# Cloud Services Published articles for Cloud Services. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Automattic CEO Matt Mullenweg returned after 33 hours of leave without a public explanation DevFeed: [Automattic CEO Matt Mullenweg returned after 33 hours of leave without a public explanation]() Original publisher: [Read original article]() Author: Adrian Bridgwater Published: 2026-09-16T20:39:42Z Content type: news Language: en Sources: [The New Stack]() Topics: [WordPress](), [App](), [Open Source](), [Tumblr]() Tags: [cloud-services](), [company](), [cto](), [developer-tools](), [governance](), [matt-mullenweg](), [open-source](), [wordpress]() ### AI overview Automattic CEO Matt Mullenweg was placed on leave and reinstated about 33 hours later. The company has not publicly explained what prompted the leave or return, while observers have raised--but not established--the possibility of an attempted change in control. ### Source excerpt There were unusual goings-on this month at Automattic, a company known for its free and open-source app for building WordPress The post Automattic says CEO Mullenweg was gone and back inside 33 hours. What happened between? appeared first on The New Stack. ## AWS agents will suggest your new flights. Code decides what gets booked. DevFeed: [AWS agents will suggest your new flights. Code decides what gets booked.]() Original publisher: [Read original article]() Author: Meredith Shubel Published: 2026-09-15T21:27:21Z Content type: news Language: en Sources: [The New Stack]() Topics: [AWS Step Functions](), [Amazon Bedrock AgentCore](), [Orchestration](), [Generative AI](), [Amazon Web Services]() Tags: [ai-agents](), [ai-engineering](), [airline](), [amazon-bedrock-agentcore](), [aws](), [aws-step-functions](), [cloud-services](), [generative-ai](), [orchestration]() ### AI overview AWS published a Step Functions pattern for airline rebooking in which Amazon Bedrock AgentCore agents propose itineraries and compensation messages, while deterministic workflow steps validate proposals before reservations change or payments are issued. ### Source excerpt AWS published a new Step Functions pattern this week that gives AI agents a role in airline rebooking while keeping The post AWS agents will suggest your new flights. Code decides what gets booked. appeared first on The New Stack. ## How to attach an owner to every cloud resource you find DevFeed: [How to attach an owner to every cloud resource you find]() Original publisher: [Read original article]() Author: Zeen Rachidi Published: 2026-09-15T14:00:00Z Content type: tutorial Language: en Sources: [The New Stack]() Topics: [Cloud](), [Provisioning](), [Open Policy Agent](), [audit trail]() Tags: [audit-trail](), [cloud](), [cloud-services](), [devops](), [env-zero](), [how-to](), [logs](), [open-policy-agent](), [platform-engineering](), [policy](), [post-contributed](), [provisioning](), [sponsor-env-zero](), [sponsored-post-contributed]() ### AI overview This tutorial explains how to identify cloud resources without assigned owners and prevent new ownerless resources. It presents continuously synced inventory queries, Open Policy Agent policies requiring owner tags, and logs or audit trails for resource governance. ### Source excerpt The engineer who knew why that cloud instance existed has left the company. The instance is still running, the bill The post How to attach an owner to every cloud resource you find appeared first on The New Stack. ## OpenAI's researchers burned $7,000 a day on AI agents -- now it's opening the floodgates DevFeed: [OpenAI's researchers burned $7,000 a day on AI agents -- now it's opening the floodgates]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-11T21:27:42Z Content type: news Language: en Sources: [The New Stack]() Topics: [Inference](), [long-context]() Tags: [agents](), [ai](), [ai-agents](), [ai-infrastructure](), [api](), [cloud-services](), [codex](), [compute](), [developers](), [inference](), [openai](), [research](), [sandbox](), [tools]() ### AI overview OpenAI's public-beta Agents API lets developers run long-lived agents with managed job state, context compression, optional tools, parallel subagents, and execution in OpenAI's sandbox or developer-controlled infrastructure. The article highlights the resulting inference and compute costs, citing internal research-agent usage figures. ### Source