标签：#infrastructure

Uber’s GitFarm provides Git operations as a centralized service, eliminating local repository clones across large scale monorepo workloads. The platform uses prewarmed checkouts, ephemeral sandboxes, repository synchronization, and gRPC streaming to reduce resource consumption and startup latency for automation services operating across thousands of repositories. By Leela Kumili

Golden Images How I Stopped Manually Logging Into Every New Server The problem Every time I spun up a new server for a service, it worked but it wasn't actually ready . There was always one manual step left: log in, run through some interactive setup, get the application into a working state. Only after that could the server actually do its job. For one server, that's a minor annoyance. For a fleet that's supposed to scale up and down on demand, it's a dealbreaker. You can't call something "automated provisioning" if a human still has to remote in and click through a setup wizard before it's usable. The fix: capture the setup once, replay it everywhere The pattern here is usually called a golden image and the idea is simple: instead of repeating a manual setup step on every new machine, do it once, capture the result of that setup, and have every future machine apply that captured state automatically during provisioning. Concretely, I built a small tool that: Connects to a machine that's already been through the manual setup and is in a known-good state. Packages up just the state that setup actually produced not the whole machine, just the specific files/config that resulted from the manual steps. Uploads that package to storage, versioned. Then the provisioning script for every new machine downloads that package and applies it automatically as part of boot no human, no remote session, no wizard. The mistake worth mentioning My first version of this captured too much. Instead of packaging just the setup-derived state, it grabbed an entire application data folder which included the application's own installed binaries, not just the configuration that setup had produced. That meant every new machine, when it applied the "golden" package, got its fresh application install silently overwritten with whatever binary version happened to be running on the machine I captured from. New servers ended up running an older version of the software than the one they'd just install

The federal cyber agency's warning comes amid a wave of suspected Iran-backed cyberattacks targeting critical water systems across the United States.

DigitalOcean App Platform is a metered system charged by app; Peon provisions limitless services to your existing Droplet. A practical pricing and feature comparison. The same cloud, but two very distinct approaches. There are two methods of deploying your app with DigitalOcean, and the pricing disparity between the two may be much greater than you expected. App Platform is the managed PaaS service: you integrate with the code repository, and DigitalOcean provisions, deploys and maintains your app. The costs include monthly rates per component starting at $5 for web services plus separate payments for workers plus $7+ for a development database and $15+ for a production database. The alternative way is just a regular Droplet: either a $6 VPS (1 CPU, 1 GB) or a $12 VPS (1 CPU, 2 GB) with ability to run as many containerized apps as it has available resources. Traditionally, the droplet approach required self-managing your infrastructure, exactly what a platform like Peon fixes. Cost at small scale, with real numbers For example, take a regular indie/agency load of three small apps, shared Postgres, and Redis. In App Platform, this would cost about $37 a month, where three web services ($15), a managed dev database ($7), and Redis ($15) are the cheapest tier offerings (share CPU, limited to 512 MB memory). On one $12 Droplet using Peon, $12 for the Droplet, $6 for three projects running, all with access to 2 GB of memory plus. About $18 per month total, and the ability to use as much memory as the application needs (without being limited to 512 MB slices). And this ratio grows with every additional service, as the costs for the additional Droplet resources are already included. The fourth app on App Platform will add somewhere between $5 and $12 of the bill; on your own Droplet, $2. Comparison of features Push Git deployment: both, with build log Automatic HTTPS for custom domains: both Roll out and roll back with zero downtime: both Database support: App Platform nee

Building a Serverless Weather Pipeline on AWS: A Step-by-Step Walkthrough This is a build log for someone who's used AWS a bit — deployed a Lambda from the console, poked around S3 — but hasn't touched CDK, Step Functions, EventBridge Scheduler, or GitHub's OIDC setup before. I'll explain each concept the first time it comes up, and show the actual code behind every piece, roughly in the order I built it. Here's what it ends up doing: every 10 minutes, EventBridge Scheduler kicks off a Step Functions workflow that pulls current weather for five cities in parallel from a free public API, reshapes the results into JSON Lines, drops them into S3 in a partitioned layout, and makes them queryable in Athena with plain SQL. No crawler, and no AWS credentials sitting anywhere in the GitHub repo that deploys it. kasukur / serverless-weather-pipeline AWS Serverless Weather Pipeline Serverless Weather Data Pipeline A small but complete serverless data pipeline on AWS walkthrough: EventBridge Scheduler → Step Functions → Lambda → S3 → Glue/Athena , deployed by GitHub Actions with no AWS access keys stored anywhere (authentication is via GitHub's OIDC provider). flowchart TD A["EventBridge Scheduler (every 10 min)"] --> B["Step Functions state machine"] B --> C["PrepareCities (Pass)"] C --> D["ForEachCity (Map, concurrency 4)"] D --> E["FetchWeather (Lambda -> Open-Meteo public API)"] E -.-> F["retries transient errors (up to 2 attempts)"] E -.-> G["FetchFailed (Pass): per-city failure absorbed here, other cities continue"] E --> H["TransformWeatherData (Lambda, pure function, no AWS calls)"] H -.-> I["splits successes vs failures"] H -.-> J["builds JSON-Lines body + partitioned S3 key"] H --> K["LoadToS3 (Lambda, writes to S3 via boto3)"] K --> L["S3 (processed/dt=YYYY-MM-DD/hour=HH/*.jsonl)"] L --> M["Glue Data Catalog table (partition projection -- no crawler)"] M --> N["Athena (query with plain SQL)"] D -.-> … View on GitHub Table of Contents What we're building, and why eac

