标签：#Cloud

Sam Newman discusses the concept of progressive collapse in civil engineering and how it applies to distributed systems. Using real-world examples - from the 1968 Ronan Point tower failure to AWS outages - he shares crucial resilience engineering strategies for software leaders. Learn how to strengthen components, isolate failures, and reduce interconnections to prevent catastrophic cascades. By Sam Newman

I got an AWS question and implemented it to make sure that the option is correct. A critical financial application runs on RDS for PostgreSQL. The requirements are tight: 1-second RPO, 60-second RTO, and database credentials rotated every 30 days without taking the application offline. Two independent problems. Two independent solutions. Prerequisites Check these before running terraform apply : RDS Proxy availability RDS Proxy is not available on all instance types. It requires instances with at least 2 vCPUs. db.t3.micro is not supported. db.t3.medium and above work. Terraform executor permissions The IAM principal running Terraform needs, at minimum: rds:CreateDBInstance rds:CreateDBProxy rds:CreateDBProxyTargetGroup rds:RegisterDBProxyTargets rds:ModifyDBInstance iam:CreateRole iam:AttachRolePolicy iam:PutRolePolicy iam:PassRole secretsmanager:CreateSecret secretsmanager:PutSecretValue secretsmanager:RotateSecret lambda:CreateFunction lambda:AddPermission ec2:CreateSecurityGroup ec2:AuthorizeSecurityGroupIngress ec2:CreateDBSubnetGroup AdministratorAccess on the account covers all of these. Lock it down after the initial setup. VPC requirements RDS Proxy runs inside your VPC. You need at least two private subnets in different Availability Zones. The rotation Lambda also runs inside the VPC so it can reach the RDS instance directly during the credential update step. The problem Database failure recovery RPO of 1 second means almost no data loss is acceptable. RTO of 60 seconds means the application must resume within a minute of a failure. A standard single-instance RDS setup fails both requirements: there is no automatic failover, and restoring from a backup takes far longer than 60 seconds. Credential rotation Rotating credentials on a schedule sounds simple until you factor in application downtime. If you update a password and the application still holds connections authenticated with the old one, those connections fail. The rotation mechanism needs to handle

Cloudflare is introducing WriteGuard, now in private beta, to provide fine-grained security controls for MCP (Model Context Protocol) servers. It aims to make AI agents safer by controlling their access to tools that can modify data or perform actions, rather than simply read information. By Sergio De Simone

Eight user agents, and the one that decides whether ChatGPT cites you is not among them. An r/SEO post from April, 53 points and 40 comments, says Cloudflare quietly cut the author's site off from ChatGPT, from Perplexity and from Google's AI Overviews. I went and read the block. It names ChatGPT's training crawler and not its search crawler, and it never mentions Perplexity at all. The Google agent it does name is one Google says does not affect inclusion or ranking in Search. Take just two of the eight agents. GPTBot governs inclusion in OpenAI's training data, and Google-Extended governs grounding in Gemini Apps. Those are real things to give up. They are not the three things the warning names. What is actually in the file Cloudflare prints the whole block on its managed robots.txt page : # BEGIN Cloudflare Managed content User - Agent : * Content - signal : search = yes , ai - train = no , use = reference Allow : / User - agent : Amazonbot Disallow : / User - agent : Applebot - Extended Disallow : / User - agent : Bytespider Disallow : / User - agent : CCBot Disallow : / User - agent : ClaudeBot Disallow : / User - agent : Google - Extended Disallow : / User - agent : GPTBot Disallow : / User - agent : meta - externalagent Disallow : / # END Cloudflare Managed Content Read that against Cloudflare's own crawler reference table and a pattern falls out. GPTBot is in, OAI-SearchBot is out. ClaudeBot is in, Claude-SearchBot is out. For both of those pairs the table calls the blocked agent an AI Crawler and the one left alone AI Search. Applebot-Extended is in and plain Applebot is not, the same split again. The table has no Applebot-Extended row, so it cannot tell you what Cloudflare calls that one. The block runs along the training and search seam, and that looks deliberate to me. OpenAI's side of it is one line: "Each setting is independent of the others". I worked through the three OpenAI bots and which one governs search visibility in an earlier post , so I will

