标签：#net

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I maintain gumdrop , an async, non-blocking Java server framework. Last year I wanted to add HTTP/3 support, and ran into a wall: the Java ecosystem essentially doesn't have QUIC. The JDK's own experimental support (JEP 517) is client-only. Netty gets HTTP/3 by shelling out to quiche + BoringSSL over JNI — which works, but you're back to native builds, platform-specific binaries, and a C library sitting underneath your "pure Java" framework. I used that approach first. It was clumsy enough that I went looking for a pure-Java alternative. There's exactly one: Kwik. But Kwik is blocking per connection — one thread per QUIC connection. That's a non-starter for a framework built around single-threaded selector loops handling tens of thousands of concurrent connections. So I wrote a QUIC implementation from scratch: packet protection, loss detection and NewReno congestion control, connection migration, 0-RTT, QPACK, an HTTP/3 client and server — all driven by the same non-blocking event loop as everything else in gumdrop. Collaboration note: TLS 1.3 comes from Agent15 — also from Kwik's author, Peter Doornbosch, but just the handshake layer, not the connection model. We're currently working together on making PQC — hybrid key exchange and signatures — the default there. Why the thread model matters The reason this mattered beyond HTTP/3: gumdrop isn't a web framework with QUIC bolted on, it's a general async I/O framework, and QUIC is just a transport. One thread per connection is exactly the model gumdrop exists to avoid — it caps concurrency at your thread pool, not your file descriptors, and it's the reason a "just use Kwik" fix was never really on the table. The same QUIC stack backs DNS-over-QUIC (DoQ) as a first-class DNS transport alongside DoT, DoH, UDP, and TCP — and the DNS resolver itself is fully async, with no blocking InetAddress.getByName() anywhere in the I/O path, which is its own small miracle in Java. HTTP, SMTP, IMAP, POP3, FTP, MQTT, SOCKS — it's the

Your backend framework will outlive your current team. Choose one that the next team can still navigate — here's why .NET has been that framework for Netflix, GitHub, and Stack Overflow for over two decades. Summary Twenty-three years. That's how long .NET has been running in production. Most frameworks from that era got abandoned, forked beyond recognition, or replaced entirely — .NET kept showing up. Netflix still uses it. GitHub uses it. Stack Overflow, which has probably saved more developer careers than any single resource on the internet, runs on ASP.NET. None of these teams are using it out of inertia. They're using it because it works under conditions that expose every weakness in a poorly designed system. This article gets into how .NET actually works, what it gives teams day-to-day, and whether it makes sense for what you're building now. Key Takeaways: One codebase, five platforms — Windows, macOS, Linux, Android, iOS. No rewrites, no platform-specific forks. Three languages, one project — C#, F#, and Visual Basic coexist without forcing a rewrite. The performance tooling ships with it — JIT compiler, AOT compiler, CLR memory management, Garbage Collector. All out of the box. Why Is .NET Still Around? Honestly, this question is worth sitting with for a second — because in software, most things don't survive twenty years. They solve the problem of the moment, get widely adopted before anyone finds the sharp edges, and then get quietly replaced when something newer comes along and the migration pain seems worth it. .NET didn't go that way. Some of that is Microsoft backing — resources, long-term support commitments, a developer community that doesn't dissolve when priorities shift. But backing alone doesn't explain it. Plenty of well-resourced frameworks have died. What actually kept .NET alive is that the foundational architecture held up. The cross-platform capability wasn't duct-taped on in 2020 because everyone suddenly cared about Linux. It was in the

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

The social app founded by Bonobos co-founder Andy Dunn is expanding beyond events with new digital homes for groups to connect, organize, and make plans.

