# cloud-computing Cloud computing is a model providing on-demand network access to shared, configurable computing resources that can be rapidly provisioned and released. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## APNIC 62 keynotes explore automation, trust, and the future of the Internet DevFeed: [APNIC 62 keynotes explore automation, trust, and the future of the Internet]() Original publisher: [Read original article]() Author: Dan Fidler Published: 2026-08-13T05:57:46Z Content type: article Language: en Sources: [APNIC Blog]() Topics: [Automation](), [Internet](), [Network](), [networking](), [Cybersecurity](), [Incident response](), [Threat Research](), [Cloud](), [cloud-computing](), [telemetry](), [Resilience](), [Latency]() Tags: [ai](), [apnic-62](), [automation](), [cloud](), [community](), [cybersecurity](), [edge-computing](), [events](), [incident-response](), [internet](), [latency](), [network](), [networking](), [real-time](), [resilience](), [routing](), [security](), [technology](), [telemetry]() ### AI overview APNIC 62 will feature keynotes on autonomous network operations and trusted cybersecurity collaboration. The article describes automation, software-defined infrastructure, real-time telemetry, automated routing, self-healing fibre architectures, and zero-touch operations as ways to improve Internet resilience and performance, alongside the importance of cooperation among cybersecurity incident response teams. ### Source excerpt APNIC 62 will explore two essential foundations of a resilient Internet: Intelligent network automation and trusted cybersecurity collaboration. Keynotes from Amajit Gupta and Yukako Uchida offer complementary perspectives on how technology and human relationships will shape the Internet's future. ## The fuel of the future is already here: Why TRISO matters DevFeed: [The fuel of the future is already here: Why TRISO matters]() Original publisher: [Read original article]() Author: Katy Huff Published: 2026-06-24T19:57:09Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [AI Infrastructure](), [amazon](), [cloud-computing]() Tags: [advanced-nuclear-reactors](), [advanced-reactor-fuel-fabrication](), [ai-infrastructure](), [amazon](), [amazon-nuclear-energy](), [clean-energy-data-centers](), [cloud-computing](), [decarbonization](), [durability](), [energy](), [haleu](), [integrity](), [modular-nuclear-reactors](), [nuclear-energy-for-ai](), [physical-science](), [renewable-energy](), [resilience](), [safety](), [science](), [sustainability](), [triso-fuel](), [triso-particles](), [x-energy-xe-100]() ### AI overview Amazon explains how TRISO nuclear fuel particles use layered carbon and ceramic coatings to contain radioactive byproducts and withstand extreme temperatures. The article connects the technology to rising energy demands from AI infrastructure and cloud computing, citing testing that found no detectable failures at 1600°C for 300 hours. ### Source excerpt Millimeter-scale particles of nuclear-reactor fuel are encased in four layers of different materials that act as a "miniature containment system". ## A low-carbon computing platform from your retired phones DevFeed: [A low-carbon computing platform from your retired phones]() Original publisher: [Read original article]() Published: 2026-06-12T17:37:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Green Software](), [cloud-computing](), [datacenter](), [Hardware](), [cpu](), [Google]() Tags: [climate-sustainability](), [cloud](), [cloud-computing](), [compute](), [datacenter](), [distributed-systems-parallel-computing](), [efficiency](), [energy](), [energy-efficiency](), [google](), [hardware](), [memory](), [performance](), [servers](), [smartphones](), [storage](), [sustainability]() ### AI overview Researchers at the University of California San Diego, with Google's support, are developing a low-carbon cloud computing platform from retired smartphones. A planned datacenter using 2,000 Pixel smartphones aims to provide low-cost computing while reducing the need for newly manufactured hardware and its associated emissions. ### Source excerpt Climate & Sustainability ## Types of Cloud Service Models DevFeed: [Types of Cloud Service Models]() Original publisher: [Read original article]() Author: Sumit Malviya Published: 2026-03-12T07:01:39Z Content type: tutorial Language: en Sources: [Backend Archives | Talent500 blog]() Topics: [cloud-computing](), [paas and iaas](), [Software as a service](), [Architecture & Design](), [Multi-tenancy]() Tags: [advantages-and-disadvantages-of-cloud-service-models](), [architecture](), [backend](), [best-practices-for-implementation-of-cloud-models](), [choosing-the-right-cloud-service-model](), [cloud](), [cloud-computing](), [comparing-saas-paas-and-iaas](), [conclusion](), [container-as-a-service-caas](), [emerging-cloud-service-models](), [function-as-a-service-faas](), [future-trends-in-cloud-service-models](), [infrastructure-as-a-service-iaas](), [paas-and-iaas](), [platform-as-a-service-paas](), [saas](), [scalability](), [software-as-a-service-saas](), [the-three-main-cloud-service-models](), [types-of-cloud-service-models](), [use-cases-for-every-cloud-service-model]() ### AI overview This article explains the main cloud service delivery models: Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). It also introduces emerging models such as FaaS and CaaS and discusses SaaS tenancy choices. ### Source excerpt Cloud computing has fundamentally changed how organizations build, deploy, and scale IT systems. Instead of investing heavily in on-premises hardware [...] The post Types of Cloud Service Models appeared first on Talent500 blog. ## Scheduling in a changing world: Maximizing throughput with time-varying capacity DevFeed: [Scheduling in a changing world: Maximizing throughput with time-varying capacity]() Original publisher: [Read original article]() Published: 2026-02-11T10:34:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Algorithms & Theory](), [cloud-infrastructure](), [cloud-computing](), [Google]() Tags: [algorithms](), [algorithms-theory](), [cloud-computing](), [cloud-infrastructure](), [google](), [hardware](), [infrastructure](), [scale]() ### AI overview Google Research presents approximation algorithms for scheduling non-preemptive jobs when cloud capacity varies over time. The work targets throughput maximization in volatile environments where interruptions can erase progress. ### Source excerpt Algorithms & Theory ## Leaving Intel DevFeed: [Leaving Intel]() Original publisher: [Read original article]() Published: 2025-12-04T13:00:00Z Content type: opinion Language: en Sources: [Brendan Gregg's Blog]() Topics: [intel](), [Artificial Intelligence](), [GPU](), [eBPF](), [Cloud](), [cloud-computing](), [Linux](), [Open Source](), [Monitoring]() Tags: [ai](), [blog](), [cloud-computing](), [cpu](), [ebpf](), [gpu](), [intel](), [leaving](), [linux](), [monitoring](), [open-source](), [performance]() ### AI overview The author announces his departure from Intel after 3.5 years and reflects on his work there, including open-source AI flame graphs, GPU performance visualization, Linux stack walking, eBPF security monitoring, conference leadership, customer meetings, and a company-wide cloud strategy. ### Source excerpt InnovatiON 2022 AI Flame Graphs GPU Flame Scope Harshad Sane SREcon APAC Cloud strategy Last day I've resigned from Intel and accepted a new opportunity. If you are an Intel employee, you might have seen my fairly long email that summarized what I did in my 3.5 years. Much of this is public: AI flame graphs and released them as open source GPU subsecond-offset heatmap Worked with Linux distros to enable stack walking Was interviewed by the WSJ about eBPF for security monitoring Provided leadership on the eBPF Technical Steering Committee (BSC) Co-chaired USENIX SREcon APAC 2023 Gave 6 conference keynotes It's still early days for AI flame graphs. Right now when I browse CPU performance case studies on the Internet, I'll often see a CPU flame graph as part of the analysis. We're a long way from that kind of adoption for GPUs (and it doesn't help that our open source version is Intel only), but I think as GPU code becomes more complex, with more layers, the need for AI flame graphs will keep increasing. I also supported cloud computing, participating in 110 customer meetings, and created a company-wide strategy to win back the cloud with 33 specific recommendations, in collaboration with others across 6 organizations. It is some of my best work and features a visual map of interactions between all 19 relevant teams, described by Intel long-timers as the first time they have ever seen such a cross-company map. (This strategy, summarized in a slide deck, is internal only.) I always wish I did more, in any job, but I'm glad to have contributed this much especially given the context: I overlapped with Intel's toughest 3 years in history, and I had a hiring freeze for my first 15 months. My fond memories from Intel