# cloud-computing Published articles for cloud-computing. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Presentation: How To Run on Three Clouds at Once, and When Not To DevFeed: [Presentation: How To Run on Three Clouds at Once, and When Not To]() Original publisher: [Read original article]() Author: Ross McFarlane, Kevin Holditch Published: 2026-09-11T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [networking](), [Databases](), [Kubernetes]() Tags: [amazon](), [architecture](), [architecture-design](), [availability](), [aws](), [cloud](), [cloud-architecture](), [cloud-computing](), [cloud-networking](), [cockroach-labs](), [cockroachdb](), [containers](), [databases](), [devops](), [disaster-recovery](), [engineering](), [europe](), [financial-applications](), [fintech](), [form3-multicloud-architecture](), [infoq](), [kubernetes](), [microservices](), [networking](), [presentation](), [qcon-london-2026](), [qcon-software-development-conference](), [resilience](), [transcripts](), [us]() ### AI overview A presentation on Form3's move from one cloud to a triple-active multi-cloud architecture, covering cross-cloud networking, distributed databases, Kubernetes operators, and regional disaster-recovery expectations. ### Source excerpt Ross McFarlane and Kevin Holditch discuss Form3's evolution from a single-cloud setup to a triple active multi-cloud architecture. They share key engineering strategies for cross-cloud networking, distributed databases with CockroachDB and NATS, custom Kubernetes operators, and navigating distinct regional disaster recovery expectations across the UK, Europe, and US financial markets. By Ross McFarlane, Kevin Holditch ## We Cut Cloud Waste Before Touching Cluster Sizes: Lessons from Running a Data Platform DevFeed: [We Cut Cloud Waste Before Touching Cluster Sizes: Lessons from Running a Data Platform]() Original publisher: [Read original article]() Author: Deepika Saini Published: 2026-09-07T06:33:31Z Content type: article Language: en Sources: [Housing.com]() Topics: [BigQuery](), [databricks](), [Optimization](), [AWS Database Migration Service](), [data-platforms](), [Platform Engineering](), [migration](), [Cloud]() Tags: [bigquery](), [cloud-computing](), [cost](), [cost-optimization](), [data-engineering](), [data-platforms](), [databricks](), [delta-lake](), [finops](), [infrastructure](), [migration](), [optimization](), [platform-engineering]() ### AI overview This article explains how a data platform team reduced cloud costs by removing obsolete BigQuery data, adjusting Delta Lake retention, right-sizing DMS infrastructure, identifying unmonitored Databricks jobs, and standardizing pipeline onboarding and cost alerts. It reports that DMS costs were cut by over 50% and that retention was reduced from 90 days to 7 days for appropriate workloads after operational validation. ### Source excerpt How orphaned BigQuery storage, Delta retention, DMS right-sizing, and Databricks System Tables became our biggest cloud cost wins. The biggest cloud cost optimization we made wasn't shrinking clusters.It was deleting data we'd forgotten we were paying for.Like most teams, our first instinct was to tune infrastructure first. Instead, we discovered a treasure trove of hidden costs: orphaned BigQuery datasets, 90-day Delta retention, 24-hour jobs no one monitored, and DMS infrastructure that no longer matched business needs.We stopped treating cloud bills as a finance problem and started treating them as a platform engineering problem.30-second takeaway Why deleting forgotten data saved more than shrinking clusters. How we cut DMS costs by over 50%. How Databricks System Tables exposed hidden 24-hour jobs. How config.metadata standardized pipeline onboarding. How weekly Slack alerts turned cost optimization into a habit. Section 1: Storage Was Our Biggest Leak -- We Were Paying to Store Data Nobody Used This is the most overlooked cost on many data platforms. Storage duplication across platforms We had already migrated several workloads from BigQuery to Databricks. Large datasets were still sitting in BigQuery long after they had stopped serving production workloads - quietly generating storage costs month after month. Nothing failed. No alerts fired. Every month, we paid for storage that no longer served production workloads.A migration isn't complete until the old storage is decommissioned.The hidden cost of long