excerpt OpenAI rolled out its Agents API in public beta Thursday, opening the backend behind Codex to developers looking to run The post OpenAI's researchers burned $7,000 a day on AI agents -- now it's opening the floodgates appeared first on The New Stack. ## AI floods security teams with flaws -- business context sets priorities DevFeed: [AI floods security teams with flaws -- business context sets priorities]() Original publisher: [Read original article]() Author: Megan Carnegie Published: 2026-09-10T12:00:00Z Content type: article Language: en Sources: [The New Stack]() Topics: [vulnerability](), [Cybersecurity](), [Business Security]() Tags: [ai](), [business](), [cloud-services](), [i-o-mergent](), [post](), [security](), [sponsor-i-o-mergent](), [sponsored](), [sponsored-post](), [vulnerability]() ### AI overview The article argues that security teams should prioritize vulnerabilities using business context rather than scanner severity alone, because automated findings can overwhelm limited engineering capacity. ### Source excerpt A security researcher testing a 300-person B2B company with a global footprint discovered an internet-exposed database with weak authentication during The post AI floods security teams with flaws -- business context sets priorities appeared first on The New Stack. ## How to Carry User Identity Across Federated Kubernetes and AI Platforms DevFeed: [How to Carry User Identity Across Federated Kubernetes and AI Platforms]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-03T22:36:02Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Platform](), [Kubernetes](), [AI, ML & Data Engineering]() Tags: [ai-platform](), [ai-platforms-deployment](), [api](), [aws](), [cloud](), [cloud-services](), [data](), [data-center-cloud](), [developer-tools-techniques](), [how-to](), [identity](), [infrastructure](), [kubernetes](), [software-defined-data-center]() ### AI overview The article presents a central identity-gateway pattern for carrying user identity across federated Kubernetes, data, and AI platforms. It uses OIDC, a shared session store, stateless data-plane gateways, and an identity-validation API to establish trusted local identity context without distributing raw tokens to every application. ### Source excerpt Modern AI platforms are no longer a single application behind one login screen. A user may start in a central portal, open a governed dataset, launch a notebook... ## Get to VMware Cloud Foundation Faster with VMware Pro Cloud Services DevFeed: [Get to VMware Cloud Foundation Faster with VMware Pro Cloud Services]() Original publisher: [Read original article]() Author: paula swanson Published: 2026-09-03T20:21:10Z Content type: article Language: en Sources: [VMware Blogs]() Topics: [Cloud]() Tags: [architectures](), [best-practices](), [cloud](), [cloud-infrastructure](), [cloud-services](), [infrastructure](), [modernization](), [private-cloud](), [vcf-professional-services](), [vmware](), [vmware-cloud-foundation]() ### AI overview The article presents VMware Pro Cloud Services as a way to deploy VMware Cloud Foundation faster and deliver a public-cloud experience on premises. It describes pre-packaged services for designing, deploying, configuring, integrating, and operationalizing a secure private cloud, using VMware reference architectures and practices developed through customer engagements. ### Source excerpt Public clouds have upped the ante when it comes to what we expect from private cloud solutions. Our smartphones and fast access to public cloud resources have conditioned us to expect instant access to what we need when we need it. Anything less feels antiquated and cumbersome, leaving your business users frustrated and disenchanted. To ... Continued The post Get to VMware Cloud Foundation Faster with VMware Pro Cloud Services appeared first on VMware Blogs. ## NVIDIA Vera Rubin and Blackwell Set a New Standard for Agentic AI Performance per Watt DevFeed: [NVIDIA Vera Rubin and Blackwell Set a New Standard for Agentic AI Performance per Watt]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-24T15:00:05Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI-assisted coding]() Tags: [agentic-ai](), [agentic-ai-generative-ai](), [agentic-coding](), [ai-agent](), [ai-agents](), [ai-factory](), [ai-infrastructure](), [benchmark](), [blackwell](), [cache](), [cloud-networking](), [cloud-services](), [data-center-cloud](), [gb300-nvl72](), [hardware](), [inference](), [long-context](), [mixture-of-experts-moe](), [nemotron](), [nvidia](), [performance](), [software-defined-data-center](), [tools](), [vera-rubin](), [vera-rubin-nvl72]() ### AI overview The article examines the SemiAnalysis AgentX benchmark for measuring infrastructure efficiency on replayed agentic coding sessions. It compares Vera Rubin and Blackwell NVL72 systems by agentic throughput per megawatt and explains why dynamic, stateful sessions require more realistic evaluation than fixed-length inference tests. ### Source excerpt AI agents have expanded inference from single-turn interactions into multi-step workflows that reason, invoke tools, coordinate subagents, and carry growing... ## NVIDIA Vera Storage Benchmarks: Faster Encryption, Compression, Integrity Checking, and Recovery for AI-Native Storage DevFeed: [NVIDIA Vera Storage Benchmarks: Faster Encryption, Compression, Integrity Checking, and Recovery for AI-Native Storage]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-03T16:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Nvidia](), [Benchmark](), [cpu](), [Encryption](), [Inference Performance](), [Security](), [Resilience](), [systems]() Tags: [agentic](), [agentic-ai](), [agentic-ai-generative-ai](), [agents](), [ai](), [ai-agent](), [ai-factory](), [ai-inference](), [benchmark](), [benchmarks](), [bluefield-dpu](), [cloud-apis](), [cloud-networking](), [cloud-services](), [compression](), [cpu](), [data-center-cloud](), [doca](), [encryption](), [featured](), [infrastructure](), [networking-communications](), [nvidia](), [nvidia-vera](), [performance](), [security](), [software-defined-data-center](), [storage](), [systems](), [top-stories](), [vera-cpu](), [vera-rubin](), [vera-rubin-nvl72]() ### AI overview NVIDIA presents benchmark results for the Vera BlueField-4 STX Storage Processor in AI-native storage workloads. The results describe faster encryption and decryption, recovery, integrity checking, compression and decompression, and multi-stage storage processing than an x86 CPU, with lower CPU and power overhead. ### Source excerpt Storage is an active part of every agentic AI workflow. As agents retrieve enterprise knowledge, access persistent memory, reuse key-value (KV) cache data,... ## How EKS is simplifying Kubernetes cluster lifecycle management DevFeed: [How EKS is simplifying Kubernetes cluster lifecycle management]() Original publisher: [Read original article]() Author: Spyros Angelopoulos Published: 2026-08-01T15:00:00Z Content type: article Language: en Sources: [Kubernetes Overview, News and Trends | The New Stack]() Topics: [Amazon Elastic Kubernetes Service](), [Kubernetes](), [Availability](), [Deployment]() Tags: [availability](), [aws-marketplace](), [cloud-services](), [deployment](), [devops](), [kubernetes](), [lifecycle](), [post-contributed](), [sponsor-aws-marketplace](), [sponsored-post-contributed](), [upgrades]() ### AI overview The article describes the operational challenges of Kubernetes upgrades and EKS efforts to make cluster lifecycle management easier. It highlights Extended Support, which broadens the availability of each Kubernetes version to 26 months, giving organizations more time to validate and schedule upgrades. ### Source excerpt Kubernetes moves at a pace of three minor version releases per year, and staying current is not optional if you The post Kubernetes upgrades don't have to break things: How EKS is making cluster lifecycle management simpler and safer appeared first on The New Stack. ## What's new in clickhousectl v0.4.0 DevFeed: [What's new in clickhousectl v0.4.0]() Original publisher: [Read original article]() Author: Al Brown Published: 2026-07-31T11:44:09Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Command-line interface](), [autoscaling](), [Cloud](), [Kafka](), [MySQL](), [API keys]() Tags: [api-keys](), [autoscaling](), [cli](), [clickhouse](), [cloud](), [cloud-services](), [kafka](), [mysql](), [telemetry]() ### AI overview clickhousectl v0.4.0 introduces horizontal autoscaling for ClickHouse Cloud services, beta ClickPipe schema discovery for Kafka and Kinesis sources, more controls for object storage and MySQL/Postgres ingestion, global OAuth token storage, safer local server lifecycle behavior, and opt-out anonymous usage telemetry. ### Source