include meeting Linus at an Intel event who said "everyone is using fleme graphs these days" (Finnish accent), meeting Pat Gelsinger who knew about my work and introduced me to everyone at an exec all hands, surfing lessons at an Intel Australia and HP offsite ( ## Solving virtual machine puzzles: How AI is optimizing cloud computing DevFeed: [Solving virtual machine puzzles: How AI is optimizing cloud computing]() Original publisher: [Read original article]() Published: 2025-10-17T17:56:35Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [cloud-computing](), [Algorithms & Theory](), [data centers](), [servers](), [Provisioning]() Tags: [2025](), [ai](), [algorithms](), [algorithms-theory](), [cloud](), [cloud-computing](), [distributed-systems-parallel-computing](), [efficiency](), [google](), [packing](), [provisioning](), [research](), [resource](), [scale](), [server](), [servers](), [software-systems-engineering](), [virtual-machines]() ### AI overview Google Research presents LAVA, a scheduling system that uses AI models to continuously predict and adapt to virtual machine lifetimes. Its NILAS, LAVA, and LARS algorithms improve VM allocation and rescheduling in large cloud data centers, reducing stranded resources and improving server efficiency. ### Source excerpt Algorithms & Theory ## Cangjie -- a programming language from Huawei DevFeed: [Cangjie -- a programming language from Huawei]() Original publisher: [Read original article]() Author: Jasper Morgan Published: 2025-08-19T07:37:53Z Content type: opinion Language: en Sources: [Snapp Mobile - Medium]() Topics: [Programming language](), [huawei](), [Programming](), [Code](), [Embedded Systems](), [Cross-Compilation](), [Mobile](), [cloud-computing](), [Package Management](), [debug](), [Testing](), [ide]() Tags: [architectures](), [bytecode](), [cangjie](), [china](), [cloud](), [cross-compilation](), [debugging](), [developers](), [development](), [embedded-systems](), [huawei](), [huawei-developers](), [language](), [mobile](), [package-management](), [programming](), [programming-language](), [programming-languages](), [tools]() ### AI overview This article examines Cangjie, an open-sourced programming language from Huawei. It describes Cangjie as a C-like language with features associated with Swift and Kotlin, support for embedded systems, mobile development, and cloud architectures, interoperability with C and Python, JVM bytecode compilation, cross-compilation, and integrated developer tools. The article suggests Huawei created it to gain more control over its language roadmap and consolidate development skills across products. ### Source excerpt Cangjie -- a programming language from Huawei Huawei have been busy. Last month they open-sourced a programming language that is looking pretty interesting. Whilst you might not be writing in Cangjie next week, it's good to see what one of the world's largest technology companies is doing such that they have created a new programming language. TL;DR Cangjie is a C-like programming language that feels more like Swift/Kotlin than Java/C# or JavaScript/Typescript. The idea is for Cangjie to become the common programming language to be used throughout Huawei. Cangjie can be used for embedded systems, mobile development, server-side cloud architectures. It supports a variety of programming paradigms (function, OO, procedural), compilation and integration options. Cangjie can call C libraries (and be called from C), use Python libraries and even compile to JVM bytecode. Cangjie supports language extension to create lightweight DSLs. Primarily Cangjie programs are compiled into machine code -- this include cross compilation which mean they can run natively on different computer architectures. Cangjie comes with a set of in-built tools for developers such as package management, debugging, testing and IDE integration. Cangjie is openly available since this month and is in version 0.53.13. Why create a new language? Simply put, Cangjie gives Huawei a level of control and an economy of scale that they cannot get from other languages. Most mainstream languages are driven predominantly by teams in USA and Europe. It is hard to influence the road map for these languages -- especially for those tightly controlled by corporations (e.g. Java/Kotlin/Swift). Furthermore, consolidating programming skills could be a big efficiency gain for Huawei -- engineers working on products ranging from routers to petabyte storage to cloud computing and mobile devices can