标签：#infrastructure

Hello everyone! In this article, we will take a technical look at Tencent EdgeOne Makers , a Web and Agent development and deployment platform built on Tencent EdgeOne infrastructure. Rather than following a step-by-step tutorial, we will explore how the platform works behind the scenes and how its different technologies work together to support modern web applications. We will look at the journey from source code and build processes to deployment on the EdgeOne network. We will also explore how edge computing and caching deliver content closer to users, how Edge Functions and Cloud Functions handle server-side workloads, and how EdgeOne Makers supports AI Agents, storage, deployment environments, and developer tools . By understanding how these components interact, we can get a clearer picture of what Tencent EdgeOne Makers offers and how its architecture differs from traditional web hosting. Modern web applications are built from more than a frontend. They may combine APIs, serverless computing, storage, deployment automation, and AI services. Tencent EdgeOne Makers is a Web and Agent development and deployment platform built on Tencent EdgeOne infrastructure. It brings these capabilities into one environment, connecting application development with build processes, deployment, edge delivery, serverless execution, and AI development. EdgeOne Makers evolved from EdgeOne Pages, expanding from frontend hosting to full-stack Web and Agent development. Its architecture connects source code, deployment, and EdgeOne's global edge network. 1. From Source Code to Deployment At the center of Makers is its build and deployment workflow. A project can be connected to a Git repository, allowing Makers to detect common frameworks and apply build configurations. Developers can define parameters such as the root directory, installation command, build command, and output directory. Source Code → Git Repository → Build System → Deployment → EdgeOne Infrastructure → Users A push to

Originally published on WordPress on September 27, 2025. When AI systems fail in production, the failure is often blamed on data quality, model drift, or algorithmic limitations. In practice, many of the most damaging failures originate much earlier and much lower in the stack. They come from weak security foundations that allow systems to behave in unintended ways. Reliable AI is not just about accuracy or performance. It is about whether the surrounding infrastructure enforces discipline around access, data handling, and execution paths. Infrastructure as the First Line of AI Security Every AI system depends on infrastructure that controls how compute, storage, and networking are consumed. If that infrastructure is loosely governed, the AI system inherits that weakness. A common example is a shared compute environment where multiple teams run experiments. If isolation is poorly enforced, one workload can access artifacts, logs, or intermediate data from another. The model may be mathematically sound, but the environment allows behavior that violates assumptions about separation and control. From a reliability standpoint, this creates hidden coupling. An AI job might fail or behave inconsistently because another process consumed shared resources or modified shared state. From a security standpoint, the same weakness allows unauthorized access to sensitive datasets or trained models. Strong infrastructure boundaries do not just protect against attackers. They protect teams from each other and from accidental misuse. Access Control Across the AI Lifecycle AI systems have long lifecycles that include data ingestion, preprocessing, training, evaluation, deployment, and monitoring. Each stage introduces different access needs. Problems arise when a single identity or role is allowed to operate across too many of these stages. For example, an engineer might have permission to both modify training data and deploy models. That convenience can quietly undermine trust in the

Even though networking fundamentals are often taught at the early stages of a tech career, their relevance becomes far more important when you begin working with distributed IoT and edge-driven architectures. Concepts like subnetting, routing, NAT, DNS, firewalls, and VPNs evolve from simple textbook ideas into core architectural tools that determine how devices communicate, how secure the system remains, and how reliably data moves between the edge and the cloud. This refresher looks at these fundamentals from the perspective of someone building and supporting real IoT and edge environments. The goal is not to re-teach the basics, but to reconnect them with the realities of large-scale, low-power, and cloud-connected systems. 1. Subnetting as the Backbone of IoT Network Segmentation Subnetting plays a much bigger role in IoT and edge-driven environments than most people realize. In traditional networking, subnets help organize traffic and reduce broadcast noise. In IoT, they become a core part of the system architecture. When you’re dealing with sensors, gateways, and edge compute nodes running side by side, the network must be segmented in a way that keeps each function secure and predictable. A typical LoRaWAN setup shows this clearly. The gateway LAN, the packet-forwarder network, and the edge analytics node usually sit in different subnets. This separation allows you to apply strict ACLs around what each component can communicate with, especially because IoT devices often have limited security controls of their own. Subnetting also helps manage traffic flow, ensuring that noisy sensor broadcasts don’t interfere with time-sensitive edge workloads. Beyond security, good subnet design improves fault isolation. If a node misbehaves, the impact is contained within its segment. This structure also supports multi-tenant IoT deployments, where different applications or departments share the same physical infrastructure without touching each other’s data paths. In short