retention The next surprise came from Delta Lake retention settings. Our workspace was configured to retain deleted table data and transaction history for 90 days to support time travel. Time travel is incredibly useful. But did every table need three months of historical recovery? Not really. We reduced retention to 7 days for appropriate workloads after validating operational needs. What changed immediately: Less storage tied up in deleted data. Faster clea ## Happy 20th Birthday, Amazon EC2 DevFeed: [Happy 20th Birthday, Amazon EC2]() Original publisher: [Read original article]() Author: Channy Yun (윤석찬) Published: 2026-08-25T16:11:02Z Content type: article Language: en Sources: [AWS News Blog]() Topics: [Amazon EC2](), [Inference Performance]() Tags: [ai-inference](), [amazon-cloudwatch](), [amazon-ec2](), [amazon-ec2-mac-instances](), [apple](), [aws](), [aws-inferentia](), [aws-local-zones](), [aws-outposts](), [aws-trainium](), [cloud](), [cloud-computing](), [general](), [generative-ai](), [graviton](), [infrastructure](), [intel](), [linux](), [news](), [nitro-system](), [performance]() ### AI overview AWS marks Amazon EC2's 20th anniversary by reviewing its evolution from a single Linux virtual-server instance to a global service with more than 1,200 instance types. The article highlights foundational services, expanded deployment options, and AWS-designed silicon for AI inference and training. ### Source excerpt On the 20th Anniversary, we recognize how AWS has continued to push the boundaries of what cloud computing can deliver, building custom silicon for general-purpose and AI workloads and expanding EC2 into new form factors and deployment models that our customers in 2006 could not have imagined. ## Patching at Fleet Scale, Twice: How DigitalOcean Closed Januscape and the AMD Safe RET Issue Without Customer Impact DevFeed: [Patching at Fleet Scale, Twice: How DigitalOcean Closed Januscape and the AMD Safe RET Issue Without Customer Impact]() Original publisher: [Read original article]() Author: Tim Lisko Published: 2026-08-24T21:25:19Z Content type: article Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [virtualization](), [vulnerability](), [Cloud](), [cloud security](), [Security](), [Exploit](), [Kernel](), [Linux]() Tags: [cloud](), [cloud-computing](), [cve](), [digitalocean](), [engineering](), [exploit](), [infrastructure](), [kernel](), [linux](), [security](), [trust-security](), [update](), [virtualization](), [vulnerability]() ### AI overview DigitalOcean describes how it responded to two serious vulnerabilities affecting its hypervisor fleet: Januscape, a KVM nested-virtualization flaw, was addressed with fleet-wide livepatching, while a separate AMD hypervisor vulnerability required kernel updates and reboots across roughly 1,600 hypervisors. The article reports zero confirmed customer-facing impact. ### Source excerpt Setting the stakes In early July, security researcher Hyunwoo Kim discovered Januscape (CVE-2026-53359), a flaw in KVM's handling of nested virtualization that could allow a malicious guest to escape into the host hypervisor. It was disclosed publicly on July 6 via the Linux oss-security mailing list. For a cloud provider, a guest-to-host escape is the most serious class of vulnerability there is: the hypervisor is the boundary that keeps each customer's workloads isolated from each other, and from our infrastructure itself. We responded, patched the entire fleet in eight days with zero confirmed customer-facing impact, and drafted a post about how we did it. Then, before we could hit publish, it happened again. In late July we learned of a second and unrelated vulnerability affecting our entire AMD hypervisor fleet, that could not be livepatched. Roughly 1,600 hypervisors needed a kernel update and a reboot. So now this story is about two responses, three weeks apart. The first built the muscle. The second proved it was repeatable, at a larger scale, and on a harder constraint. Here's how both played out, and why two of the most serious vulnerability classes in cloud computing ended up feeling like just another couple of weeks for us. Act one: Januscape The fast path: fleet-wide livepatching Our response kicked off the same night the vulnerability was disclosed. When public exploit code surfaced late in the evening of July 6, the Kernel Engineering team was paged and dug in immediately. Engineers