to $250 And thanks to our partners Modal, Google Gemini, and Z.ai!
3:50

Introducing Workshop: cloud + on-device agentic AI. And to celebrate, we're giving away $250k in Google Gemini AI credits. (details below). The future of AI work is neither cloud-based nor local. It's both. In Workshop Cloud, you can use agents powered by frontier models like Claude and/or open source models like Z.ai's GLM-5 to build internal tools, dashboards, and AI web apps. Or, breeze through tasks like managing your Google and Meta Ads. In Workshop Desktop, you can do all the same right on your computer, plus make desktop apps, mobile apps, and 3D creations. Our favorite part? You can power the full agent experience with local models like Qwen 3.5 family on your computer. Fully offline. 2026 is the year in which local models for agentic tasks will become viable for mainstream use. But the setup for tools like OpenClaw is like setting up Linux from scratch on your computer. Workshop Desktop is one-click to install on Windows, Mac, and Linux. It recommends which open source model you should use for your hardware and lets you download and run it right in the app. And its agent harness allows you to chat, create websites, build personal utilities, and analyze data. 100% offline. Or multitask with AI models in the cloud while running other agent threads locally. Start in Workshop Cloud when you want flexibility and speed. Download your project and continue in Workshop Desktop when you want local files, privacy, and/or better performance on large code bases. Publish from either. The agent tooling space is maturing and discerning users have come to expect a lot from their tools. We've packed Workshop with features to help you 10x your productivity. - Native support for skills - Autocompaction for seamless context management - Built-in AI for your apps - Dozens of connectors, like Google Drive, Big Query, and Supabase - dbt integration to ground your dashboards in your semantic layer - Native Github integration - Private app deployment - ... and more (+ we're shipping super fast) To access the free credit offer, RT this post and reply with "Workshop". Make sure you are following us so we can DM you the instructions to redeem. - First 100 to RT + comment get $500 in credits. - Everyone else gets up to $250 And thanks to our partners Modal, Google Gemini, and Z.ai!

Workshop AI

28,510 Aufrufe • vor 4 Monaten

New course: Gemini CLI: Code & Create with an Open-Source Agent, built with Google Cloud Tech/Gemini CLI and taught by Jack Wotherspoon. Agentic coding assistants like Gemini CLI are transforming how developers work. This short course teaches you to use Google's open-source agent to coordinate local tools and cloud services for coding and non-coding workflows. Gemini CLI works from your terminal, so it works with your local files and development tools. You can also connect it to services through MCP. Then provide high-level instructions, and it autonomously plans and executes complex workflows. Skills you'll gain: - Build website features and automate code reviews with GitHub ActionsCreate data dashboards that combine local files with cloud data sources - Use MCP servers and extensions to orchestrate workflows across GitHub, Canva, and Google Workspace - Generate social media content from multimedia files like conference recordings I particularly appreciate that Gemini CLI is open-source. You can see exactly how it works, read the prompts it uses, and understand its architecture. The community has contributed thousands of pull requests. Since Gemini 3’s release I've found Gemini CLI highly capable - this is a tool worth having in your toolbox! Whether you're prototyping applications, automating workflows, or working with multimedia content, join to learn to delegate complex tasks and build faster:
2:50

New course: Gemini CLI: Code & Create with an Open-Source Agent, built with Google Cloud Tech/Gemini CLI and taught by Jack Wotherspoon. Agentic coding assistants like Gemini CLI are transforming how developers work. This short course teaches you to use Google's open-source agent to coordinate local tools and cloud services for coding and non-coding workflows. Gemini CLI works from your terminal, so it works with your local files and development tools. You can also connect it to services through MCP. Then provide high-level instructions, and it autonomously plans and executes complex workflows. Skills you'll gain: - Build website features and automate code reviews with GitHub ActionsCreate data dashboards that combine local files with cloud data sources - Use MCP servers and extensions to orchestrate workflows across GitHub, Canva, and Google Workspace - Generate social media content from multimedia files like conference recordings I particularly appreciate that Gemini CLI is open-source. You can see exactly how it works, read the prompts it uses, and understand its architecture. The community has contributed thousands of pull requests. Since Gemini 3’s release I've found Gemini CLI highly capable - this is a tool worth having in your toolbox! Whether you're prototyping applications, automating workflows, or working with multimedia content, join to learn to delegate complex tasks and build faster:

