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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!

AWS Amplify

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18,403 просмотров • 2 лет назад •via X (Twitter)

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Фото профиля Emmanuel Chucks 🇬🇭

Emmanuel Chucks 🇬🇭2 лет назад

Oh my! I'm too hyped

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Automated GitHub, GitLab, Bitbucket and Jira backups, security compliance, data migration and every-scenario-ready Disaster Recovery for 360 cyber resilience. Schedule a custom demo or try 14 days for free.

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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 💜

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 просмотров • 2 лет назад

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.

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 просмотров • 4 месяцев назад

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:

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

39,294 просмотров • 3 месяцев назад

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.

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

145,146 просмотров • 1 месяц назад

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.

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

569,490 просмотров • 11 месяцев назад

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!!

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

312,788 просмотров • 4 месяцев назад

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.

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 просмотров • 3 месяцев назад

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 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 просмотров • 4 месяцев назад

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

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

141,694 просмотров • 3 месяцев назад

BREAKING $AMD is ready to ship Ryzen AI Halo 🆕 in June 2026!!!! 🚀🚀🚀 AKA Personal/Local AI system. Ryzen AI Max+ 395 (Strix Halo APU) with 128 GB of unified LPDDR5X memory, 40 RDNA 3.5 GPU compute units, and a full ROCm software stack preloaded for both Windows and Linux. AMD Halo is $2,000-$3,000 vs $NVDA Spark $4,699 The main purpose of the AMD Ryzen AI Halo is to serve as a compact, developer focused workstation for running intensive AI workloads locally, specifically building, testing, prototyping, fine-tuning, and deploying generative AI models (like LLMs) and Agentic AI applications without relying on the cloud. Run models with up to 200 billion parameters on your desk using 128 GB of unified memory (shared across CPU, GPU, and NPU). This eliminates cloud costs, latency, data privacy concerns, and internet dependency. ~This is for AI developers, researchers, engineers, and creators. ~Teams or individuals wanting faster iteration, data privacy, and cost savings compared to cloud APIs. ~Users experimenting with open-source models, fine-tuning, inference, and production-like local deployment Source: AMD CES 2026

BREAKING $AMD is ready to ship Ryzen AI Halo 🆕 in June 2026!!!! 🚀🚀🚀 AKA Personal/Local AI system. Ryzen AI Max+ 395 (Strix Halo APU) with 128 GB of unified LPDDR5X memory, 40 RDNA 3.5 GPU compute units, and a full ROCm software stack preloaded for both Windows and Linux. AMD Halo is $2,000-$3,000 vs $NVDA Spark $4,699 The main purpose of the AMD Ryzen AI Halo is to serve as a compact, developer focused workstation for running intensive AI workloads locally, specifically building, testing, prototyping, fine-tuning, and deploying generative AI models (like LLMs) and Agentic AI applications without relying on the cloud. Run models with up to 200 billion parameters on your desk using 128 GB of unified memory (shared across CPU, GPU, and NPU). This eliminates cloud costs, latency, data privacy concerns, and internet dependency. ~This is for AI developers, researchers, engineers, and creators. ~Teams or individuals wanting faster iteration, data privacy, and cost savings compared to cloud APIs. ~Users experimenting with open-source models, fine-tuning, inference, and production-like local deployment Source: AMD CES 2026

Mike

16,097 просмотров • 3 месяцев назад

If you run an independent store, here's a question worth asking — how many orders are you losing every single day? In Episode 2 of our series (2/3), we show you how to build and deploy an AI customer support Agent for cross-border e-commerce with EdgeOne Makers. 💡 What you'll see: • Real case: Tencent Cloud Kiki — 2,000 users on day one, deployed with one sentence • End-to-end demo: Add an AI customer Agent to your store • No rewrite — integrate Agent capabilities without changing your existing architecture Check the comments for event perks — join now and get up to 50M Tokens!