reproduced the exploit in an isolated environment, confirmed which kernel lines were affected, and built the first working livepatch before 1 AM, roughly 45 minutes after answering the page. Livepatching lets us fix a running kernel in place, with no reboot, no migration, and no observed disruption to the customer. A few hours later, patches for the kernel versions (6.1 and 6.12) that run the majority of our hypervisor fleet were ready to ship. For the remainder, we had to ## Graceful Kubernetes Shutdowns Help Protect In-Flight Orders DevFeed: [Graceful Kubernetes Shutdowns Help Protect In-Flight Orders]() Original publisher: [Read original article]() Author: Global Technology Published: 2026-08-12T14:03:07Z Content type: tutorial Language: en Sources: [McDonald's Technical Blog - Medium]() Topics: [Kubernetes](), [distributed-systems](), [Cloud]() Tags: [cloud](), [cloud-computing](), [devops](), [distributed-systems](), [infrastructure](), [kubernetes](), [messaging](), [operations](), [scale](), [software-engineering]() ### AI overview This article explains how Kubernetes scaling, rolling updates, and cluster maintenance can interrupt in-flight requests and messages. It introduces graceful shutdown patterns in application code and infrastructure to reduce message loss and improve reliability for large-scale order processing. ### Source excerpt Why graceful shutdowns matter when millions of orders are in flight. by: Samuel Hapgood, Software Engineer III Quick Bytes At McDonald's scale, rare failures in distributed systems can impact real customer and crew experiences Graceful shutdown patterns in Kubernetes -- across application code and infrastructure -- help protect in-flight work during scaling and deployments With the right shutdown strategies, teams can reduce message loss, improve reliability, and ensure orders make it to the kitchen Imagine you're placing a mobile order at McDonald's during a busy lunch rush. You hit "submit," and the kitchen never gets the order. Now you're left waiting -- no food, no updates, and growing frustration. At first glance, it seems like an easy problem to avoid. How hard can it be to just keep track of the order? But in modern, large-scale distributed systems -- especially those that must scale continuously -- that's not always straightforward. Behind the scenes, platforms like Kubernetes (which orchestrate containerized applications in the cloud) are continuously rescheduling pods as part of normal operations such as scaling applications, performing rolling updates, and maintaining cluster health. During these transitions, in-flight requests or background messages can be interrupted in subtle ways. A pod might shut down before finishing a task, a request might be canceled mid-flight, or a message might be acknowledged by a receiving service, but never actually processed. Even with strong messaging platforms and best practices in place, application shutdowns can still interrupt in-flight work if they are not handled carefully. These are edge cases -- but at McDonald's scale, edge cases happen often enough to matter. In the US alone, our platforms support roughly one million orders per hour on a dynamically scaled cluster of hundreds of nodes. At that volume, reliability becomes critical to ensuring a seamless experience for customers, crews, and restaurant operations. Prevent ## Don't Buy a Raspberry Pi Just to Learn Raspberry Pi DevFeed: [Don't Buy a Raspberry Pi Just to Learn Raspberry Pi]() Original publisher: [Read original article]() Author: Patrick Fromaget Published: 2026-08-12T05:00:00Z Content type: opinion Language: en Sources: [RaspberryTips]() Topics: [coding](), [Linux](), [networking](), [Security](), [Software](), [Databases](), [Server](), [Cloud]() Tags: [cloud-computing](), [coding](), [databases](), [electronics](), [learn](), [linux](), [networking](), [programming](), [quick-tips](), [raspberry-pi](), [security](), [software-development]() ### AI overview Raspberry Pi should be treated as a practical training ground rather than a standalone skill. The article recommends using it to learn Linux, coding, electronics, networking, security, databases, and related software skills through focused projects. ### Source excerpt Raspberry Pi isn't really a skill in itself. It's not something you'll put on your CV like Windows administration or Office use. So if you're buying a Raspberry Pi just to become "good