Andrew Ng

125,182 Aufrufe • vor 6 Monaten

🚨 JUST IN: Charlie Kirk's incredible speech is resurfacing where he STRONGLY advocates for letting young Americans achieve the American dream again of homeownership and a family Hard to listen to this without missing him. "To all the Gen Zers watching this convention on TikTok right now, I have a message just for you. You don't have to stay poor. You don't have to accept being worse off than your parents." "The basic things our parents enjoyed are increasingly out of reach for Gen Z and millennials. You see, the American dream has become a luxury item for the wealthy elites." "Democrats have given hundreds of billions of dollars to illegals and foreign nations, while Gen Z has to pinch pennies just so that they can never own a home, never marry, and work until they die." For Charlie🙏🇺🇸
0:44

🚨 JUST IN: Charlie Kirk's incredible speech is resurfacing where he STRONGLY advocates for letting young Americans achieve the American dream again of homeownership and a family Hard to listen to this without missing him. "To all the Gen Zers watching this convention on TikTok right now, I have a message just for you. You don't have to stay poor. You don't have to accept being worse off than your parents." "The basic things our parents enjoyed are increasingly out of reach for Gen Z and millennials. You see, the American dream has become a luxury item for the wealthy elites." "Democrats have given hundreds of billions of dollars to illegals and foreign nations, while Gen Z has to pinch pennies just so that they can never own a home, never marry, and work until they die." For Charlie🙏🇺🇸

Eric Daugherty

981,071 Aufrufe • vor 8 Monaten

New on Claude Code Desktop We’re shipping new features for Claude Code on desktop that let you preview running apps, auto-review code, and auto-fix and merge PRs to help close the development loop. - Server previews: Claude starts dev servers and previews your running app in the desktop interface. It reads console logs, catches errors, and keeps iterating on its own. - Local code review: Claude examines your local diffs and leaves inline comments before you push — an immediate second set of eyes on every change. - PR monitoring: Claude tracks CI status after you open a PR. With auto-fix, it attempts to resolve failures automatically. With auto-merge, PRs land as soon as checks pass. You can move on to your next task while Claude handles the last one. - Session mobility: Move sessions from CLI to desktop, and from desktop to the cloud. Start work at your desk, pick it up from the web or your phone.
1:15

New on Claude Code Desktop We’re shipping new features for Claude Code on desktop that let you preview running apps, auto-review code, and auto-fix and merge PRs to help close the development loop. - Server previews: Claude starts dev servers and previews your running app in the desktop interface. It reads console logs, catches errors, and keeps iterating on its own. - Local code review: Claude examines your local diffs and leaves inline comments before you push — an immediate second set of eyes on every change. - PR monitoring: Claude tracks CI status after you open a PR. With auto-fix, it attempts to resolve failures automatically. With auto-merge, PRs land as soon as checks pass. You can move on to your next task while Claude handles the last one. - Session mobility: Move sessions from CLI to desktop, and from desktop to the cloud. Start work at your desk, pick it up from the web or your phone.

Ado

71,910 Aufrufe • vor 5 Monaten

Building a truly private AI model isn’t as simple as the tech world wants you to believe. despite all the marketing around one click deployments, RAG systems, fine tuning services, and memory features, we still don’t have models that are genuinely trained only on your personal data with complete privacy from third parties. the reality is that most AI solutions today require you to trust someone else with your books, notes, journals, and training data. even when companies promise privacy, there’s usually a way for the provider or other parties to access your information during training or inference. Phala is taking a different approach by building the infrastructure needed for real data ownership. They’re developing confidential runtime environments where your AI training happens inside secure enclaves with attestation guarantees. The key innovation is their in-enclave keying system through dstack, which means that once your model is training, even Phala themselves cannot see your data or model weights.
0:34

Building a truly private AI model isn’t as simple as the tech world wants you to believe. despite all the marketing around one click deployments, RAG systems, fine tuning services, and memory features, we still don’t have models that are genuinely trained only on your personal data with complete privacy from third parties. the reality is that most AI solutions today require you to trust someone else with your books, notes, journals, and training data. even when companies promise privacy, there’s usually a way for the provider or other parties to access your information during training or inference. Phala is taking a different approach by building the infrastructure needed for real data ownership. They’re developing confidential runtime environments where your AI training happens inside secure enclaves with attestation guarantees. The key innovation is their in-enclave keying system through dstack, which means that once your model is training, even Phala themselves cannot see your data or model weights.