post 7: A practical guide to understanding Docker Compose—what it is, how it works, and the misconceptions that catch most beginners. You've mastered single containers. Now it's time to build a real application. A frontend. A backend. A database. A Redis cache. Suddenly you're juggling multiple docker run commands. Ports. Networks. Volumes. Environment variables. Chaos. Then someone says: "Just use Docker Compose." It works beautifully. But here's the twist most people never realize… Why Docker Compose Exists Imagine starting an application like this: Frontend Backend PostgreSQL Redis Running each container manually quickly becomes repetitive and error-prone. Docker Compose lets you describe your entire application in a single YAML file and start everything with one command. Instead of remembering dozens of commands, you define your infrastructure once. What Docker Compose Actually Is Docker Compose is not a container orchestrator . Docker Compose is a tool that reads your Compose YAML file and uses the Docker Engine to create and manage the resources defined in it.” Modern Docker uses Compose V2 , which runs as: docker compose instead of the older: docker-compose Compose runs only when you execute a command. It creates the required Docker resources, starts the containers, and then exits. This makes it ideal for development, testing, and single-host deployments , but it doesn't provide orchestration features like automatic scheduling, self-healing, or multi-node management. A Simple docker-compose.yml services : web : build : . ports : - " 8080:80" environment : - DB_HOST=db depends_on : - db db : image : postgres:15 volumes : - postgres_data:/var/lib/postgresql/data redis : image : redis:alpine volumes : postgres_data : YAML Quick Reference Key Purpose services Defines containers (web, db, redis) build Builds an image from a Dockerfile image Uses an existing image from a registry ports Maps host ports to container ports environment Sets environment variables depend

Groq raised $350 million at a $3.5 billion valuation as the former AI chipmaker pivots to a neocloud business and expands its Nvidia-powered data center footprint.

🌐 Network Devices Explained The Foundation Every Cloud & DevOps Engineer Needs Series: Networking Fundamentals for Cloud & DevOps — Part 1 of 6 Before VPCs, subnets, route tables, and security groups make sense, you need to understand what's happening beneath them. This series builds that foundation — starting with the devices that make networks work. Why Networking Before Cloud? I hit a wall during my AWS VPC sessions. Route tables, subnets, gateways, NACLs — the concepts existed in isolation. I could follow steps in the console, but I couldn't reason about why traffic was or wasn't flowing. The fix wasn't more AWS documentation. It was going back to networking fundamentals. Once I understood what a router actually does — how it makes forwarding decisions, what a routing table really is — the AWS route table stopped being a mysterious config screen and became something I could think through. That's what this series is. Six posts covering the networking concepts that directly underpin Cloud and DevOps work. No exam prep framing, no CCNA depth. Just what you actually need. 1. What is a Host? A host is any device that participates in network communication by sending or receiving traffic. That's broader than most people assume. Examples: your laptop, your phone, an EC2 instance, a web server, a virtual machine. The word "host" doesn't imply a server — your laptop is a host just as much as a data center machine is. 2. Client vs Server — Roles, Not Hardware A client is a host that initiates a request. A server is a host that responds. The critical point: a server is not a special type of computer . It's just a computer running software that listens and responds. Your Browser (Client) │ │ HTTP Request ▼ Web Server (Server) │ │ HTTP Response ▼ Your Browser (Client) The same machine can be a client in one communication and a server in another. Your EC2 running a web app is a server to users hitting it — and a client when it queries RDS. 3. IP Address — The Network Identity

Cloudflare has released cloudflare/ci, a CI SDK that defines pipelines in TypeScript on top of Cloudflare Workflows, giving each step durable retries and replay, concurrent steps by default and Sandbox snapshot caching. It targets the Workers runtime and depends on Artifacts, still in private beta, so the transferable lesson is the durable-step model rather than a drop-in CI replacement. By Mark Silvester