If you run an independent store, here's a question worth asking — how many orders are you losing every single day? In Episode 2 of our series (2/3), we show you how to build and deploy an AI customer support Agent for cross-border e-commerce with EdgeOne Makers. 💡 What you'll see: • Real case: Tencent Cloud Kiki — 2,000 users on day one, deployed with one sentence • End-to-end demo: Add an AI customer Agent to your store • No rewrite — integrate Agent capabilities without changing your existing architecture Check the comments for event perks — join now and get up to 50M Tokens!

Tencent EdgeOne

1,604,490 просмотров • 2 месяцев назад

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.

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 просмотров • 10 месяцев назад

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:

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

42,810 просмотров • 1 год назад

ByteDance Seed delivered again. They released EdgeBench, to test whether AI agents can improve through experience, using 134 real-world tasks that run for at least 12 hours. The big deal is that it shifts AI evaluation from “what does the model already know?” to “can the model learn while doing real work?” Huge, because future AI agents will not just answer questions from training data. They will enter messy environments, use tools, make attempts, read feedback, fix mistakes, and slowly build better solutions. Most current benchmarks are too short for that, so they mostly test memory, coding skill, or one-shot reasoning. EdgeBench instead gives agents 12-hour real-world tasks with feedback loops, so it can measure whether the agent improves through experience. Each task has a local workspace for fast trial and error, plus a hidden judge that gives stronger feedback on submitted work, which is meant to feel closer to real expert work. The authors then ran frontier agents for about 38,000 total hours and tracked how their best score changed as they kept interacting with the task environment. The big result is that when scores are averaged across many tasks, learning follows a very clean log-sigmoid curve, meaning progress is slow, then faster, then starts to level off. They also found that newer agents seem to learn from environments much faster, with the top models roughly doubling their 2-hour learning speed every 3 months.

ByteDance Seed delivered again. They released EdgeBench, to test whether AI agents can improve through experience, using 134 real-world tasks that run for at least 12 hours. The big deal is that it shifts AI evaluation from “what does the model already know?” to “can the model learn while doing real work?” Huge, because future AI agents will not just answer questions from training data. They will enter messy environments, use tools, make attempts, read feedback, fix mistakes, and slowly build better solutions. Most current benchmarks are too short for that, so they mostly test memory, coding skill, or one-shot reasoning. EdgeBench instead gives agents 12-hour real-world tasks with feedback loops, so it can measure whether the agent improves through experience. Each task has a local workspace for fast trial and error, plus a hidden judge that gives stronger feedback on submitted work, which is meant to feel closer to real expert work. The authors then ran frontier agents for about 38,000 total hours and tracked how their best score changed as they kept interacting with the task environment. The big result is that when scores are averaged across many tasks, learning follows a very clean log-sigmoid curve, meaning progress is slow, then faster, then starts to level off. They also found that newer agents seem to learn from environments much faster, with the top models roughly doubling their 2-hour learning speed every 3 months.

Rohan Paul

14,309 просмотров • 2 месяцев назад

✨ Managed to load my custom GPT script into an open source LLM called OpenHermes 2.5 Mistral 7B I used which makes it super easy to run open source LLMs on your laptop without any cloud servers or OpenAI or ChatGPT, just your own GPU in your MacBook! I use my MBP's M1 GPU! It runs maybe 2x-3x faster than ChatGPT4 and with about the same quality, no image gen of course cuz no DALL-E but with LCM (1 second img gen LoRa) that could happen in future If OpenAI implodes we might be able to continue after all thanks to open source! Nice to try this and see how accessible it is, here's how it looks: Thx for tips in replies 🐜 Max Rovensky MrDee@SOG🫡 Xenon John Shahawy @the_carlosdp Bilal KY @bigpt_dog

✨ Managed to load my custom GPT script into an open source LLM called OpenHermes 2.5 Mistral 7B I used which makes it super easy to run open source LLMs on your laptop without any cloud servers or OpenAI or ChatGPT, just your own GPU in your MacBook! I use my MBP's M1 GPU! It runs maybe 2x-3x faster than ChatGPT4 and with about the same quality, no image gen of course cuz no DALL-E but with LCM (1 second img gen LoRa) that could happen in future If OpenAI implodes we might be able to continue after all thanks to open source! Nice to try this and see how accessible it is, here's how it looks: Thx for tips in replies 🐜 Max Rovensky MrDee@SOG🫡 Xenon John Shahawy @the_carlosdp Bilal KY @bigpt_dog

@levelsio

745,229 просмотров • 2 лет назад

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!