coalesce around one language and toolset. Show me some code I won't go into any detail here so this is just to satisfy your curiosit ## Supabase UI: Platform Kit DevFeed: [Supabase UI: Platform Kit]() Original publisher: [Read original article]() Author: Saxon Fletcher Published: 2025-07-14T07:00:00Z Content type: release Language: en Sources: [Supabase Blog]() Topics: [Supabase](), [Shadcn UI](), [Tailwind CSS](), [Database](), [Authentication](), [paas and iaas](), [Artificial Intelligence](), [cloud-computing]() Tags: [ai](), [api](), [authentication](), [components](), [database](), [infrastructure](), [library](), [platform](), [shadcn-ui](), [solutions](), [tailwind](), [ui]() ### AI overview Supabase is releasing Platform Kit, a set of Supabase UI Library components for building platforms on top of Supabase. The kit includes database, storage, authentication, and AI components, and supports customizable shadcn/ui and Tailwind CSS styling. ### Source excerpt We are launching new components in the Supabase UI Library that makes it incredibly easy to build platforms on top of Supabase. ## The European Cloud/Computing Situation DevFeed: [The European Cloud/Computing Situation]() Original publisher: [Read original article]() Published: 2025-07-11T10:03:55Z Content type: opinion Language: en Sources: [Bert Hubert's writings]() Topics: [cloud-computing](), [Software as a service](), [Amazon Web Services](), [Azure](), [Google]() Tags: [amazon-web-services](), [azure](), [cloud](), [cloud-computing](), [google](), [saas]() ### AI overview This commentary examines Europe's cloud-computing situation, tracing the shift from locally owned software to software as a service. It argues that European SaaS providers often depend on advanced services from Amazon, Microsoft/Azure, and Google, despite Europe having affordable infrastructure providers. ### Source excerpt A brief addition to the 50000 words I wrote earlier on the cloud: what is the European situation? Software Initially, companies and governments would buy licenses to software. You'd typically have a piece of software in your office, on one of your computers, to calculate payroll with. Most other computers would have copies of WordPerfect installed. This software would function for years without updates or maintenance. If WordPerfect-the-company would disappear, you would not even notice. ## A Short Summary of Chinese AI Global Expansion DevFeed: [A Short Summary of Chinese AI Global Expansion]() Original publisher: [Read original article]() Author: Adina Yakefu Published: 2024-10-03T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Artificial Intelligence](), [AI Infrastructure](), [cloud-computing](), [huawei](), [data centers](), [App]() Tags: [africa](), [ai](), [ai-infrastructure](), [apps](), [china](), [cloud](), [cloud-computing](), [community](), [data-centers](), [global-expansion](), [huawei](), [research](), [southeast-asia]() ### AI overview The article examines the international expansion of Chinese AI companies. It describes how Huawei, Tencent, Alibaba, and ByteDance are targeting overseas markets through cloud computing, AI infrastructure, data centers, large model services, and consumer applications, with particular focus on Southeast Asia, the Middle East, Africa, and selected Western markets. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Interpreting your reliability test results DevFeed: [Interpreting your reliability test results]() Original publisher: [Read original article]() Author: Andre Newman Published: 2024-09-19T00:00:00Z Content type: article Language: en Sources: [Gremlin Blog]() Topics: [Resilience](), [Scalability](), [autoscaling](), [systems](), [cpu](), [cloud-computing](), [Hardware](), [Processes](), [Amazon EC2](), [Azure]() Tags: [amazon-ec2](), [autoscaling](), [azure](), [blog](), [cloud-computing](), [cpu](), [gremlin](), [hardware](), [health](), [how-to](), [processes](), [resilience](), [scalability](), [systems]() ### AI overview This blog explains how to interpret failed reliability tests and convert their results into actions that improve service reliability. It covers Gremlin's seven-test Recommended Test Suite, including scalability, redundancy, dependency-failure resilience, CPU scalability, health checks, and autoscaling. ### Source excerpt You've run your reliability tests and got your results...now what? This blog explains how to turn your insights into actions and, ultimately, into more reliable services. ## How to test AWS managed services