CERN engineers announced a shift from Red Hat-based distributions to Debian for its accelerator control systems. This decision stems from Red Hat's tightening compiler mandates, which threatened legacy hardware. The transition, focused on 2,200 specialized control machines, is set for completion in late 2026, while CERN's other systems will remain with Red Hat and AlmaLinux. By Olimpiu Pop

I rent six dedicated servers from a company in Germany. Together they have more cores, more memory, and more SSD than most production clusters I worked on a decade ago. I run my own Kubernetes on them. Not managed. Not EKS. Not GKE. The whole stack, from the immutable OS up to the workloads. People who hear this ask me why, and the question usually arrives in one of two tones. The dangerous tone is "that's amazing, how do I do it" . The responsible tone is "why on earth would you do that to yourself" . This post is for the second group. Why the cloud is the right answer for almost everyone Let me get this out of the way honestly: by every conventional metric, I should be using the cloud. Managed Kubernetes has become genuinely good. EKS has dramatically improved over the last three years. GKE has always been better than people gave it credit for. The serverless options are mature. The serverless databases are mature. The observability is mature. The bill is predictable in the way a Tuesday is predictable. Self-hosting violates almost every assumption that makes a startup productive. Time is the most expensive resource you have. The cloud sells you abstractions that turn that time into product. Running your own substrate means the time goes into the substrate. If you are trying to ship a product to customers — go use the cloud. Stop reading this post. It will only confuse you. What the cloud does not sell you Here is what the cloud will not sell you, even if you are willing to pay extra: control over your own roadmap. The cloud's roadmap is the cloud's. They decide which APIs deprecate. They decide which regions get the new feature. They decide what your egress bill looks like. They decide whether your monitoring vendor — sitting on top of their infrastructure — is allowed to charge you eight times what it would cost you to host the same software yourself. They decide whether the small ML company hosting your fine-tuned model gets acquired by someone with very differ

TrueNAS CORE has been the standard for open-source storage using FreeBSD and OpenZFS. The shift to TrueNAS SCALE, based on Debian, left some users needing alternatives. FreeCORE upgrades TrueNAS CORE to FreeBSD 15.0, restoring essential features like FreeBSD Jails. While it satisfies certain administrators' needs, its long-term sustainability and maintenance by a single individual raise concerns. By Olimpiu Pop

Ev Ağı Nasıl Çalışır? Bir ev ağını en basit haliyle şöyle düşünebiliriz: Internet │ ISP ağı │ Ev Router'ı │ ┌─────────────┼─────────────┐ │ │ │ Laptop Telefon NAS Ev router'ı burada iki farklı dünyayı birbirine bağlar: ISP üzerinden ulaştığı dış ağ ve evdeki cihazların bulunduğu yerel ağ. Bu basit topolojinin arkasında LAN, WAN, subnet, DHCP, routing, NAT, firewall, bridge ve VLAN gibi kavramlar birlikte çalışır. 1. Ev ağı, ISP ve internet tarafı LAN ve WAN LAN — Local Area Network , router'ın yerel ağ tarafıdır. Evdeki laptop, telefon, NAS, televizyon gibi cihazlar genellikle bu tarafta bulunur. Örneğin router'ın LAN adresi: 192.168.1.1/24 olsun. Cihazlar da: Laptop 192.168.1.20 Telefon 192.168.1.30 NAS 192.168.1.50 adreslerini kullanabilir. Bunların tamamı aynı: 192.168.1.0/24 yerel IP ağına aittir. WAN — Wide Area Network ise router'ın kendi yerel ağı dışındaki bir upstream ağa bağlandığı taraftır. Tipik bir evde: Internet / ISP │ WAN │ Router │ LAN │ Ev cihazları şeklinde görünür. LAN ve WAN, Ethernet kablosunun fiziksel türünü tanımlamaz. Aynı standart Ethernet bağlantısı bir router için LAN, başka bir router için WAN rolünde olabilir. Örneğin: Internet │ Upstream Router LAN: 192.168.1.1 │ │ Ethernet ▼ Downstream Router WAN: 192.168.1.50 LAN: 192.168.10.1 Buradaki 192.168.1.0/24 ağı: upstream router açısından LAN, downstream router açısından WAN tarafıdır. Dolayısıyla LAN ve WAN kavramları hangi router açısından baktığımıza göre anlam kazanır . Upstream ve downstream Ağda internet veya daha üstteki ağa doğru olan yön upstream , son kullanıcı cihazlarına doğru olan yön ise downstream olarak adlandırılır. Internet │ Upstream Router │ Downstream Router │ Laptop Downstream router'ın internete doğru bağlandığı router onun upstream router'ıdır. Bu terminoloji özellikle evde bir modem/router arkasına ikinci bir router bağlandığında kullanışlı hale gelir. ISP'nin rolü ISP — Internet Service Provider , ev ağını daha büyük internet altyapısına bağlayan servis sağlayıcıdı