Seri: Proxmox VE Cluster ve Corosync | Hafta 5 Serinin adı "Cluster ve Corosync"; ama dört modüldür ağırlık HA Manager, resource affinity ve CRS'teydi, Corosync'in kendisine (redundant link'ler, izleme araçları) hiç dönmemiştim. Bu modülde iki konuyu birleştirip derinlemesine işledim: birden fazla corosync link'i tanımlayıp gerçekten birini kesip diğerinin devralmasını kanıtlamak, ve günlük operasyonda kullanılacak izleme araçlarını tek tek denemek. İkisi de planladığımdan çok daha fazla soru açtı; biri yanlış bir config anahtarı yüzünden saatler süren bir araştırmaya dönüştü, diğeri ise hiç beklemediğim bir kilitlenme keşfiyle bitti. Bölüm 1: Redundant Corosync Links Kurulum: İkinci Link'i Eklemek Şu ana kadar cluster'ımızda tek bir corosync link'i vardı ( link1 , izole corosync-net ağı). Management ağını ( 192.168.122.x ) link0 olarak ekleyip gerçek bir yedeklilik kurdum; /etc/pve/corosync.conf 'u kopyalayıp düzenleyip atomik olarak yerine taşıdım: cp /etc/pve/corosync.conf /etc/pve/corosync.conf.new # nodelist'teki her node'a ring0_addr ekledim, totem'e ikinci bir interface bloğu ekledim mv /etc/pve/corosync.conf.new /etc/pve/corosync.conf Doğrulama: corosync-cfgtool -s LINK ID 0 udp addr = 192.168.122.11 status: ... connected ... connected LINK ID 1 udp addr = 10.10.10.11 status: ... connected ... connected Teknik olarak başarılı; iki link de bağlı. Ama log'a dikkatlice bakınca, mimarimizin niyetini tersine çeviren bir şey oldu: [KNET ] rx: host: 3 link: 0 is up [KNET ] host: host: 3 (passive) best link: 0 (pri: 1) link_mode: passive modunda, öncelik eşitken düşük numaralı link kazanıyor . link0 'ı sonradan eklediğim için, o Corosync'in asıl trafiğini üstlenmiş; Modül 0'da özellikle izole ettiğimiz corosync-net ( link1 ) sessizce yedek konuma düşmüştü. Yanlış Anahtar, Saatler Süren Bir Araştırma Bunu düzeltmek için link1 'e daha yüksek öncelik vermeye çalıştım: interface { linknumber : 0 priority : 5 } interface { linknumber : 1 priority : 10 } İşe yaramadı. cor

Shift-left and DevOps have impacted how we flow changes from inception to production, but at the cost of increased cognitive load and duplication of effort across testing, security, and maintenance. This article explores the real-world challenges of rightsizing developer platforms and finding a cultural match for engineering teams who use them to reduce cognitive load and deliver change faster. By John Keates

OVHcloud will raise prices from September, with 2026-edition gaming servers up 87 percent and other recent servers 40 to 59 percent. Founder Octave Klaba says memory cost six times more in June than a year earlier, as RAM suppliers shifted capacity toward high-bandwidth memory for AI. AWS, buying years ahead, has repriced one reserved GPU product. By Steef-Jan Wiggers