By: Pierre Le Clezio, Lead Product Manager – GitGuardian; Nic Gumina, Senior Security Consultant – AWS; Manu Chandrasekhar, Senior DevOps Consultant – AWS; Dan Parlin, Security Consultant – AWS This article was originally published at AWS blogs . The rise of AI coding assistants and Model Context Protocol (MCP) servers has accelerated the secret management challenge as developers increasingly share configuration files and context with AI tools that inadvertently expose sensitive credentials. API keys, access tokens, and credentials end up in Git repositories and CI/CD logs. Organizations lack answers to critical questions. They don't know which vaulted secrets have been exposed in code, whether developers have shared credentials through AI tool configurations, how many duplicate credentials exist across accounts, or how many orphaned secrets remain that no application uses. The visibility gap leads to: Credential exposure : Hardcoded secrets in version control systems create attack vectors that persist even after rotation Secret sprawl : Duplicate credentials across accounts expand your attack surface Compliance gaps : Inability to track secret lifecycles undermines audit requirements Remediation delays : Without correlation between secret inventory and code exposure, security teams lack the context to prioritize and act quickly With multi-account AWS architectures, the need for unified visibility becomes critical. Organizations need more than just a vault. They need visibility across the entire secret lifecycle, from developer workstations to production environments. GitGuardian and AWS Secrets Manager GitGuardian is an AWS Partner specializing in non-human identity (NHI) security, which focuses on protecting machine credentials such as API keys, service accounts, tokens, and secrets management. GitGuardian can be integrated with code repositories, container registries, package registries, documentation platforms, and messaging channels. GitGuardian's integration w

✓ Human-authored analysis; AI used for formatting and proofreading. Kelsey Hightower gave a talk at PlatformCon 2026 that was about the arc of a career, from running commands in SharePoint to writing Go tools that play music on your terminal. The stories has an architecture principle that applies to how security verification should work. Solve the problem once, encode the solution as a reusable artifact, and never solve that problem again. The Jira loop He joined a company where deployments were driven by Jira tickets. Someone opens a ticket with deployment parameters. An engineer would read the ticket, copy the parameters, run the commands, paste the output back into the ticket, close it, and wait for the next one. Every hour, another ticket. Same process, commands and manual steps. The engineer became the loop. He wrote a Puppet manifest that watches the tickets, extracts the parameters, runs the deployment, posts the output, and closes the ticket. The loop ran once as automation and then it was over. No engineer in the loop or ticket waiting for a human. The problem was solved, permanently, by encoding the solution into a reusable artifact. Doing a repetitive manual process faster is not the right thing to do. Eliminate the loop by recognizing the abstraction hiding in the repetition and encoding it into an artifact that makes the manual steps unnecessary. The substrate This is the pattern that runs through every transition he describes. It's missed by most people when they talk about automation. System administrators ran deploy.sh manually. Docker didn't automate typing apt-get install . Docker recognized that "application + dependencies + environment" was a repeatable unit. The container image became the substrate. Deployment stopped being a sequence of commands and became a declaration. The commands didn't get faster. They became unnecessary. Operators placed workloads on servers manually. Kubernetes didn't automate SSH-ing into machines to check available mem

AWS has open-sourced Dogwood, a policy language extending Cedar with temporal conditions so rules can reason about an agent's prior tool calls rather than one request in isolation. It covers approvals, rate limits and running totals, ships under Apache 2.0, and is supported in AgentCore Policy, though the reference interpreter is not production-ready. By Steef-Jan Wiggers

Amazon DynamoDB recently introduced native vector search, allowing developers to store embeddings alongside application data and run approximate nearest-neighbor queries directly from DynamoDB without using a separate vector database. The feature supports filtered similarity searches and configurable vector indexes for semantic search workloads. By Renato Losio