excerpt clickhousectl v0.4.0 adds horizontal autoscaling for Cloud services, ClickPipe schema discovery, finer control over ClickPipe ingestion, and opt-out anonymous usage telemetry. ## NVIDIA Exemplar Cloud: Lessons for Unlocking Full Performance on AI Infrastructure DevFeed: [NVIDIA Exemplar Cloud: Lessons for Unlocking Full Performance on AI Infrastructure]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-07-30T16:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [debugging](), [Processes]() Tags: [ai](), [ai-infrastructure](), [benchmarks](), [blackwell](), [cloud](), [cloud-services](), [cpu](), [data-center-cloud](), [debugging](), [developer-tools-techniques](), [dgx-cloud](), [diagnostics](), [distributed-training](), [featured](), [gb200](), [gb300-nvl72](), [grace-cpu](), [hardware](), [hopper](), [infiniband](), [infrastructure](), [kernel](), [linux](), [nvidia](), [nvl72](), [performance](), [training]() ### AI overview A troubleshooting guide for closing AI-training throughput gaps between NVIDIA reference architectures and partner clusters. It covers configuration and installation issues across memory management, CPU power and NUMA placement, NCCL queue-pair concurrency, and hardware setup. ### Source excerpt Two AI computing clusters built from identical NVIDIA H100, GB200 NVL72, or GB300 NVL72 systems can deliver materially different training throughput. We... ## What running Kubernetes across millions of clusters taught AWS about zonal failures DevFeed: [What running Kubernetes across millions of clusters taught AWS about zonal failures]() Original publisher: [Read original article]() Author: Raghav Tripathi Published: 2026-07-10T15:00:00Z Content type: article Language: en Sources: [Kubernetes Overview, News and Trends | The New Stack]() Topics: [Amazon EKS](), [Kubernetes](), [Amazon Web Services](), [Resilience](), [Availability]() Tags: [amazon-eks](), [api](), [availability](), [aws](), [aws-marketplace](), [cloud-services](), [health-checks](), [kubectl](), [kubernetes](), [networking](), [operations](), [outage](), [post-contributed](), [resilience](), [sponsor-aws-marketplace](), [sponsored-post-contributed]() ### AI overview AWS describes how years of engineering on Amazon EKS addressed zonal failures, including gray failures where a zone is degraded but still passes some health checks. The approach emphasizes static stability, preserving capacity, and routing around the impaired Availability Zone. ### Source excerpt Handling the obvious failures is the easy part: a server goes down, you spin up another. The failures that actually The post What running Kubernetes across millions of clusters taught AWS about zonal failures appeared first on The New Stack. ## Synthetic Data Generation for Financial AI Research with NVIDIA NeMo DevFeed: [Synthetic Data Generation for Financial AI Research with NVIDIA NeMo]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-07-09T19:40:37Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Synthetic Data Generation](), [synthetic-data](), [Large Language Model](), [Fine-tuning](), [Natural language processing](), [Nemotron](), [vllm](), [datasets]() Tags: [agentic-ai-generative-ai](), [ai](), [ai-agent](), [ai-ready-data](), [cloud-services](), [compute](), [data](), [data-science](), [featured](), [financial-services](), [fine-tuning](), [generation](), [hardware](), [inference](), [llms](), [models](), [natural-language-processing](), [nemo](), [nemotron](), [nvidia](), [research](), [structured-generation](), [synthetic](), [synthetic-data](), [synthetic-data-generation](), [vllm](), [workflow]() ### AI overview This developer article presents an iterative pipeline for generating a diverse synthetic dataset of more than 500,000 financial news headlines. It combines NeMo Data Designer for structured generation, NeMo Curator for semantic deduplication, Nemotron models for synthesis, and a farthest-from-centroid few-shot strategy to reduce repetition and correct category imbalance. ### Source excerpt Fine-tuning LLMs for financial natural language processing (NLP) is constrained by limited, imbalanced data. Real-world financial news overrepresents earnings... ## Hardware-Rooted AI Security That Won't Slow You Down DevFeed: [Hardware-Rooted AI Security That Won't Slow You Down]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-07-02T21:25:42Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Cryptography](), [Inference