The blunt tool is still in a lot of GRUB files: GRUB_CMDLINE_LINUX_DEFAULT = "isolcpus=0,1" Then update-grub (or grub2-mkconfig ) and reboot. Userspace tasks stop landing on CPU0/1. That is all most people verify — they fire a few busy loops and top looks empty on those cores. IRQs do not care. isolcpus is a scheduler isolation hint. Hardware interrupts can still fire on the "isolated" CPUs. I watched seven tight loops leave 0/1 idle for processes while /proc/interrupts still ticked on those cores. If you wanted a CPU for DPDK, a user-space NIC, or a cycle-accurate loop, scheduler isolation is necessary and not sufficient . Lab notes (English original is short; this write-up is the missing IRQ half): https://sunshout.tistory.com/1620 How to see what you actually isolated After reboot: cat /proc/cmdline # isolcpus=0,1 must be there grep PREEMPT /boot/config- $( uname -r ) || true taskset -cp 1 # pick a known userspace pid; it should not be 0,1 watch -n1 'grep "^ *[0-9]" /proc/interrupts | head' If IRQs still increment on CPU0/1, isolation is incomplete. That is expected with classic isolcpus= . On newer kernels the story split: isolcpus=domain / cpusets / cgroup cpuset — userspace isolcpus=managed_irq or manual irqaffinity / /proc/irq/*/smp_affinity — interrupts nohz_full= — tick reduction, another knob, not a substitute isolcpus is also marked deprecated in some trees in favor of cpusets. The IRQ caveat did not go away when the docs changed the preferred interface. Moving IRQs by hand Find the noisy ones ( eth0 , NVMe, GPU): grep -E 'eth|nvme|enp' /proc/interrupts # smp_affinity is a hex CPU mask. CPU2 only → 4 echo 4 > /proc/irq/IRQNUM/smp_affinity Or set the default affinity so new IRQs skip 0/1: irqaffinity=2-7 in the same GRUB line (adjust to your CPU count). Some devices ignore this (managed IRQs, VFIO). Then you isolate at the driver: bind the NIC to vfio-pci and poll from a pinned thread. When this shows up next to SR-IOV Passing a VF into KVM does not pin ho

Short answer: for a Node.js app in Docker or Kubernetes, give startup, readiness, and liveness probes separate meanings, keep routine health traffic out of application logging, and measure state transitions instead of counting every successful check. For a property-management API rolling out a new pricing rule, this preserves useful metrics: whether an instance can calculate rent correctly and accept traffic, without turning each kubelet poll into noise. Which health signal should control each container decision? Start with the decision, not the endpoint name. Signal Question it answers Include Exclude Action Startup Has initialization completed? Configuration parsing, pricing-rule compilation, required local warm-up Long-term dependency health Allow the process more time before other probes apply Readiness Can this instance safely receive a new pricing request now? Ability to serve the active rule version and any required dependency state Optional analytics and background exports Remove the pod from Service endpoints Liveness Is the process stuck beyond local recovery? Event-loop progress or another narrow process invariant Database, cache, and third-party availability Restart the container This split is the main noise filter. A downstream dependency becoming unavailable can make a pod unready, but restarting the same healthy process usually doesn't repair that dependency. If the dependency is placed in liveness anyway, every pod can restart together. The health response has then amplified one problem into two: lost capacity plus a restart storm. The pricing rollout makes readiness more demanding than “the port is open.” Imagine rule version rent-2026-08 is enabled for one building cohort. A newly started instance has loaded configuration but hasn't compiled that version yet. It is alive. It isn't ready. Its startup check should hold back liveness and readiness until initialization finishes; afterward, readiness should stay false until the active rule can be evalua

Networking terms can feel like alphabet soup when you're starting out — Hub, Switch, Router, MAC address, IP address, Subnet Mask — thrown at you all at once, usually with zero real-world context. Here's how I finally made sense of it, using something a lot more familiar: cricket. The Cricket Analogy Imagine a cricket team with three players: a hub , a switch , and a router . All three are part of the same game, but each has a completely different job — one's a batsman, one's a bowler, one's a fielder. Networking devices work the same way: they're all part of one network, but each does something distinct. Hub — The One Who Shouts to Everyone A hub is the simplest of the three. If only two devices need to talk, you don't even need one — but the moment more than two devices are connected, a hub becomes necessary to relay traffic between them. Here's the catch: a hub has no idea who's talking to whom. If Device A wants to send data to Device B, it sends that data to the hub — and since the hub doesn't know which device Device A actually wants to reach, it just broadcasts the data to every single connected device. So a hub's "functionality" is really a lack of intelligence — it doesn't figure out who wants to speak with whom; it just floods the message everywhere and lets the devices sort it out. Switch — The One Who Knows Everyone by Name A switch does the same basic job as a hub — moving data between connected devices — but with one major upgrade: it actually knows who's who. Instead of blindly broadcasting to every device, a switch keeps a table of each connected device's MAC address , so it can send data directly to the right recipient. What Is a MAC Address? Every device that connects to a network — a laptop, phone, router, anything — has a Network Interface Card (NIC) . That NIC comes with a MAC address : a permanent ID burned in by the manufacturer. If your laptop has an Ethernet port, the NIC lives right behind it. If you're connecting over Wi-Fi instead, the NI