The Nvidia Cloverleaf data center partnership announced on Friday tells you where the real constraint in AI has moved, and it is not the chip. TechCrunch reported that Nvidia has taken a minority stake in Cloverleaf , a company founded in 2024 that raised $300 million that year and sits between utility companies and data centers, arranging power and site infrastructure. Nvidia did not buy a chip designer. It bought a piece of the electricity supply chain. That reframing is worth thinking about if you build software from anywhere outside a well-supplied grid. 🔌 The bottleneck moved from silicon to substations Read the deal literally. Cloverleaf's product is not compute. It is power sourcing and site infrastructure — the interconnect agreements, the substations, the land next to a utility that can actually deliver load. Nvidia buying into that layer is an admission that shipping more GPUs does not help if nobody can plug them in. This was not a one-off either. The same week, per the reporting: Deal Announced Reported size What Nvidia bought into SB Energy (OpenAI-linked, Ohio) 17 Aug 2026 $1.5 billion Data center project with an energy parent Cloverleaf 21 Aug 2026 Several hundred million (WSJ), minority stake (Reuters) Utility-to-data-center power intermediary Terms were not disclosed by either company, so treat the dollar figures as press reporting rather than filings. Key takeaway: When the company that sells the shovels starts buying the mines, the scarce input is no longer shovels. For AI in 2026, the scarce input is grid capacity. 💰 The circular money problem you should price in Here is the part I would not skip over. Nvidia sells GPUs to data centers. Nvidia is now also investing in the companies that build and power those data centers . Some of that capital flows back as GPU orders. That is not illegal or even unusual in capital-intensive industries. Telecom vendors financed carriers for decades. But it has a specific consequence for you as a buyer of compute:

Did you know there's a new major version in town for Puppet Core? You might have heard about it through the grapevine or in the Are You Ready for Puppet 9? webinar that @gpatton and I recently hosted. The wait is over and Puppet Core 9.0.0 is now available alongside Puppet Core 8.21.0. Puppet Core 9 introduces significant runtime and platform changes, moving to Ruby 4.0, OpenSSL 3.5, and other changes, but the essential Puppet under the hood is largely unchanged from Puppet 8. The majority of upgrade effort will center on Ruby 4 compatibility and runtime dependency changes rather than Puppet language changes. If you are staying on the Puppet Core 8.x release track, the latest Puppet Core 8.21 delivers the basic support fixes and security improvements you might need without the major dependency changes found in Puppet Core 9. What matters most for the admins Before upgrading to Puppet Core 9: Test custom facts, functions, types, and providers against Ruby 4.0. Validate any Forge modules you use for Ruby 4 compatibility. Review integrations that depend on OpenSSL behavior. Verify any workflows that still rely on SHA-1. Confirm managed nodes are running supported operating systems. Review any custom code that depends on PSON or multi_json . Check deferred function behavior if you have custom types or providers. Perforce will be rolling out updates to Puppetlabs modules on the Forge based on their priority tier and dependencies. The first batch of these should be rolling out soon. Puppet Core 9.0 highlights These are a few highlights I pulled from the release notes. Make sure to reference the full 9.0 release notes to get all the details about what has changed! Ruby updated to 4.0.5: With a new Ruby baseline some deprecated syntax from older Ruby versions will no longer be compatible. This is the primary focus area for upgrades as you will want to validate your custom code and modules. The latest PDK 3.8.0 introduced some Ruby 4 validators to help you update your syntax

James Arthur shares why sync is the next frontier in frontend architecture. He explains how extending reactivity to the server with Electric and TanStack DB replaces imperative fetching with declarative data bindings. Learn how query-driven sync and local optimistic updates enable engineering leaders to build insanely fast, collaborative, and agentic applications using their existing stack. By James Arthur

Jordan Nanos discusses how semiconductor constraints, data center expansion, and networking bottlenecks impact AI software architecture. Drawing from SemiAnalysis research, he shares insights on benchmark performance, GPU scaling, and tokenomics from chip fab to model inference. By Jordan Nanos

Imagine opening a website from India while its servers are located in the United States. You request an image. Your request travels thousands of kilometers to the server, the server processes it, and the response travels all the way back to you. It works. But what happens when millions of users around the world do the same thing? This is where a CDN (Content Delivery Network) comes in. A CDN helps websites deliver content from servers that are geographically closer to users, reducing latency, improving performance, and taking load away from the main server. In this article, we'll understand how CDNs work, why they're important, and how they're used in large-scale systems. What Is a CDN? A Content Delivery Network is a globally distributed network of servers that stores and delivers frequently requested content closer to users. Without a CDN, requests might look like this: User ↓ Main Server ↓ Content With a CDN, a distributed layer is added between users and the origin server: ┌── CDN Edge Server ── User (India) │ Origin Server ────┼── CDN Edge Server ── User (Europe) │ └── CDN Edge Server ── User (USA) The main server is called the origin server . The distributed servers are commonly called edge servers or Points of Presence (PoPs) . Why Do We Need a CDN? Without a CDN, users from different parts of the world may have to communicate with the same origin server. For example: User in India ───────┐ User in Germany ─────┤ User in USA ─────────┼──→ Origin Server User in Japan ───────┘ As traffic grows, this creates several problems: Higher latency More traffic reaching the origin Increased server load Slower image and video delivery Poor performance for users far away from the server A CDN solves this by distributing frequently requested content geographically. How Does a CDN Work? Suppose your website contains an image: /images/product.jpg A user in India requests it. Instead of immediately contacting your origin server, the request goes through the CDN: User ↓ CDN ↓