soulman 🎮

11,702 Aufrufe • vor 10 Monaten

This is part 2 of a 2 part post (see part 1 here Below is a structured analysis to demonstrate the validity of using buyers of Veritaseum #SmartMetal to buy into and sell compute from globally aggregated cell phone compute pools - directly compeiting with the big guys - Google, Amazon and Microsoft cloud businesses. We discuss estimates, business model propositions, and potential economic outcomes, but first, see my Executive Global Article on Zero Profit Models( and purchase Veritaseum SmartMetal here - Can you really disintermediate the most profitable revnues of t $6.7 trillion worth of technology cloud providers? Well, the fact that it is among, if not the, most profitable of their revenue drivers is a very material clue! Step 1: Estimating the Number of High-End Smartphones Globally As of early 2025, approximately 7.5 billion smartphones are actively used worldwide. Considering that: About 30% of global smartphones are high-end (comparable or superior to an iPhone X; for instance, Samsung Galaxy S22/S23 Ultra, iPhone 16 Pro Max with A18 chips, and Qualcomm Snapdragon 8 Gen 3 or newer). Thus, approximately 2.25 billion high-end smartphones exist today (30% of 7.5B). Step 2: Aggregate Compute Power Estimation (Idle Capacity) Average Computational Capacity per High-End Smartphone: A high-end phone has roughly: CPU: ~1 to 1.5 TFLOPS GPU: ~1.5 to 2 TFLOPS Average Idle Compute per Phone: 1 TFLOPS (CPU) + 1.5 TFLOPS (GPU) = ~2.5 TFLOPS idle. Total Potential Compute Power: 2.25B smartphones × 2.5 TFLOPS each ≈ 5,625,000,000 TFLOPS (5.625 ExaFLOPS) Comparison to Cloud Vendors: Amazon AWS, Microsoft Azure, Google Cloud combined currently deploy approximately ~1 to 2 ExaFLOPS of continuous computing power. Thus, aggregate idle compute power from high-end smartphones (5.625 ExaFLOPS) exceeds the largest cloud vendors combined by at least 2.8x. Step 3: Proposed Business Model ("Zero Margin Trustless Model") Following Middleton’s economic principles, a decentralized marketplace based on his IP (SmartMetal Rounds and patented protocols) would allow individual users to rent their smartphones’ idle compute power. The economics would follow: Revenue Structure: Compute resources provided by phone owners (children, elderly, economically disadvantaged communities) rented to consumers (AI firms, universities, research institutions, enterprises). Offered at 10% above net cost ("as close to free as possible" per the attached article​Executive Global articl…). Revenue Distribution: SmartMetal Owners (phone owners): Receive 20% of net revenue generated. Platform Cost & Overhead: Costs for electricity, network management, and maintenance (approximately 70% of net revenue). Intellectual Property Licensing (Middleton’s IP): A modest licensing fee—around 10% (aligned with Middleton’s zero-margin, IP-licensing-centric model). Step 4: Revenue Estimation Example Assumptions: Average monthly idle compute contribution per phone: 4 hours/day, 30 days = 120 hours/month. Market price for decentralized high-performance computing: approximately $0.10 per TFLOP-hour. Revenue per Smartphone per Month: Compute provided: 2.5 TFLOPS × 120 hrs = 300 TFLOP-hours Revenue at $0.10 per TFLOP-hour: 300 × $0.10 = $30/month per smartphone Aggregate Monthly and Annual Revenue: Monthly revenue (2.25 billion phones): $30 × 2.25B ≈ $67.5 billion Annual revenue potential: $67.5B × 12 months = $810 billion annually Distribution of Annual Revenue: SmartMetal Round Owners (20%): $810B × 20% ≈ $162 billion/year Operational Cost (70%): $810B × 70% ≈ $567 billion/year Middleton IP Licensing (10%): $810B × 10% ≈ $81 billion/year Thus, the total economic benefit is substantial, particularly transformative for economically disadvantaged participants (children, elderly, developing regions). Step 5: Practical Impact & Social Value Impact on Children & Young Adults: Empowerment through earning potential (around $360 annually per child smartphone owner). Practical, intuitive introduction to economics, technology, and entrepreneurship through gamified interfaces and secure, decentralized platforms. Impact on Elderly and Economically Disadvantaged Communities: Significant supplemental income (potentially exceeding many pension plans or assistance programs). Bridging the technology gap, ensuring inclusive participation in global digital economies. Step 6: Strategic Value & Market Positioning Middleton's patented Zero Margin Trustless Model ("ZMTM")​Executive Global articl… creates a highly attractive, low-cost computational offering. Competing directly with incumbent cloud providers: The computational marketplace can massively disrupt cloud computing with lower fees and broader global reach. Leveraging Middleton’s IP and SmartMetal Rounds, it creates defensible competitive barriers and immense value for early adopters. Step 7: Driving Middleton’s Peer-to-Peer Economy As described in Middleton’s vision​Executive Global articl…, this marketplace underpins a global peer-to-peer economy, transforming idle smartphone resources into meaningful economic output. The P2P economy will leverage: AI-driven autonomous economic agents. Secure blockchain-based IP rights enforcement. Economic democratization by redistributing traditional cloud revenues directly to everyday device owners. Summary & Strategic Conclusion Implementing a decentralized compute platform powered by high-end smartphones and Middleton’s patented Zero Margin Trustless Model presents enormous economic potential, far exceeding current major cloud vendors combined. With annual revenues estimated up to $810 billion, and meaningful income distribution to disadvantaged demographics, this innovative model could dramatically reshape the global computational economy, achieve significant social impacts, and provide the backbone for Middleton’s envisioned peer-to-peer decentralized economy.
8:25