at Raspberry Pi", you're missing the point. For me, Raspberry Pi is like a training ground where I can try out different... ## Deploy MCP Servers on AWS Lambda with Serverless Framework v4 DevFeed: [Deploy MCP Servers on AWS Lambda with Serverless Framework v4]() Original publisher: [Read original article]() Author: Serverless Team Published: 2026-08-11T00:00:00Z Content type: tutorial Language: en Sources: [Serverless Blog]() Topics: [MCP](), [AWS Lambda](), [serverless framework](), [Streaming]() Tags: [aws](), [aws-lambda](), [cloud-computing](), [faas](), [function-as-a-service](), [infrastructure-as-code](), [mcp](), [news](), [pricing](), [serverless](), [serverless-architecture](), [serverless-framework](), [streaming](), [yaml]() ### AI overview A Serverless Framework v4.41.0 deployment workflow for MCP servers on AWS Lambda, using YAML and providing an HTTPS endpoint, response streaming, and pay-per-request pricing. ### Source excerpt Serverless Framework v4.41.0 deploys MCP servers to AWS Lambda in a few lines of YAML - a real HTTPS endpoint, response streaming, and pay-per-request pricing. ## The Dodo Digest: Don't Let Infrastructure Become Your Biggest Risk DevFeed: [The Dodo Digest: Don't Let Infrastructure Become Your Biggest Risk]() Original publisher: [Read original article]() Author: Rishabh Goel Published: 2026-08-03T00:00:00Z Content type: article Language: en Sources: [Dodo Payments Blog]() Topics: [Security](), [Artificial Intelligence](), [Cybersecurity](), [anthropic](), [OpenAI](), [ransomware]() Tags: [ai](), [anthropic](), [cloud](), [cloud-computing](), [cybersecurity](), [features](), [fraud](), [newsletter](), [openai](), [real-time](), [security]() ### AI overview The Dodo Digest argues that as AI systems become more capable, security, abuse prevention, and infrastructure are as important as model performance. It discusses security incidents reported by OpenAI and Anthropic, the growing risks of misuse, and product updates including rebuilt Analytics and Prevent Trial Misuse in Dodo Payments v1.109.0. ### Source excerpt Security incidents at OpenAI and Anthropic show why infrastructure matters as much as models. Plus Analytics v3, Prevent Trial Misuse, payout accounts, and a ReplyDaddy builder spotlight. ## AWS Lambda MicroVM Sandboxes with the Serverless Framework DevFeed: [AWS Lambda MicroVM Sandboxes with the Serverless Framework]() Original publisher: [Read original article]() Author: Serverless Team Published: 2026-07-21T00:00:00Z Content type: release Language: en Sources: [Serverless Blog]() Topics: [AWS Lambda](), [serverless framework](), [Firecracker](), [Emulator]() Tags: [aws](), [aws-lambda](), [cloud-computing](), [emulator](), [faas](), [firecracker](), [function-as-a-service](), [infrastructure-as-code](), [news](), [serverless](), [serverless-architecture](), [serverless-framework]() ### AI overview AWS Lambda MicroVMs run untrusted and agent-generated code in isolated Firecracker virtual machines. The Serverless Framework supports them as Sandboxes in serverless.yml, with deployment and a local MicroVM emulator for development and testing. ### Source excerpt AWS Lambda MicroVMs are a new compute primitive for running untrusted and agent-generated code in fully isolated Firecracker virtual machines. The Serverless Framework brings them into serverless.yml as Sandboxes, with one-command deploys and a full local MicroVMs emulator so you can build and test before you ship. ## 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 ## Slack AI: The Path to Multi-Cloud DevFeed: [Slack AI: The Path to Multi-Cloud]() Original publisher: [Read original article]() Author: Shaurya Kethireddy Published: 2026-05-28T14:15:20Z Content type: article Language: en Sources: [Engineering at Slack]() Topics: [AI Chat]() Tags: [ai](), [amazon-bedrock](), [anthropic](), [availability](), [aws](), [backend](), [cloud](), [cloud-architecture](), [cloud-computing](), [collaboration](), [compliance](), [containers](), [engineering](), [enterprise](), [gpu](), [iam](), [infrastructure](), [innovation](), [llms](), [machine-learning](), [performance](), [security](), [slack](), [software-development](), [uncategorized](), [vpc]() ### AI overview Slack describes evolving its enterprise LLM-serving infrastructure from AWS SageMaker toward multi-cloud, multi-vendor orchestration to improve resilience, capacity management, and access to