Performance]() Tags: [agentic-ai-generative-ai](), [ai](), [ai-agent](), [ai-inference](), [ai-security](), [benchmarks](), [blackwell](), [cloud-services](), [code-software-generation](), [data-center-cloud](), [dynamo-triton](), [encryption](), [featured](), [gpu](), [hardware](), [inference-performance](), [latency](), [nvlink](), [security](), [security-for-ai](), [software-defined-data-center](), [tensorrt](), [tensorrt-llm](), [trustworthy-ai-cybersecurity]() ### AI overview An overview of NVIDIA Confidential Computing for protecting AI inference workloads, including hardware-rooted attestation and encryption. It reports benchmark results of up to 98% of the inference performance of configurations without confidential-computing security. ### Source excerpt AI has transformed how organizations operate, driving unprecedented levels of productivity and innovation. However, AI adoption can be impeded by concerns... ## How Amazon tracks carbon intensity across its operations DevFeed: [How Amazon tracks carbon intensity across its operations]() Original publisher: [Read original article]() Author: Kerry Constabile Published: 2026-07-01T15:56:42Z Content type: news Language: en Sources: [Amazon Science homepage]() Topics: [amazon](), [data](), [Cloud](), [Routing (disambiguation)]() Tags: [amazon](), [amazon-sustainability-report](), [cloud-services](), [data](), [decarbonization](), [energy](), [operations](), [renewable-energy](), [routing](), [scale](), [sustainability](), [the-climate-pledge](), [transportation]() ### AI overview Amazon describes its use of sector-specific carbon-intensity metrics to track decarbonization across diverse operations. For retail, it measures emissions per unit shipped and reports a 39% reduction from 2019 to the end of 2025, attributing progress to carbon-free energy, routing improvements, lighter packaging, lower-carbon fuels, alternative transportation, and electric vehicles. ### Source excerpt Amazon is developing precise, sector-specific approaches to measuring decarbonization progress -- starting with emissions per unit shipped. ## Operating Kubernetes at scale: a few stories from running Amazon EKS DevFeed: [Operating Kubernetes at scale: a few stories from running Amazon EKS]() Original publisher: [Read original article]() Author: Neelendra Bhandari Published: 2026-06-30T13:00:00Z Content type: article Language: en Sources: [Kubernetes Overview, News and Trends | The New Stack]() Topics: [Amazon EKS](), [Kubernetes](), [Availability](), [systems](), [Apache Spark](), [AI Platform]() Tags: [agentic-ai](), [ai-infrastructure](), [amazon-eks](), [apache-spark](), [availability](), [aws-marketplace](), [cloud-services](), [data](), [gpu](), [kubernetes](), [post-contributed](), [real-time](), [sponsor-aws-marketplace](), [sponsored-post-contributed](), [systems]() ### AI overview This article describes how operating Amazon EKS at very large scale shaped approaches to Kubernetes control-plane resilience. It argues that availability problems often arise when components amplify faults, and explains how changing workload patterns--including foundation-model training, real-time inference, Apache Spark analytics, and agentic AI--create extremely rapid state transitions that challenge traditional Kubernetes operating assumptions. ### Source excerpt Amazon EKS runs hundreds of thousands of Kubernetes clusters across more than thirty AWS regions. Operating at that scale has The post Operating Kubernetes at scale: a few stories from running Amazon EKS appeared first on The New Stack. ## Optimizing cloud economics with linear elastic caching DevFeed: [Optimizing cloud economics with linear elastic caching]() Original publisher: [Read original article]() Published: 2026-06-25T10:03:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Caching](), [Cloud](), [Algorithms](), [Database](), [Serverless](), [Machine learning]() Tags: [algorithms](), [algorithms-theory](), [caching](), [cloud](), [cloud-services](), [cost](), [data-management](), [google](), [performance](), [real-time](), [serverless](), [systems]() ### AI overview Linear elastic caching applies the ski rental problem to dynamic cache sizing, balancing memory costs against cache misses. The approach treats memory as a time-dependent cost and adjusts cache capacity for changing