Upgrading Juniper MX Networks from 100GbE to 400GbE: What Engineers Need to Know Moving a production network from 100 Gigabit Ethernet to 400 Gigabit Ethernet sounds simple on paper: Replace a 100G interface with a 400G interface and get four times the bandwidth. In a real carrier or data-center network, however, the interface is only one part of the equation. The router's forwarding silicon, switch fabric, midplane, power system, cooling, optics, software release, slot selection, redundancy configuration, and licensing can all determine whether the expected capacity is actually available. Juniper's MX240, MX480, and MX960 platforms provide an interesting example because these systems can be upgraded with newer generations of Modular Port Concentrators rather than requiring an immediate chassis replacement. One particularly useful case study is the Juniper MPC10E-15C , a Trio 5-based line card capable of supporting both 100GbE and 400GbE interfaces. This article isn't about whether you should buy a particular line card. Instead, we'll use the MPC10E-15C to examine the engineering questions that should be answered before attempting a 100G-to-400G upgrade on an existing Juniper MX network. Video Overview The video provides a short overview of the hardware. Below, we'll go deeper into the architecture and the deployment considerations that matter when integrating this class of line card into an existing MX environment. Why Moving from 100G to 400G Isn't Just a Port Upgrade Suppose an edge router has four heavily utilized 100GbE connections. At first glance, replacing those links with 400GbE interfaces appears straightforward. But consider what happens behind the physical port. Traffic entering that 400G interface must travel through several parts of the system: Interface → Packet Forwarding Engine → Fabric → Other line cards/interfaces Every component in that path needs sufficient capacity. A 400GbE optic connected to a router that cannot move 400 Gbps through its inte

To add the AWS AppConfig Agent Lambda extension to an AWS Lambda function, you can open the documentation, scroll through a table of ARNs, find the one that matches your AWS Region and architecture, copy it, and paste it into your template. However, a few months later, AWS publishes a new version and now your deployment is silently using an older one. There’s a better approach that uses public parameters in AWS Systems Manager Parameter Store (Parameter Store). What are public parameters in Parameter Store? Many AWS services use Parameter Store to publish read-only public parameters with names that start with aws/service/{service-name} . These public parameters contain up-to-date metadata about AWS services. You've probably seen them used for AMI lookups for fetching the latest Amazon Linux AMI ID without hardcoding it. The same mechanism is available for Lambda layer ARNs, ECS-optimized AMIs, and other resources that AWS updates regularly. The key idea is instead of looking up a value in documentation and pasting it into your code, you query Parameter Store at deploy time and get the current value. The only IAM permission that's required is ssm:GetParameter . The parameters are public and readable from any AWS account. The problem with hardcoded layer ARNs The AWS AppConfig Agent Lambda extension is distributed as a Lambda layer. To attach it, you need the layer's ARN, which includes a version number at the end: arn:aws:lambda:us-east-1:027255383542:layer:AWS-AppConfig-Extension:128 That version number changes every time AWS releases an update. If you hardcode it, you get a working deployment, but you also get silent drift. A few months from now, you'll be running an older version without realizing it. Imagine a team that's managing dozens of functions in multiple AWS Regions, and you can see how this can become a maintenance problem. Someone has to regularly check the documentation, update the ARN, and redeploy. It's not difficult work, but it's the kind of thing

It sure seems like it. The stores confirmed to be affected include Fort Irwin , Calif.; F.E. Warren Air Force Base , Wyo.; Fort Huachuca , Ariz.; Naval Station Newport , R.I.; Columbus Air Force Base , Miss.; and Travis Air Force Base , Calif., according to announcements made online by each installation. Naval Air Station Lemoore, Calif., also experienced an outage, according to M. Elizabeth, writer of the Substack newsletter Signal and Silence . Each service declined to answer questions about how many bases are affected by the outages, referring all questions to the Defense Department. Pentagon officials did not respond to questions...