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

29,245 просмотров • 5 месяцев назад

We just crossed 100k downloads of OpenWhispr! 🤯 This feels like a good time for a reminder of why we’re building it. Your ideas and conversations are becoming more valuable, while software is becoming easier to build than ever. But that creates some worrying incentives for existing software companies: use your proprietary data to train better AI models to stay competitive, and lock that data behind walled gardens to keep you from leaving. We think that’s pretty shitty. You should stay because a product is useful, not because your data is stuck there. If billions of people are going to feel comfortable sharing their most private information when talking to AI, and organizations are going to trust it with their work, we have to earn that trust. For us, that starts with being fully transparent about how your data is handled, and giving you full control over it. That’s what we’re building with OpenWhispr. You can run models on your own device, use your own cloud provider, or use our cloud service. We’re not anti-cloud. We just think it should be your choice. We’re still super early, but I’m genuinely excited to bring you along for more of this. This is the first in a series of videos sharing the new features we’re building, what we’re figuring out, and the team behind it. And if you’re still here reading, we’re officially hiring for two really important roles: - Founding Engineering Lead - Social / Brand / Growth Creator You can find more info about both on our website!

Gabe Stein

12,282 просмотров • 7 дней назад

If you’ve ever opened Chrome DevTools, or optimized a page for Core Web Vitals, you’ve used software built by Addy Osmani. Timestamps: 00:00 Intro 02:50 Addy’s current workflow 05:11 Addy’s path into tech 15:04 Addy’s work on jQuery 16:44 TodoMVC 21:44 Getting hired at Google and working on Chrome 27:17 Building dev tools 40:15 Core Web Vitals 45:42 Google’s engineering culture 51:03 Addy’s career trajectory at Google 57:55 The director role at Google 1:01:40 Cognitive debt and cognitive surrender 1:03:03 Working with agents 1:05:52 Loop engineering 1:12:55 The changing role of the software engineer 1:18:15 How Addy uses AI in writing 1:27:40 What’s next for Addy 1:28:47 Career advice Brought to you by: • Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. Teams like Jane Street, and the etcd community use Antithesis to ship better code, faster. • Sentry – application monitoring software considered “not bad” by millions of developers. • Google Cloud Run – run untrusted agent code without the security anxiety. Cloud Run sandboxes deliver hyper-isolated, ephemeral execution environments that spin up in milliseconds. Check them out: Here's Addy's advice on where he believes engineers should invest efforts, in the coming years, in his words: “What we are very likely to see happen next with engineering careers (as well as product and other roles) is the unbundling of them, so that an engineer also has product sense, while a product person also has engineering sense, or UX sense. You should think about the non-engineering things if you don’t [usually] have the time to think about product or technical evangelism, or go-to-market approaches, or any other parts of how businesses are successful. If you can show employers that you are not just a builder, but someone that can help them as roles start to become a little bit fuzzier, then I think that you can be successful in these times. Don’t be just an engineer.”