workloads, aiming to reduce total cache-management expenses without compromising performance. ### Source excerpt Algorithms & Theory ## clickhousectl v0.2.0: Postgres, ClickPipes and more DevFeed: [clickhousectl v0.2.0: Postgres, ClickPipes and more]() Original publisher: [Read original article]() Author: Al Brown Published: 2026-05-20T20:54:03Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [API](), [Cloud](), [SQL](), [Command-line interface](), [Rust](), [API keys](), [Kafka](), [Docker]() Tags: [api](), [api-keys](), [cli](), [clickhouse](), [cloud](), [cloud-services](), [connectors](), [data](), [docker](), [http](), [kafka](), [release](), [rust](), [sql](), [update]() ### AI overview ClickHouse releases clickhousectl v0.2.0 with support for local and ClickHouse Cloud-managed Postgres, ClickPipes management, SQL over HTTP for Cloud services, agent-friendly output changes, and a standalone Rust client library for the ClickHouse Cloud API. ### Source excerpt We're releasing clickhousectl v0.2.0. This release adds Postgres, ClickPipes, SQL over HTTP, a few agent-friendly output tweaks, and a standalone Rust client library for the ClickHouse Cloud API. ## Doubling down on AI: Deeper investment, new talent DevFeed: [Doubling down on AI: Deeper investment, new talent]() Original publisher: [Read original article]() Author: Preeti Somal Published: 2026-04-30T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [Artificial Intelligence](), [distributed-systems](), [Tooling](), [Cloud](), [Orchestration]() Tags: [ai](), [ai-agents](), [announcements](), [cloud-services](), [developer](), [developer-tools](), [engineering](), [orchestration]() ### AI overview Temporal announces a deeper investment in AI, including new engineering leadership from Chkk and Adviser Labs. The article connects this investment to durable execution, reliability for long-running agentic systems, and AI-oriented developer tooling and cloud orchestration. ### Source excerpt Temporal doubles down on AI with new engineering leaders from Chkk and Adviser Labs, advancing durable execution, agentic AI, and developer tools at scale. ## From Single Instance to Split-Brain: A Database Scaling Journey DevFeed: [From Single Instance to Split-Brain: A Database Scaling Journey]() Original publisher: [Read original article]() Author: Alok Mishra Published: 2026-03-31T18:40:52Z Content type: tutorial Language: en Sources: [Walmart Global Tech]() Topics: [Databases](), [Replication](), [MySQL](), [PostgreSQL](), [Self-hosted](), [backups](), [Cloud](), [Monitoring](), [Amazon EC2](), [Kubernetes]() Tags: [architecture](), [backups](), [bare-metal](), [cloud](), [cloud-services](), [cloud-sql](), [cockroachdb](), [database](), [ec2](), [failover](), [google](), [google-cloud](), [google-cloud-sql](), [kubernetes](), [monitoring](), [mysql](), [postgresql](), [production](), [read-replica](), [recovery](), [software-architecture](), [software-development](), [software-engineering]() ### AI overview This article explains how database scaling commonly uses a single-leader architecture with asynchronous read replicas. It discusses replication lag, stale reads, split-brain risks, read/write traffic separation, and the operational responsibilities of self-managed versus fully managed database services. ### Source excerpt I used to think adding a 'Read Replica' was a magic button for scaling applications. I was wrong. While splitting read and write traffic is a standard system design pattern, implementing it introduces a world of pain - from stale reads to the dreaded Split-Brain problem. Here is how database replication actually works, and how to survive the transition. When people talk about "scaling databases" or "adding read replicas", they are almost always thinking about one specific architecture: Single-leader (Primary-Replica) architecture with asynchronous replication This is the architecture used by: MySQL + replicas PostgreSQL + streaming replication Google Cloud SQL PlanetScale, Neon, Supabase, etc. There is exactly one node that accepts writes -> called the Primary (or Leader/Master). All other nodes are Read Replicas -> they apply changes from the primary as fast as they can, but always with some delay (replication lag). This is the default and dominant model in 99% of applications today. Alternative architectures exist (multi-primary, leaderless, CRDTs, etc.), but