with Gremlin DevFeed: [How to test AWS managed services with Gremlin]() Original publisher: [Read original article]() Author: Andre Newman Published: 2024-08-01T00:00:00Z Content type: article Language: en Sources: [Gremlin Blog]() Topics: [Amazon Web Services](), [Cloud](), [cloud-computing](), [Software as a service](), [Caching]() Tags: [aws](), [blog](), [caching](), [cloud](), [cloud-services](), [gremlin](), [how-to](), [operations](), [outages](), [use-cases]() ### AI overview This Gremlin blog explains how to test and prepare for failures in AWS managed services and other cloud or SaaS dependencies. It focuses on reliability risks, cascading effects, single points of failure, and techniques such as caching, circuit breakers, and asynchronous calls. ### Source excerpt How do you run reliability tests on services that you don't manage? This blog explains how to manage reliability when using cloud services, including AWS, GCP, and Azure. ## KubeCon Paris: Launching SpinKube and Shaping the Future of Kubernetes with WebAssembly Integration DevFeed: [KubeCon Paris: Launching SpinKube and Shaping the Future of Kubernetes with WebAssembly Integration]() Original publisher: [Read original article]() Author: Matt Butcher Published: 2024-03-29T12:00:00Z Content type: article Language: en Sources: [Fermyon - Experience the next wave of cloud computing.]() Topics: [Kubernetes](), [WebAssembly](), [cloud-computing](), [Developer experience](), [Deployment]() Tags: [cloud-computing](), [deployment](), [developer-experience](), [integration](), [kubernetes](), [platform](), [webassembly]() ### AI overview The article covers the launch of SpinKube and Fermyon Platform for Kubernetes, describing their use of WebAssembly to improve application density and developer workflows from concept through deployment. ### Source excerpt Discover how SpinKube and Fermyon Platform for Kubernetes are redefining cloud computing, boosting application density, and streamlining the developer experience from concept to deployment. ## How to scale your systems using CPU utilization DevFeed: [How to scale your systems using CPU utilization]() Original publisher: [Read original article]() Author: Andre Newman Published: 2024-03-14T00:00:00Z Content type: article Language: en Sources: [Gremlin Blog]() Topics: [autoscaling](), [cpu](), [cloud-computing](), [systems]() Tags: [autoscaling](), [blog](), [chaos-engineering](), [cloud](), [cloud-computing](), [compute](), [cpu](), [infrastructure](), [load-balancer](), [observability](), [production](), [resources](), [scale](), [systems](), [testing]() ### AI overview This article explains how to scale systems based on CPU utilization. It covers CPU-based autoscaling, threshold selection, handling demand spikes, scaling horizontally or vertically, validating rules, and deploying them confidently to production. ### Source excerpt Scaling on CPU usage is a fundamental practice in cloud computing. Learn why CPU-based scaling is so important, how to set CPU scaling thresholds, and how to validate those thresholds using Gremlin. ## How to use host redundancy to improve service reliability and availability DevFeed: [How to use host redundancy to improve service reliability and availability]() Original publisher: [Read original article]() Author: Andre Newman Published: 2024-02-22T00:00:00Z Content type: article Language: en Sources: [Gremlin Blog]() Topics: [site-reliability-engineering](), [Availability](), [Replication](), [servers](), [cloud-computing](), [Provisioning](), [Infrastructure as code]() Tags: [availability](), [chaos-engineering](), [cloud](), [cloud-computing](), [how-to](), [iac](), [load-balancer](), [outages](), [provisioning](), [replication](), [servers]() ### AI overview This article explains how host redundancy improves service reliability and availability. It describes distributing applications across multiple hosts, replicating data and configuration, using a load balancer or API gateway, and testing resilience to host failures. ### Source excerpt Learn how to prepare for--and become resilient to--host and instance outages using Gremlin. ## How to make your services zone redundant DevFeed: [How to make your services zone redundant]() Original publisher: [Read original article]() Author: Andre Newman Published: 2024-02-08T00:00:00Z Content type: tutorial Language: en Sources: [Gremlin Blog]() Topics: [Availability](), [cloud-computing](), [Amazon Web Services](), [Shared Responsibility Model](), [datacenter](), [Azure]() Tags: [availability](), [aws](), [azure](), [cloud](), [cloud-computing](), [datacenter](), [gremlin](), [how-to](), [infrastructure](), [outages](), [shared-responsibility](), [shared-responsibility-model]() ### AI overview This tutorial explains zone redundancy and how to design services that continue operating when an AWS availability zone fails. It covers multi-zone infrastructure, outage risks, resilience testing, and the customer's responsibilities under the cloud Shared Responsibility Model. ### Source excerpt Learn how to prepare for--and become resilient to--availability zone and region outages. ## Fermyon releases a major Finicky Whiskers update showcasing Fermyon Cloud features DevFeed: [Fermyon releases a major Finicky Whiskers update showcasing Fermyon Cloud features]() Original publisher: [Read original article]() Author: Sohan Maheshwar Published: 2023-07-26T16:00:00Z Content type: release Language: en Sources: [Fermyon - Experience the next wave of cloud computing.]() Topics: [Cloud](), [cloud-computing]() Tags: [cloud](), [cloud-computing](), [features](), [major](), [update]() ### AI overview Fermyon has released a major update to Finicky Whiskers that showcases Fermyon Cloud features. ### Source excerpt We've released a major update to Finicky Whiskers, showcasing Fermyon Cloud's features in action. ## CDNs: Speed Up Performance by Reducing Latency DevFeed: [CDNs: Speed Up Performance by Reducing Latency]() Original publisher: [Read original article]() Author: Austin Published: 2023-05-01T14:57:05Z Content type: tutorial Language: en Sources: [Back End - Austin Gil]() Topics: [Latency](), [Cache](), [Cloud](), [cloud-infrastructure](), [cloud-computing]() Tags: [akamai](), [architecture](), [back-end](), [cache](), [cloud](), [cloud-computing](), [coding](), [development](), [latency](), [optimization](), [performance](), [programming](), [software](), [speed](), [uploads](), [web](), [web-development]() ### AI overview This tutorial explains how content delivery networks reduce latency for files stored in object storage. It describes caching copies near users and reports a reduction in one Japan-based request from 1.1 seconds to 0.2 seconds when served through a CDN. ### Source excerpt This post covers what CDNs are, how they work, and why you need one. I also show you how to connect Akamai CDN to Object Storage. ## More Predictable Shared Dyno Performance DevFeed: [More Predictable Shared Dyno Performance]() Original publisher: [Read original article]() Author: Michael Friis Published: 2023-04-11T23:00:00Z Content type: release Language: en Sources: [Heroku]() Topics: [Heroku](), [cpu](), [cloud-computing]() Tags: [cloud-computing](), [dynos](), [engineering](), [improvements](), [performance](), [platform]() ### AI overview Heroku describes improved resource isolation for shared dynos to reduce noisy-neighbor effects and make CPU access more stable and predictable. Shared dynos can still burst CPU usage, but less than before; Performance, Private, and Shield dynos are unaffected because they use dedicated instances. ### Source excerpt In this post, we'd like to share an example of the kind of behind-the-scenes work that the Heroku team does to continuously improve the platform based on customer feedback. The Heroku Common Runtime is one of the best parts of Heroku. It's the modern embodiment of the principle of computing resource time-sharing pioneered by John [...] The post More Predictable Shared Dyno Performance appeared first on Heroku. ## Why Do We Need Another Kind of Cloud Compute? DevFeed: [Why Do We Need Another Kind of Cloud Compute?]() Original publisher: [Read original article]() Author: Matt Butcher Published: 2023-03-28T17:13:47Z Content type: article Language: en Sources: [Fermyon - Experience the next wave of cloud computing.]() Topics: [cloud-computing](), [Serverless](), [WebAssembly]() Tags: [cloud](), [cloud-computing](), [functions](), [serverless](), [webassembly]() ### AI overview The article discusses WebAssembly as a new wave of cloud computing and identifies serverless functions as the reason another wave of cloud computing is needed. ### Source excerpt We talk about WebAssembly as the next wave of cloud computing. But why do we need another wave of cloud computing. The answer is serverless functions. ## What is Cloud Computing? According to ChatGPT. DevFeed: [What is Cloud Computing? According to ChatGPT.]