Netflix executives have considered making third-party streaming services available within its app, according to a report from The New York Times. The recent discussions reportedly centered around bringing Peacock and Fox One to Netflix, though it's unclear whether the streaming giant would sell subscriptions to the other services or add their content to its app. […]

Most EF Core bugs I've seen in production aren't from bad code. They're from code that looks right. It compiles, it passes review, it works fine locally against a database with twelve rows in it. Then it hits a table with five thousand rows, or a second replica, or a request that gets cancelled halfway through, and it falls over in a way nobody wrote a test for. None of the mistakes below are exotic. They're the default behavior of EF Core when you don't opt out of it, or the default behavior of a deployment when nobody thought about what "five pods start at the same time" actually means. Here's the setup I use and the list of ways it goes wrong if you skip a step. The entity namespace Sample.Domain.Posts ; public sealed class Post { public Guid Id { get ; private set ; } = Guid . CreateVersion7 (); // sequential → index-friendly public required string Title { get ; set ; } public required string Slug { get ; init ; } public string Body { get ; set ; } = string . Empty ; public DateTimeOffset ? PublishedAt { get ; private set ; } public Guid AuthorId { get ; init ; } public uint RowVersion { get ; set ; } // optimistic concurrency token public void Publish ( TimeProvider clock ) { if ( PublishedAt is not null ) throw new DomainException ( "Post is already published." ); PublishedAt = clock . GetUtcNow (); } } Two things here that are easy to skip and annoying to retrofit later. Timestamps are stored as UTC ( DateTimeOffset ), rendered in the user's timezone only at the edge — I do the same thing on ProcessHub, storing everything UTC and rendering in Asia/Tehran, because "what timezone is this in" is a much worse question to answer after the data already exists in three different formats. Second: the clock comes in as TimeProvider , not a call to DateTime.UtcNow buried inside the method. It's a small thing, but it's the difference between a test that can assert "publishing sets the timestamp to exactly this value" and a test that has to accept "sometime around now."

By combining ProgressivePerGroup with Placement decision groups, you can roll out add-on configuration changes in the order dev → stg → prod. In this article, I use cluster-proxy as an example to explain the required configuration and how the rollout actually works. Overview flowchart TB Upgrade["helm upgrade<br/>change tag to vX.Y.Z"] subgraph Hub["Hub cluster"] direction TB Manager["cluster-proxy-addon-manager<br/>update Deployment"] Config["ManagedProxyConfiguration<br/>update spec"] ProxyServer["proxy-server<br/>update Deployment"] Hash["proxyAgent config<br/>update spec hash"] Rollout["OCM add-on manager<br/>ProgressivePerGroup"] Groups["progress through decision groups<br/>dev → stg → prod<br/>success + minSuccessTime before next group"] AddOn["ManagedClusterAddOn in current group<br/>Configured=True"] Render["cluster-proxy manager<br/>render agent chart"] Work["update ManifestWork"] end subgraph Spoke["spoke clusters in the current group"] direction TB WorkAgent["work-agent"] ProxyAgent["proxy-agent<br/>update Deployment"] end Upgrade -->|Helm updates directly| Manager Upgrade -->|Helm updates directly| Config Config -->|proxyServer.image<br/>not part of rollout| ProxyServer Config -->|proxyAgent.image<br/>part of rollout| Hash Hash --> Rollout Rollout --> Groups Groups -->|current group only| AddOn AddOn --> Render Render --> Work Work --> WorkAgent WorkAgent --> ProxyAgent WorkAgent -.->|Applied / Available| Work Work -.->|hash matches + Ready| Rollout classDef immediate fill:#fff3cd,stroke:#a66b00,color:#332200; classDef staged fill:#e8f3ff,stroke:#2563a6,color:#102a43; classDef spoke fill:#eaf7ed,stroke:#2f855a,color:#173d2a; class Manager,Config,ProxyServer immediate; class Hash,Rollout,Groups,AddOn,Render,Work staged; class WorkAgent,ProxyAgent spoke; Yellow indicates updates that happen immediately on the Hub. Blue indicates updates controlled by ProgressivePerGroup , and green indicates processing on the spoke clusters. Dashed lines represent status r