Nginx Load Balancing with DNS-Based Service Discovery on Incus Hari ini saya buat satu practical lab untuk memahami Nginx Load Balancing , DNS-based Service Discovery , dan operational logging dalam persekitaran self-hosted menggunakan Incus. Lab ini bermula dengan architecture yang simple: Client │ ▼ Nginx LB │ ├──► web01 └──► web02 Kemudian saya tambah satu DNS server supaya backend tidak perlu bergantung sepenuhnya kepada hard-coded IP address. 1. Architecture Final architecture: DNS dns / dnsmasq 10.107.109.18 ▲ │ DNS lookup: web.incus │ │ Nginx LB 10.107.109.69 │ Load Balancing ┌──────────┼──────────┐ ▼ ▼ ▼ web01 web02 web03 .100 .253 .xxx Ada dua jenis communication flow dalam architecture ini. DNS resolution Nginx LB ──────► DNS │ └── web.incus ↓ .100, .253, .xxx DNS hanya digunakan untuk mengetahui IP address backend. HTTP traffic Client │ ▼ Nginx LB │ ├────► web01 ├────► web02 └────► web03 DNS tidak membawa HTTP traffic . DNS hanya menjawab: Where is web.incus ? Nginx kemudian menggunakan IP yang diperoleh daripada DNS untuk melakukan load balancing. 2. Static / Hard-Coded Upstream Cara paling mudah untuk configure Nginx Load Balancer ialah dengan meletakkan IP backend secara terus. Contoh: upstream backend { server 10.107 .109.100 ; server 10.107 .109.253 ; } Architecture: Nginx LB │ ├──► 10.107.109.100 │ └──► 10.107.109.253 Kelebihan Simple Mudah difahami Predictable Sesuai untuk environment kecil Tidak memerlukan DNS service discovery Kekurangan Kalau tambah web03 : web01 web02 web03 Nginx configuration perlu diubah: upstream backend { server 10.107 .109.100 ; server 10.107 .109.253 ; server 10.107 .109.xxx ; } Kemudian configuration perlu divalidasi dan biasanya Nginx perlu di-reload. 3. DNS-Based Service Discovery Pendekatan kedua ialah menggunakan hostname sebagai service identity. Contohnya: web.incus DNS: web.incus ├── 10.107.109.100 ├── 10.107.109.253 └── 10.107.109.xxx Nginx tidak perlu mengetahui backend IP secara hard-coded. Contoh: resolver 10.

This article is Part of a series on running vLLM on AKS and walks through creating an AKS cluster with a GPU node pool, deploying vLLM onto it, and wiring up Prometheus and Grafana for visibility. Companion pieces: Choosing the right GPU | Why your autoscaler flaps | Source Setup Summary Cloud: Azure GPU node: Standard_NV36ads_A10_v5 (1× A10, 24 GB) Image / model: vllm/vllm-openai:latest serving Qwen/Qwen2.5-7B-Instruct-AWQ Observability: kube-prometheus-stack (Prometheus + Grafana), KEDA, NVIDIA DCGM exporter All commands below are bash. The steps are ordered and each one depends on the previous. Dependency chain The build order follows one chain: model → VRAM requirement → GPU SKU → region availability → quota. Step 0 — Prerequisites (one-time, survives resource group deletion) GPU quota. Request through Portal → Quotas → Compute → This article: Requested Standard NVADSA10v5 Family vCPUs = 108 in westus (108 = 3 nodes × 36 vCPUs, matching the autoscaler's max-count 3 set in step 3). Quota is granted per-subscription and survives resource group deletion, so this step happens once, not on every rebuild. A quota is Azure's per-subscription limit on how much of a resource (here, GPU vCPUs in a specific VM family) you're allowed to provision at once. New subscriptions start at 0 for GPU families since it's expensive and can be abused. You need it because without an approval, az aks nodepool add for a GPU will fail outright. The request goes through manual Azure approval, so it has to happen before you plan to build. * Prerequisites * Existing Azure Subscription: Local tooling: Helm 3+ kubectl Bash Bash Variables to set for use through the setup RG = <resource-group-name> CLUSTER = <cluster-name> LOCATION = <preferred-location> Step 1 — Create Resource group az group create -n $RG -l $LOCATION Step 2 — Create AKS cluster, on a CPU system pool az aks create -g $RG -n $CLUSTER \ --node-count 1 --node-vm-size Standard_D2s_v5 \ --generate-ssh-keys The GPU does not go on thi

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