newer models. ### Source excerpt In early 2023, Slack faced a foundational challenge: serving Large Language Models (LLMs) at enterprise scale with the security, reliability, and performance our customers expect. Over three years, we evolved from basic infrastructure to orchestrating a sophisticated multi-cloud architecture. We didn't just want shiny new models; we needed a system resilient to regional outages and... ## AWS Bedrock AgentCore, S3 File Mounts, Python & Install Improvements DevFeed: [AWS Bedrock AgentCore, S3 File Mounts, Python & Install Improvements]() Original publisher: [Read original article]() Author: Serverless Team Published: 2026-05-27T00:00:00Z Content type: release Language: en Sources: [Serverless Blog]() Topics: [Amazon Bedrock](), [Amazon S3](), [Python](), [Framework](), [Serverless](), [Security]() Tags: [aws](), [aws-lambda](), [bedrock](), [bug](), [bug-fixes](), [cloud-computing](), [faas](), [framework](), [function-as-a-service](), [improvements](), [infrastructure-as-code](), [install](), [news](), [python](), [s3](), [security](), [serverless](), [serverless-architecture](), [serverless-framework](), [update]() ### AI overview Serverless Framework's May 2026 update covers built-in AWS Bedrock AgentCore support, S3 file system mounts for Lambda, faster Framework installs, Python improvements, bug fixes, and security updates. ### Source excerpt Serverless Framework's May 2026 update covering built-in AWS Bedrock AgentCore support, S3 file system mounts for Lambda, faster Framework installs, Python improvements, bug fixes, and security updates. ## 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. ## Scaling Whatnot: Behind the Largest Live Shopping Stream in US History DevFeed: [Scaling Whatnot: Behind the Largest Live Shopping Stream in US History]() Original publisher: [Read original article]() Author: Whatnot Engineering Published: 2026-02-24T14:33:16Z Content type: article Language: en Sources: [Whatnot Engineering]() Topics: [Scalability](), [Architecture & Design](), [Platform Engineering](), [SRE](), [Elixir](), [Python]() Tags: [cloud-computing](), [devops](), [elixir](), [infrastructure](), [load-testing](), [python](), [resilience](), [scalability](), [scale](), [site-reliability-engineer](), [software-engineering](), [sre](), [technology]() ### AI overview Whatnot describes how it prepared its platform for a MrBeast giveaway stream that reached 583,000 concurrent viewers and became the largest live shopping event in US history. The article covers architectural investments, progressive production load testing, event-day results, and lessons for future scalability. ### Source excerpt On February 8, 2026, over a half million viewers tuned in to watch MrBeast give away 1 million dollars in prizes on Whatnot. On Big Game Sunday 2026, MrBeast went live on Whatnot for a giveaway show that would become the largest live shopping event in US history. At peak, 583,000 concurrent viewers were watching a single show on our platform. Over 555k people entered a single giveaway. We drove hundreds of thousands of new signups in 24 hours. If any one of a dozen systems buckled, it would have happened live on camera. We pulled it off with zero major incidents. But that outcome was never guaranteed. It took months of preparation, 60+ engineers across every major engineering org, and some of the most significant infrastructure investments we've ever made. In this post, we'll walk through the biggest technical challenges we faced and how we solved them, not with throwaway scaffolding, but with durable platform improvements that raise our scalability ceiling for every seller and buyer on the platform. We'll cover the work in three parts. First, the key architectural investments we made to handle this scale: admission control, connection pooling, feed resilience, and video infrastructure. Then, how we validated it all through progressive production load testing. Finally, what happened on event day, what we learned, and what we're carrying forward. Setting the Stage If you've followed our blog, you might remember our Post Malone "Post-Poned" post from 2022 or our three-part series on preparing for the 2024 Big Game. Each of those events pushed us to improve, and each one revealed new limits. As our community has grown, scaling our infrastructure to match has been a consistent priority. The MrBeast event was on a different order