() Original publisher: [Read original article]() Author: High Scalability Published: 2022-12-16T17:04:08Z Content type: opinion Language: en Sources: [High Scalability]() Topics: [cloud-computing](), [ChatGPT]() Tags: [ai](), [ai-ml](), [availability](), [chatgpt](), [cloud-computing](), [data](), [data-processing](), [networking](), [reliability](), [scalability]() ### AI overview The article presents ChatGPT's explanation of cloud computing, including internet-based access to shared computing resources, scalability, cost savings, flexibility, and availability. It also comments that ChatGPT's answers are broad but lack depth and argues that AI tools are more likely to augment people than replace them for now. ### Source excerpt DALL-E picture generated from the answer. I was curious, given all the ChatGPT love, what it would make of some of our favorite topics. I was both impressed and unimpressed. Impressed. ChatGPT instantly generated an response that might make a good answer in a Miss Universe contest. You know, ## What is Cloud Computing? According to ChatGPT. DevFeed: [What is Cloud Computing? According to ChatGPT.]() Original publisher: [Read original article]() Author: HighScalability Team Published: 2022-12-16T17:04:08Z Content type: opinion Language: en Sources: [High Scalability](), [High Scalability]() Topics: [ChatGPT](), [cloud-computing](), [Artificial Intelligence](), [data-processing](), [Scalability]() Tags: [ai](), [chatgpt](), [cloud-computing](), [efficiency](), [networking](), [servers]() ### AI overview An opinion article presents ChatGPT's explanation of cloud computing and evaluates it as broad and polished but lacking depth. It argues that AI and machine-learning tools are more likely to augment people than replace them, while the supplied excerpt describes cloud computing's on-demand access to shared resources and benefits such as scalability, flexibility, and efficiency. ### Source excerpt DALL-E picture generated from the answer. I was curious, given all the ChatGPT love, what it would make of some of our favorite topics. I was both impressed and unimpressed. Impressed. ChatGPT instantly generated an response that might make a good answer in a Miss Universe contest. You know, the kind of answer delivered with a radiant smile calculated to offend no one yet still sounds comprehensive and deep. Unimpressed. The answers are a mile wide and an inch deep. There's not a lot of insight. It's more of a zeitgeist oracle. Nothing wrong with that, but I don't think we'll be unemployed soon. It's as many have predicted about AI/ML. These are tools to augment us, flawed humans, not replace us...yet. Thank you for agreeing to be interviewed today. You're welcome! I'm happy to answer any questions you have. Please go ahead and ask your first question. Explain cloud computing to me at a professional level? Cloud computing is a model of computing that delivers computing services over the internet, including storage, data processing, and networking. It allows users to access and use shared computing resources, such as servers, storage, and applications, on demand and without the need to manage the underlying infrastructure. This model of computing has become increasingly popular in recent years, as it offers a number of benefits, including cost savings, flexibility, scalability, and increased efficiency. Additionally, cloud computing allows for greater collaboration and innovation, as it enables users to access and share data and resources from anywhere, at any time. Can you expand? Certainly. Cloud computing has become a widely-used model of computing, as it offers a number of benefits over traditional, on-premises computing systems. One of the key benefits of cloud computing is its scalability, which allows users to easily and quickly scale up or down their use of computing resources, depending on their changing needs. This means that users only pay for the computin ## Happy 1st Birthday Fermyon DevFeed: [Happy 1st Birthday Fermyon]() Original publisher: [Read original article]() Author: Matt Butcher Published: 2022-11-01T13:30:00Z Content type: opinion Language: en Sources: [Fermyon - Experience the next wave of cloud computing.]() Topics: [cloud-computing](), [Cloud]() Tags: [cloud](), [cloud-computing]() ### AI overview Fermyon reflects on its first year, noting the launch of several projects aimed at advancing cloud computing. ### Source excerpt On November 1st, 2022, Fermyon turned one year old. And it has been an exciting year as we launched several projects in pursuit of our goal to pioneer the next wave of cloud computing. [Next page]()