I build a web service that preserves evidence of harassment on social platforms. The core feature is a single thing: automatically capture a real screenshot of the offending post. There was no substitute for it. I built an alternative that pulled the text through an API and rendered a tidy "evidence card" image, and threw it away. An image you can author freely afterwards proves nothing. Here's the conclusion first. Third-party wrappers eventually die, and when they do, the failure comes back as a plausible-looking image rather than an error. The first approach was refused by the other side I started with Cloudflare Browser Rendering. The wiring worked. The capture didn't. X blocks headless browsers. The request times out YouTube refuses script injection under a Trusted Types CSP. There's no way to make it render the comment Neither is a bug in my implementation — that is how they are built. So I declared Cloudflare alone impossible for this and moved to a service with a real browser and bot avoidance behind it. Both captures started working. For X, open the post page and clip the tweet element. For YouTube, open the URL with &lc= and screenshot just that comment element. Element screenshots have one trap worth knowing: selector_algorithm=clip returns a blank image when the element sits below the fold. The selector matches, the capture "succeeds," and the file is empty. That took a while to see. ytd-comment-thread-renderer :has ( a [ href *= "lc=ID" ]) A parameter that had worked started returning 400 I wanted timestamps rendered in Japan time, so I passed time_zone: Asia/Tokyo . One day every request started coming back 400. Every capture failed. The provider had narrowed which timezones they accept. Nothing changed on my side. I could diagnose it immediately only because I was storing the raw error body in the database. The response went into rawPayload.screenshotError , so opening one row told me why. Without that, this starts as "captures stopped working, no ide

Cloud computing is a fundamental pillar of modern network architecture, shifting infrastructure management from physical data centers to flexible, virtualized environments. This guide breaks down core cloud concepts, architecture models, service types, and operational characteristics aligned with CompTIA Network+ objectives. Virtualization and Network FoundationsNetwork Functions Virtualization (NFV)NFV replaces dedicated, proprietary hardware appliances (such as firewalls, load balancers, and routers) with virtual appliances running on standard servers. This decouples network functions from physical hardware, allowing for rapid deployment, easier scaling, and reduced capital expenditure.Virtual Private Cloud (VPC)A Virtual Private Cloud (VPC) provides an isolated, private cloud environment dedicated to a single customer within a shared public cloud infrastructure.Resource Separation: Uses subnets, VLANs, and tunneling to isolate compute, storage, and networking resources.Control: Customers have full administrative control over their network configuration, IP address ranges, and routing tables.Security: Regulated via Network Security Groups (NSGs) and Access Control Lists (ACLs) to govern traffic entering and leaving subnets.Cloud Gateways & Connection MethodsCloud gateways serve as translation points or secure entryways between on-premises networks and cloud environments. Organizations connect to cloud resources using several methods:Site-to-Site VPNs: Encrypted tunnels over the public internet connecting an on-premises office or data center to a VPC.Dedicated Interconnects (e.g., AWS Direct Connect, Azure ExpressRoute): High-speed, private, dedicated circuits that bypass the public internet for enhanced security, lower latency, and predictable performance. Cloud Deployment ModelsCloud architecture defines where infrastructure is hosted and who manages the underlying hardware.ModelCharacteristicsBest Suited ForPublic CloudOwned and operated by a third-party provide

Cloudflare launched agent tracing, adding spans for agent invocations, model calls, tool runs, and approvals to existing Workers traces. Sessions replay turn by turn, though the docs warn traces are not lossless and payloads may be truncated. Payload recording defaults differ by framework, and from October 1, 2026 every span counts as a billable event. By Steef-Jan Wiggers

Cloudflare recently introduced Cache Response Rules, a rules engine that operates after an origin server responds but before content is written to Cloudflare's cache. Previously, Cache Rules operated only on request attributes. Cache Response Rules add a response phase that evaluates origin responses before they are cached. By Renato Losio