they are rare and come with their own very different trade-offs. The second axis that actually matters in practice is: Who manages the replicas and failover for you?1. Self-hosted / Self-managed You run MySQL or PostgreSQL yourself (on EC2, Kubernetes, bare metal, etc.). You are 100% responsible for: Setting up replication Promoting a new primary when the old one dies Routing traffic correctly Handling replication lag Monitoring, backups, point-in-time recovery, etc. 2. Fully-managed cloud services RDS, Aurora, PlanetScale, Neon, Supabase, CockroachDB, Spanner, YugabyteDB, etc. The provider gives you a single connection string (or two: one for writes, one for reads) and magically keeps it pointing to healthy nodes, handles failover in seconds, and often hides (or eliminates) replication lag headaches. This second axis is the one that determines how much pain you will actually feel in production. Now, suppose yo ## Laravel Cloud Incident Report: February 20, 2026 DevFeed: [Laravel Cloud Incident Report: February 20, 2026]() Original publisher: [Read original article]() Author: Laravel Team Published: 2026-02-20T15:20:00Z Content type: article Language: en Sources: [Laravel Blog]() Topics: [incident](), [Network architectures](), [Network Services]() Tags: [api](), [bgp](), [cloud-services](), [cloudflare](), [incident](), [infrastructure](), [laravel](), [outage]() ### AI overview Laravel Cloud reports a roughly 3-hour, 15-minute connectivity outage caused by Cloudflare withdrawing IP prefix advertisements. Applications and infrastructure remained operational, but users could not reach affected services; Laravel Cloud plans additional IP-announcement failover protection. ### Source excerpt ## Summary On February 20, 2026, [Laravel Cloud](https://laravel.com/blog/one-minute-or-less-how-we-built-laravel-clouds-architecture) experienced a connectivity outage lasting approximately 3 hours... ## LongNosedGoblin tries to sniff out governmental affairs in Southeast Asia and Japan DevFeed: [LongNosedGoblin tries to sniff out governmental affairs in Southeast Asia and Japan]() Original publisher: [Read original article]() Author: Anton Cherepanov Peter Strýček Published: 2025-12-18T10:00:00Z Content type: article Language: en Sources: [WeLiveSecurity]() Topics: [backdoor](), [.NET](), [enterprise deployment]() Tags: [apt](), [backdoor](), [browser](), [c-sharp](), [china](), [cloud](), [cloud-services](), [eset-research](), [google](), [japan](), [malware](), [microsoft](), [net](), [policy](), [techniques](), [windows]() ### AI overview ESET describes LongNosedGoblin, a China-aligned APT group conducting cyberespionage against governmental entities in Southeast Asia and Japan. The group deploys malware through Group Policy and uses tools including the NosyDoor backdoor and the C#/.NET NosyHistorian application. ### Source excerpt ESET researchers discovered a China-aligned APT group, LongNosedGoblin, which uses Group Policy to deploy cyberespionage tools across networks of governmental institutions ## How AWS Is Helping Enterprises Build, Scale, and Innovate With AI DevFeed: [How AWS Is Helping Enterprises Build, Scale, and Innovate With AI]() Original publisher: [Read original article]() Author: TJ URGLAVITCH, CLOUD SERVICES PRACTICE LEAD @ TOPTAL Published: 2025-11-30T23:00:00Z Content type: article Language: en Sources: [Toptal Blog]() Topics: [Amazon Web Services](), [Artificial Intelligence](), [cloud-infrastructure](), [Security & compliance, Cloud security](), [AI Infrastructure]() Tags: [ai](), [ai-adoption](), [ai-infrastructure](), [aws](), [cloud](), [cloud-infrastructure](), [cloud-services](), [collaboration](), [governance](), [podcast](), [scale](), [security](), [transformation]() ### AI overview This Executive Guidance podcast discusses how AWS helps enterprises adopt AI, scale innovation, and modernize operations through cloud infrastructure, partner collaboration, responsible design, security, and governance. ### Source excerpt Discover how AWS's Head of Partner Solutions Architecture, Divyesh Shah, is helping enterprises scale responsibly and innovate faster through the power of cloud and AI. In this episode of the Executive Guidance podcast, Shah joins Toptal's Cloud Services Practice Lead, TJ Urglavitch, to discuss how AWS builds trust, empowers collaboration, and drives transformation across industries. [Next page]()