In August 2026, Congo's Council of Ministers approved a collaboration convention worth about $1.26 billion to rehabilitate the Dilolo–Sakania line: roughly a thousand kilometres of track running from the Angolan border, across the Congolese copper belt, to the Zambian border. It is good news. Copper and cobalt from Katanga have spent decades travelling thousands of kilometres by road to ports in South Africa, Tanzania and Mozambique. Trucking is slow, expensive, and exposed to every border queue between the mine and the ship. A working railway to the Atlantic cuts that journey from something like forty-five days to under ten. I want to sit with a smaller detail. That line is the Congolese leg of the Lobito Corridor, and the corridor's spine is the Benguela Railway. The Benguela was chartered in 1902, when the Portuguese government granted a ninety-nine-year concession to Sir Robert Williams, a Scottish mining magnate and an associate of Cecil Rhodes. Construction started in 1903. The line reached the Belgian Congo border in 1929. So the flagship infrastructure project of Africa's 2026 critical-minerals moment is a rehabilitation of a route designed in 1902 to move Katanga copper to a European-facing port. The route was correct then, for the people who commissioned it. The question worth asking is whether it is still the route we would draw today, and what it means that we are mostly repairing rather than redrawing. The test Here is a test you can run on any colonial-era African railway, using nothing but a map. Find the two endpoints. One of them will be a mine, an oil field, or a plantation belt. The other will be a port. Draw the line between them and you will notice it runs more or less perpendicular to the coast — inland to seaward — and that it does not stop anywhere particularly useful along the way except to pick up more of the same cargo. Then look for what is missing. Look for lines running parallel to the coast, connecting one colony to its neighbour. Look

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

Over the last couple of weeks, hackers have targeted and broken into the systems of several water plants in the United States. Here’s what we know and don’t know about this wave of attacks allegedly carried out by the Iranian government.

Most Kubernetes advice assumes you have a platform team: specialists who own upgrades, ingress, security policies, and the 2 a.m. pages. The teams I am writing for usually have three to ten engineers, one of whom “knows Kubernetes,” and no dedicated platform team. They depend on a cluster that nobody fully owns. I work in enterprise environments where platform teams are large and everything is process. This article is the opposite exercise: what is the minimum discipline a small team needs to run Kubernetes in production—and what enterprise baggage should it refuse to copy? The question that matters more than any tool Before any checklist: who owns the platform after the migration is finished? Not “who set it up.” Who owns upgrades next year, certificate renewals, the CNI version, and deprecated APIs? If the answer is one person's name, you do not have a platform. You have key-person risk with YAML on top. If the answer is “nobody, really,” Kubernetes is invisible operational debt accumulating interest. The rest of this checklist exists to make that ownership small enough for a small team to carry. For each item, score 0 if it does not exist, 1 if it exists but is informal or untested, and 2 if it is documented and tested. The purpose is not to produce a flattering number. It is to expose the next few conversations the team needs to have. 1. Deployments: Git is the source of truth Treat Git as the source of truth for workloads and cluster configuration, including temporary fixes. Use one reconciliation path—for example, Argo CD or Flux—so production changes are reviewed and reproducible. Keep emergency access, but reconcile every emergency change back into Git. Define and test a rollback path for every service. A Git revert is useful only if your delivery process can deploy it safely. This converts your cluster from a mystery into a diff. Every other practice gets easier once “what is running?” has an answer. 2. The rollout basics that prevent late-night incidents R