When you build a system that detects key moments in a live sports feed, one architectural decision shapes everything downstream: where does the inference happen? At the edge, close to where the video is produced, or in the cloud, after the stream has been ingested? There is no universally right answer, but the trade-offs are sharp and worth laying out. The case for the edge Running detection near the source, at the venue or in an on-prem encoder, minimizes the round trip. The video does not have to travel to a data center and back before a moment is flagged, which can shave critical seconds off the time to clip. For genuinely live use cases, where a clip is worthless if it lands a minute late, that latency saving is the whole game. Edge inference also reduces egress: do the heavy frame analysis locally and ship only the clips that matter, instead of streaming everything to the cloud. The cost of the edge Edge hardware is constrained. You run on whatever GPU or accelerator fits in the rack at the venue, not on an elastic fleet. That bounds model size and concurrency. Updating models across many distributed edge nodes is an ops problem in itself, and a venue that hosts one event a week is idle hardware the rest of the time. Edge is fast but inflexible. The case for the cloud The cloud gives you elastic compute, easy model updates, and the ability to run larger or ensemble models you could never fit at the edge. If you process many concurrent streams, centralizing inference pools capacity instead of overprovisioning every venue. For workflows where a few seconds of extra latency is acceptable, near-live rather than instant, the cloud is simpler to operate and cheaper to scale. The cost of the cloud You pay for it in latency and bandwidth. Every frame you want to analyze has to be ingested first, and for high-bitrate broadcast feeds that adds up. The end-to-end path, capture, encode, transport, ingest, infer, clip, deliver, has more hops, each adding delay and a potenti

Google has launched Gemini 3.7 Flash, providing significant updates for coding, automation, and the development of autonomous agents. The company reduced production pricing to help businesses deploy these tools more affordably. This release comes only three weeks after the previous version, signaling a faster pace for developer-focused updates. Accelerated development cycles and cost reduction strategies The introduction of Gemini 3.7 Flash highlights a shift in how technology providers manage their product lineups. Google is prioritizing rapid iteration for its Flash series, which serves as a high-speed tool for developers. This latest version arrived less than a month after its predecessor, showing the company responds quickly to user feedback. Engineers designed this model to handle software engineering tasks and complex, multi-step workflows with higher precision. Pricing for the new model sits at $0.75 per million input tokens and $3.75 per million output tokens. This represents a reduction of approximately fifty percent compared to the prior version. By lowering the financial barrier, Google aims to make large-scale production deployments more sustainable for businesses. The company describes this version as a reliable workhorse capable of following instructions with greater accuracy than previous iterations. While the Flash series moves quickly, the more advanced Pro models follow a different path. These high-end models, designed for the most difficult reasoning tasks, see less frequent updates. During recent financial discussions, leadership at the company did not provide a specific timeline for the next Pro release. This indicates a growing gap between fast, cost-effective models and the slower development of premium intelligence tiers. Industry trends in model tiering Other companies in the industry are following similar patterns by separating their offerings into distinct categories. For example, some competitors have launched high-end variants alongside

"We don't have access to the data on the hardware/servers," Iron Mountain told Ars.

Last week we kicked off a series covering the (data/storage/network) announcements that, in my view, had the most impact. This week I'm bringing 5 more recent launches to run through the fine-tooth comb. Description, the pain point it solved, and my critical take on each one. 1. Aurora DSQL lands in five more regions What it is: Aurora DSQL, AWS's distributed, serverless SQL database, is now available in Asia Pacific (Hong Kong), Asia Pacific (Mumbai), Asia Pacific (Singapore), Europe (Stockholm), and South America (São Paulo). With this launch, it now covers 19 regions total. Official link: https://aws.amazon.com/about-aws/whats-new/2026/05/amazon-aurora-dsql-five-additional-aws-regions/ How it used to be (the pain point): Until now, if you wanted to run Aurora DSQL with decent latency for users in Brazil, there was no regional option here. You either accepted the latency of hitting us-east-1 (an ugly phrase for a transactional application to hear) or gave up on DSQL entirely and went with a traditional Aurora PostgreSQL, purely for geographic proximity. My take: region expansion is the kind of announcement that isn't sexy, but it's what decides whether a service actually goes into production or stays a showcase item. With São Paulo on the list, the main adoption blocker for DSQL around here (latency) drops significantly. That said, it's worth remembering DSQL is still a relatively young product, with compatibility limitations against full PostgreSQL (extensions, certain data types, certain transactional features). Regional presence solves latency, it doesn't solve feature-set maturity. Those are two separate conversations. 2. Aurora DSQL gets a native PHP connector What it is: launch of the Aurora DSQL connector for PHP (PDO_PGSQL), which handles IAM authentication natively: generates a token per connection, guarantees the valid token gets used, and keeps full compatibility with the PDO_PGSQL that the PHP ecosystem already knows. It also brings retry with exponent