This is part 2 of a 2 part post (see part 1 here Below is a structured analysis to demonstrate the validity of using buyers of Veritaseum #SmartMetal to buy into and sell compute from globally aggregated cell phone compute pools - directly compeiting with the big guys - Google, Amazon and Microsoft cloud businesses. We discuss estimates, business model propositions, and potential economic outcomes, but first, see my Executive Global Article on Zero Profit Models( and purchase Veritaseum SmartMetal here - Can you really disintermediate the most profitable revnues of t $6.7 trillion worth of technology cloud providers? Well, the fact that it is among, if not the, most profitable of their revenue drivers is a very material clue! Step 1: Estimating the Number of High-End Smartphones Globally As of early 2025, approximately 7.5 billion smartphones are actively used worldwide. Considering that: About 30% of global smartphones are high-end (comparable or superior to an iPhone X; for instance, Samsung Galaxy S22/S23 Ultra, iPhone 16 Pro Max with A18 chips, and Qualcomm Snapdragon 8 Gen 3 or newer). Thus, approximately 2.25 billion high-end smartphones exist today (30% of 7.5B). Step 2: Aggregate Compute Power Estimation (Idle Capacity) Average Computational Capacity per High-End Smartphone: A high-end phone has roughly: CPU: ~1 to 1.5 TFLOPS GPU: ~1.5 to 2 TFLOPS Average Idle Compute per Phone: 1 TFLOPS (CPU) + 1.5 TFLOPS (GPU) = ~2.5 TFLOPS idle. Total Potential Compute Power: 2.25B smartphones × 2.5 TFLOPS each ≈ 5,625,000,000 TFLOPS (5.625 ExaFLOPS) Comparison to Cloud Vendors: Amazon AWS, Microsoft Azure, Google Cloud combined currently deploy approximately ~1 to 2 ExaFLOPS of continuous computing power. Thus, aggregate idle compute power from high-end smartphones (5.625 ExaFLOPS) exceeds the largest cloud vendors combined by at least 2.8x. Step 3: Proposed Business Model ("Zero Margin Trustless Model") Following Middleton’s economic principles, a decentralized marketplace based on his IP (SmartMetal Rounds and patented protocols) would allow individual users to rent their smartphones’ idle compute power. The economics would follow: Revenue Structure: Compute resources provided by phone owners (children, elderly, economically disadvantaged communities) rented to consumers (AI firms, universities, research institutions, enterprises). Offered at 10% above net cost ("as close to free as possible" per the attached article​Executive Global articl…). Revenue Distribution: SmartMetal Owners (phone owners): Receive 20% of net revenue generated. Platform Cost & Overhead: Costs for electricity, network management, and maintenance (approximately 70% of net revenue). Intellectual Property Licensing (Middleton’s IP): A modest licensing fee—around 10% (aligned with Middleton’s zero-margin, IP-licensing-centric model). Step 4: Revenue Estimation Example Assumptions: Average monthly idle compute contribution per phone: 4 hours/day, 30 days = 120 hours/month. Market price for decentralized high-performance computing: approximately $0.10 per TFLOP-hour. Revenue per Smartphone per Month: Compute provided: 2.5 TFLOPS × 120 hrs = 300 TFLOP-hours Revenue at $0.10 per TFLOP-hour: 300 × $0.10 = $30/month per smartphone Aggregate Monthly and Annual Revenue: Monthly revenue (2.25 billion phones): $30 × 2.25B ≈ $67.5 billion Annual revenue potential: $67.5B × 12 months = $810 billion annually Distribution of Annual Revenue: SmartMetal Round Owners (20%): $810B × 20% ≈ $162 billion/year Operational Cost (70%): $810B × 70% ≈ $567 billion/year Middleton IP Licensing (10%): $810B × 10% ≈ $81 billion/year Thus, the total economic benefit is substantial, particularly transformative for economically disadvantaged participants (children, elderly, developing regions). Step 5: Practical Impact & Social Value Impact on Children & Young Adults: Empowerment through earning potential (around $360 annually per child smartphone owner). Practical, intuitive introduction to economics, technology, and entrepreneurship through gamified interfaces and secure, decentralized platforms. Impact on Elderly and Economically Disadvantaged Communities: Significant supplemental income (potentially exceeding many pension plans or assistance programs). Bridging the technology gap, ensuring inclusive participation in global digital economies. Step 6: Strategic Value & Market Positioning Middleton's patented Zero Margin Trustless Model ("ZMTM")​Executive Global articl… creates a highly attractive, low-cost computational offering. Competing directly with incumbent cloud providers: The computational marketplace can massively disrupt cloud computing with lower fees and broader global reach. Leveraging Middleton’s IP and SmartMetal Rounds, it creates defensible competitive barriers and immense value for early adopters. Step 7: Driving Middleton’s Peer-to-Peer Economy As described in Middleton’s vision​Executive Global articl…, this marketplace underpins a global peer-to-peer economy, transforming idle smartphone resources into meaningful economic output. The P2P economy will leverage: AI-driven autonomous economic agents. Secure blockchain-based IP rights enforcement. Economic democratization by redistributing traditional cloud revenues directly to everyday device owners. Summary & Strategic Conclusion Implementing a decentralized compute platform powered by high-end smartphones and Middleton’s patented Zero Margin Trustless Model presents enormous economic potential, far exceeding current major cloud vendors combined. With annual revenues estimated up to $810 billion, and meaningful income distribution to disadvantaged demographics, this innovative model could dramatically reshape the global computational economy, achieve significant social impacts, and provide the backbone for Middleton’s envisioned peer-to-peer decentralized economy.