of magnitude entirely, but it accelerated work that was already underway. Our target was to support 1 million concurrent viewers on a single stream and 1.35 million across the platform. To put that in perspective, our previous largest event had ## 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 ## Newsletter: Managed Instances, Durable Functions, Core Integrations DevFeed: [Newsletter: Managed Instances, Durable Functions, Core Integrations]() Original publisher: [Read original article]() Author: Serverless Team Published: 2026-01-20T00:00:00Z Content type: news Language: en Sources: [Serverless Blog]() Topics: [serverless framework](), [Serverless](), [Amazon Web Services](), [Single sign-on (SSO)]() Tags: [2026](), [aws](), [aws-lambda](), [cloud-computing](), [faas](), [function-as-a-service](), [infrastructure-as-code](), [integrations](), [lambda](), [news](), [serverless](), [serverless-architecture](), [serverless-framework](), [sso]() ### AI overview A Serverless Framework Q1 2026 newsletter covers Lambda managed instances, durable functions, native AppSync support, built-in pruning, AWS SSO login, and integrations moved from plugins into core. ### Source excerpt Serverless Framework's Q1 2026 newsletter covering Lambda managed instances, durable functions, native AppSync support, built-in pruning, AWS SSO login, and plugin-to-core integrations. ## The Design & Implementation of Sprites DevFeed: [The Design & Implementation of Sprites]() Original publisher: [Read original article]() Published: 2026-01-14T00:00:00Z Content type: article Language: en Sources: [The Fly Blog]() Topics: [fly.io](), [fly](), [Cloud](), [Linux](), [Orchestration](), [ssh](), [Containers](), [Docker]() Tags: [cdn](), [close-to-users](), [cloud](), [cloud-computing](), [containers](), [cost](), [deploy-app-servers](), [docker](), [elixir](), [fly](), [fly-io](), [heroku-alternative](), [heroku-competitor](), [hosting](), [i](), [linux](), [networking](), [orchestration](), [platform](), [postgresql-clusters](), [scale](), [servers](), [ssh]() ### AI overview Fly.io's article introduces Sprites, disposable Linux virtual machines designed to start in seconds, provide root access and a durable 100GB filesystem, and automatically sleep when inactive. It explains the orchestration decisions behind the platform, including moving away from container images while retaining convenient, Docker-like workflows. ### Source excerpt We're Fly.io, and this is the place in the post where we'd normally tell you that our job is to take your containers and run them on our own hardware all around the world. But last week, we launched Sprites, and they don't work that way at all. Sprites are something new: Docker without Docker without Docker. This post is about how they work. Replacement-level homeowners buy boxes of pens and stick them in "the pen drawer". What the elites know: you have to think adversarially about pens. "The purpose of a system is what it does"; a household's is to uniformly distribute pens. Months from now, the drawer will be empty, no matter how many pens you stockpile. Instead, scatter pens every place you could possibly think to look for one -- drawers, ledges, desks. Any time anybody needs a pen, several are at hand, in exactly the first place they look. This is the best way I've found to articulate the idea of Sprites, the platform we just launched at Fly.io. Sprites are ball-point disposable computers. Whatever mark you mean to make, we've rigged it so you're never more than a second or two away from having a Sprite to do it with. Sprites are Linux virtual machines. You get root. They create in just a second or two: so fast, the experience of creating and shelling into one is identical to SSH'ing into a machine that already exists. Sprites all have a 100GB durable root filesystem. They put themselves to sleep automatically when inactive, and cost practically nothing while asleep. As a result, I barely feel the need to name my Sprites. Sometimes I'll just type sprite create dkjsdjk and start some task. People at Fly.io who use Sprites have dozens hanging around. There aren't yet many things in cloud computing that have the exact shape Sprites do: Instant creation No time limits Persistent disk Auto-sleep to a cheap inactive state This is a post about how we managed to get this working. We created a new orchestration stack that undoes some of the core decisions