About QQ studio QQ studio is a Czech production and postproduction company engaged in filmmaking and European television projects. For their visual effects (VFX) artists, editors, and sound designers, high data throughput and system reliability are critical. Data bottlenecks and hardware crashes risk missed client deadlines, broken delivery promises, and interrupted creative flow. The Breaking Point In mid-2024, QQ studio’s production pipeline hit a wall. Over 40 TB of active project data lived on an aging QNAP NAS. External access via FTP failed, forcing reliance on expensive third-party file transfer services like Frame.io. Meanwhile, project management relied on Kitsu, the studio’s central tool for tracking VFX shot status, coordinating task assignments, and reviewing preview renders across creators. For most modern companies, the automatic answer to a failing local server is simply: Move everything to the cloud. However, given QQ studio’s hybrid setup where editors, VFX artists, and render nodes work directly on local workstations, relying on remote storage isn’t practical. The studio works with massive, uncompressed video files where even a 2.5Gbps local network can sometimes feel slow. Pulling terabytes of raw footage down from the internet daily would cripple their creative workflow, and paying for 40+ TB of active, high-speed cloud storage would burn through project budgets entirely. They needed the massive speed of a local network, but with enterprise-grade reliability. Meanwhile, daily backups were manual and fragile, relying entirely on hope that systems wouldn't crash. With growing project scopes, a team scaling over 40 creators, and strict commitments to major partners, studio leadership knew their old infrastructure had reached its limits. They brought me in to work alongside their team and replace the fragile setup with a stable, secure foundation. Deploying the Industry Standard Rather than forcing a proprietary, unproven setup from scratch, I worked

The 10-Minute Tax For many years, as a CDK developer, I'd run cdk synth , then cdk deploy , and then cross my fingers — either it deployed cleanly, or it failed somewhere in the middle of a CloudFormation run that had already been going for ten minutes: ❌ MyStack failed: UPDATE_ROLLBACK_COMPLETE Resource handler returned message: "The runtime parameter of nodejs16.x is no longer supported" (HandlerErrorCode: InvalidRequest) Ten minutes. For something CDK could have told you before it ever talked to CloudFormation. These days I let AI agents write a good chunk of my CDK code, which made this even worse — an agent can't iterate when every failed attempt costs it ten minutes. 🤖 AI Agent development loop: Attempt 1: cdk deploy → ⏱️ 10 min → ❌ deprecated runtime Attempt 2: cdk deploy → ⏱️ 10 min → ❌ invalid memory size Attempt 3: cdk deploy → ⏱️ 10 min → ❌ security group rule conflict Attempt 4: cdk deploy → ⏱️ 10 min → ✅ finally works Total time wasted: 30 minutes on things that were knowable at synth time. And if you're deploying something heavy like an Amazon EKS cluster, the penalty stretches to 25-30 minutes per failed attempt. What if the CDK could catch all of those on cdk synth — in seconds? The CDK Lifecycle: Where Validation Fits Before I show off the new validation, it helps to see where it plugs into the lifecycle every cdk deploy goes through: Stage What Happens Executed By 1. Construction Execute main.ts , call new Stack() , build the construct tree in memory CDK App (local) 2. Synth app.synth() traverses the tree, produces CloudFormation template to cdk.out/ CDK App (local) 3. Template Validation 🆕 Post-synth offline validation — default rule set + registered policy plugins CDK App (aws-cdk-lib, local) 4. Create Change Set 🆕 CloudFormation pre-deployment validation — 6 types of online checks against real account state CloudFormation (AWS) 5. Execute Change Set CloudFormation provisions/updates/deletes actual AWS resources CloudFormation (AWS) The gap was a

The Other Half of Compute xAI stood up its first 100,000 GPUs in Memphis in 122 days. It doubled that in another 92. By early 2026 the site, Colossus, held around 555,000 of them, building toward two gigawatts of power, for a reported 18 billion dollars. 1 Two sophisticated people can look at that number and reach opposite conclusions. Jensen Huang’s view is that the only real risk is underspending. He puts the buildout at a trillion dollars and counting, and argues the company that holds back capacity loses the decade. 2 Dario Amodei and Ray Dalio sit on the other side. Amodei has said it can be rational not to buy unlimited compute, because the revenue to justify it may arrive on a timeline that bankrupts whoever guessed wrong. Dalio keeps making a narrower point: a technology can succeed completely and still ruin the people who financed it. 3 Same buildout. Same dollar figure. One camp calls it the obvious move of the decade and the other calls it the setup for a wipeout. They are not disagreeing about the facts. They are reading the same number and the number is the problem. What 18 billion dollars buys Every token a model produces runs down a physical path. Electricity has to be generated, moved across a grid, and stepped down through transformers to a voltage a data centre can use. Chips have to be fabricated at advanced nodes, which in practice means TSMC and a single supplier of the lithography machines that make the process possible. The chips have to be wired together with optical interconnect, assembled into racks, and kept cold. None of those layers move at the same speed, and the slowest one always sets the schedule. For four years the slowest layer kept changing. In 2022 the constraint was GPUs themselves. In 2023 it was the high-bandwidth memory stacked next to them. In 2024 it was the advanced packaging that bonds the two together. By 2025 it was photonics, the lasers and transceivers that move data between racks. By 2026 it had reached power and the