Reggie Middleton, Disruptor-in-Chief

14,751 Aufrufe • vor 1 Jahr

ChatGPT: Opens a new tab, you copy-paste. Siri: Transcribes text, you still do the work. Lemon: Completes the entire task while you keep working. Traditional voice assistants are convenient to use. But they cannot complete tasks for you. That's why Lemon stands out. Lemon is an AI agent that completes tasks from voice commands, without opening new tabs or switching apps. Press the fn key. Say what you need done. Watch it happen: ➟ "Reply to Sarah's email and tell her I'll have the report by Friday" — sent ➟ "Create a project brief for the new client" — done ➟ "Search for the latest stats on AI adoption" — on your screen ➟ "Make this email sound more professional" — edited ➟ "Give me three angles for this campaign" — ideas generated ➟ "Write this paragraph in a more casual tone" — rewritten No new tabs. No manual steps. No switching back and forth. Lemon works across every app on your Mac. The results: ✓ Users save 2+ hours daily ✓ Reply to emails 12x faster ✓ Open 70% fewer tabs ✓ Type 5x faster than traditional workflows Your voice isn't just for dictation anymore. It's a faster way to complete your work tasks. 📌 Try Lemon (it's free for 30 days): 🔄 Repost to help others work faster — #AI #Agent #LemonPartnership
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ChatGPT: Opens a new tab, you copy-paste. Siri: Transcribes text, you still do the work. Lemon: Completes the entire task while you keep working. Traditional voice assistants are convenient to use. But they cannot complete tasks for you. That's why Lemon stands out. Lemon is an AI agent that completes tasks from voice commands, without opening new tabs or switching apps. Press the fn key. Say what you need done. Watch it happen: ➟ "Reply to Sarah's email and tell her I'll have the report by Friday" — sent ➟ "Create a project brief for the new client" — done ➟ "Search for the latest stats on AI adoption" — on your screen ➟ "Make this email sound more professional" — edited ➟ "Give me three angles for this campaign" — ideas generated ➟ "Write this paragraph in a more casual tone" — rewritten No new tabs. No manual steps. No switching back and forth. Lemon works across every app on your Mac. The results: ✓ Users save 2+ hours daily ✓ Reply to emails 12x faster ✓ Open 70% fewer tabs ✓ Type 5x faster than traditional workflows Your voice isn't just for dictation anymore. It's a faster way to complete your work tasks. 📌 Try Lemon (it's free for 30 days): 🔄 Repost to help others work faster — #AI #Agent #LemonPartnership

Andrew Bolis

91,782 Aufrufe • vor 5 Monaten