we made for Fly ## AWS re:Invent serverless features available in Serverless Framework v4 DevFeed: [AWS re:Invent serverless features available in Serverless Framework v4]() Original publisher: [Read original article]() Author: Serverless Team Published: 2025-12-09T00:00:00Z Content type: article Language: en Sources: [Serverless Blog]() Topics: [Serverless](), [serverless framework](), [Amazon Web Services](), [Streaming](), [IAM](), [networking]() Tags: [apis](), [aws](), [aws-lambda](), [cloud-computing](), [faas](), [features](), [framework](), [function-as-a-service](), [iam](), [infrastructure-as-code](), [livestream](), [networking](), [news](), [serverless](), [serverless-architecture](), [serverless-framework](), [streaming](), [subscription]() ### AI overview Serverless Framework v4 already includes several AWS re:Invent serverless launches, including new runtimes, streaming APIs, tenant isolation, and expanded IAM and networking controls. The article also mentions a livestream deep-dive and a limited-time subscription offer. ### Source excerpt AWS re:Invent's biggest serverless launches are already live in Serverless Framework v4, with new runtimes, streaming APIs, tenant isolation, enriched IAM and networking controls, and more - all ready to use today, alongside a livestream deep-dive and a limited-time subscription offer. ## 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 ( ## Newsletter: Improved Python Support, Merging Plugins into Core, and more DevFeed: [Newsletter: Improved Python Support, Merging Plugins into Core, and more]() Original publisher: [Read original article]() Author: Serverless Team Published: 2025-11-18T00:00:00Z Content type: news Language: en Sources: [Serverless Blog]() Topics: [serverless framework](), [Python](), [configuration](), [IAM](), [OpenTelemetry]() Tags: [aws](), [aws-lambda](), [cloud-computing](), [configuration](), [faas](), [function-as-a-service](), [how-to](), [iam](), [infrastructure-as-code](), [news](), [opentelemetry](), [plugins](), [python](), [serverless](), [serverless-architecture](), [serverless-framework](), [updates]() ### AI overview A Serverless Framework newsletter recaps built-in Python support, plugin consolidation into core, reliability improvements during upstream outages, IAM changes, and planned OpenTelemetry configuration and expanded variable resolution. It also mentions a limited-time discount and hiring for the Framework team. ### Source excerpt Serverless Framework is closing out the year with major updates--new Python support built in, improved reliability during upstream outages, key IAM changes, and upcoming features like OpenTelemetry configuration and expanded variable resolution--while also offering a limited-time 20% end-of-year discount and actively hiring for the Framework team. This post recaps what's new, what's coming next, and how to get involved. ## 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 ## From Cloud Chaos to Governance: How FinOps and DevOps Converge in 2025 DevFeed: [From Cloud Chaos to Governance: How FinOps and DevOps Converge in 2025]() Original publisher: [Read original article]() Author: Urolime Technologies Published: 2025-10-17T06:33:40Z Content type: article Language: en Sources: [Kubernetes Archives - Urolime Blogs]() Topics: [DevOps](), [finops](), [Cloud](), [cloud-infrastructure](), [GitOps](), [Platform Engineering](), [Kubernetes]() Tags: [automation](), [cloud](), [cloud-computing](), [devops](), [finops](), [gitops](), [kubernetes](), [kubernetes-consulting-services](), [platform-engineering]() ### AI overview This article discusses how FinOps and DevOps can work together to address cloud chaos, including rising costs, siloed teams, and inefficient resource use. It describes DevOps practices such as automation, collaboration, GitOps, and platform engineering alongside FinOps practices such as forecasting, resource tagging, and financial accountability. ### Source excerpt Cloud computing fuels today's businesses with the ability to innovate at an unprecedented scale. However, a large number of organizations encounter "cloud chaos" which can be defined through the exploding costs, siloed teams, and a wasteful resource utilization. In 2025, the intersection of FinOps and DevOps is shaping this chaos into managed, optimized cloud infrastructures. [...] ## 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. [Next page]()