If you’ve ever opened Chrome DevTools, or optimized a page for Core Web Vitals, you’ve used software built by Addy Osmani. Timestamps: 00:00 Intro 02:50 Addy’s current workflow 05:11 Addy’s path into tech 15:04 Addy’s work on jQuery 16:44 TodoMVC 21:44 Getting hired at Google and working on Chrome 27:17 Building dev tools 40:15 Core Web Vitals 45:42 Google’s engineering culture 51:03 Addy’s career trajectory at Google 57:55 The director role at Google 1:01:40 Cognitive debt and cognitive surrender 1:03:03 Working with agents 1:05:52 Loop engineering 1:12:55 The changing role of the software engineer 1:18:15 How Addy uses AI in writing 1:27:40 What’s next for Addy 1:28:47 Career advice Brought to you by: • Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. Teams like Jane Street, and the etcd community use Antithesis to ship better code, faster. • Sentry – application monitoring software considered “not bad” by millions of developers. • Google Cloud Run – run untrusted agent code without the security anxiety. Cloud Run sandboxes deliver hyper-isolated, ephemeral execution environments that spin up in milliseconds. Check them out: Here's Addy's advice on where he believes engineers should invest efforts, in the coming years, in his words: “What we are very likely to see happen next with engineering careers (as well as product and other roles) is the unbundling of them, so that an engineer also has product sense, while a product person also has engineering sense, or UX sense. You should think about the non-engineering things if you don’t [usually] have the time to think about product or technical evangelism, or go-to-market approaches, or any other parts of how businesses are successful. If you can show employers that you are not just a builder, but someone that can help them as roles start to become a little bit fuzzier, then I think that you can be successful in these times. Don’t be just an engineer.”

Gergely Orosz

398,385 просмотров • 28 дней назад

🎮 Computer Use Agent Arena is LIVE! 🚀 🔥 Easiest way to test computer-use agents in the wild without any setup 🌟 Compare top VLMs: OpenAI Operator, Claude 3.7, Gemini 2.5 Pro, Qwen 2.5 vl and more 🕹️ Test agents on 100+ real apps & webs with one-click config 🔒 Safe & free access on cloud-hosted machines Page: Leaderboard (tentative): Blog: Data & Code (coming soon): ⭐️Why Computer Agent Arena? 1️⃣Beyond Static Benchmarks: We use computers to perform enormous tasks and workflows every day, and AI agents have the potential to automate these tasks. However, existing benchmarks are very limited (e.g., only 369 tasks in OSWorld and 812 tasks in WebArena). To better measure their capabilities, we introduce Computer Agent Arena for users to easily compare & test AI agents on all kinds of crowdsourced real-world computer use tasks. 2️⃣Cloud Testing, Simplified: As agents like OpenAI’s Operator and Claude 3.7 sonnet release, users face configuration challenges and privacy hurdles to deploy on their own computers. Our platform integrates these agents with cloud-hosted machines, providing users with quick and secure access. 3️⃣Unified Embodied Digital Environment: Unlike Chatbot Arena, we provide users with a real embodied environment—computers—where all agents are grounded in real computer tasks and environments. Led by @XLANG_Lab [1/🧵]

🎮 Computer Use Agent Arena is LIVE! 🚀 🔥 Easiest way to test computer-use agents in the wild without any setup 🌟 Compare top VLMs: OpenAI Operator, Claude 3.7, Gemini 2.5 Pro, Qwen 2.5 vl and more 🕹️ Test agents on 100+ real apps & webs with one-click config 🔒 Safe & free access on cloud-hosted machines Page: Leaderboard (tentative): Blog: Data & Code (coming soon): ⭐️Why Computer Agent Arena? 1️⃣Beyond Static Benchmarks: We use computers to perform enormous tasks and workflows every day, and AI agents have the potential to automate these tasks. However, existing benchmarks are very limited (e.g., only 369 tasks in OSWorld and 812 tasks in WebArena). To better measure their capabilities, we introduce Computer Agent Arena for users to easily compare & test AI agents on all kinds of crowdsourced real-world computer use tasks. 2️⃣Cloud Testing, Simplified: As agents like OpenAI’s Operator and Claude 3.7 sonnet release, users face configuration challenges and privacy hurdles to deploy on their own computers. Our platform integrates these agents with cloud-hosted machines, providing users with quick and secure access. 3️⃣Unified Embodied Digital Environment: Unlike Chatbot Arena, we provide users with a real embodied environment—computers—where all agents are grounded in real computer tasks and environments. Led by @XLANG_Lab [1/🧵]

Bowen Wang

93,200 просмотров • 1 год назад

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:

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

125,385 просмотров • 7 месяцев назад