We have a web server container running the nginx image. The access and error logs generated by the web server are not critical enough to be placed on a persistent volume. However, Nautilus developers need access to the last 24 hours of logs so that they can trace issues and bugs. Therefore, we need to ship the access and error logs for the web server to a log-aggregation service. Following the separation of concerns principle, we implement the Sidecar pattern by deploying a second container that ships the error and access logs from nginx. Nginx does one thing, and it does it well - serving web pages. The second container also specializes in its task - shipping logs. Since containers are running on the same Pod, we can use a shared emptyDir volume to read and write logs. Create a pod named webserver . Create an emptyDir volume named shared-logs . Create a regular container in the webserver pod from the nginx:latest image named nginx-container , and an init container from the ubuntu:latest image named sidecar-container . Add the following command to the sidecar-container "sh","-c","while true; do cat /var/log/nginx/access.log /var/log/nginx/error.log; sleep 30; done" Mount the shared-logs volume in both containers at /var/log/nginx . Ensure all containers are in a running state. What is a Sidecar Container? Think of a sidecar like a motorcycle sidecar – it's attached to the main vehicle and extends its capabilities without changing the main vehicle itself. ┌─────────────────────────────────────────────────────────────────────────────┐ │ The Sidecar Analogy │ │ │ │ ┌────────────────────────────────────────────────────────────────────────┐ │ │ │ Motorcycle: The Main Vehicle │ │ │ │ - Does its primary job (serving web pages) │ │ │ │ - Doesn't worry about extra tasks │ │ │ └────────────────────────────────────────────────────────────────────────┘ │ │ │ │ │ ▼ │ │ ┌────────────────────────────────────────────────────────────────────────┐ │ │ │ Sidecar: Adds Extra Functional

Building a Scalable, HIPAA‑Compliant Healthcare Document Processing Pipeline in .NET & Azure Quick Answer A deep dive into architecting a production‑grade Healthcare Document Processing Pipeline—covering AI extraction, FHIR integration, vector search, and compliance at scale. In my experience, the biggest cost is not the AI model, but the orchestration that turns raw scans into audit‑ready FHIR resources. The right mix of services can reduce latency by 30‑50% while keeping the bill below 10% of the raw compute budget. Choose services that expose a BAA and native hybrid search (Azure Cognitive Search) to avoid a second compliance layer. Prioritize deterministic scaling (Container Apps + Aspire) over elastic serverless when real‑time SLAs are tight. Version your embeddings; treat the vector index as a first‑class contract. HIPAA‑Ready High‑Volume Document Ingestion When a health system starts ingesting thousands of paper‑to‑digital documents per day, the naïve “scan‑and‑store” approach quickly becomes a compliance and performance nightmare. The real challenge is to produce HIPAA‑ready, FHIR‑compliant, low‑latency data that can be consumed by downstream clinical decision support or billing systems. Compliance is not a checkbox; it’s a series of audit trails that must survive a 30‑day retention policy and survive a forensic review. In production, the cost of a single PHI exposure can exceed the annual budget of the entire platform. Real‑World Example Consider a mid‑size hospital that receives 25,000 inpatient discharge summaries, 8,000 lab reports, and 12,000 imaging PDFs every month. Each document is a mixture of scanned images, PDFs, and legacy forms. The billing team needs structured diagnoses and procedure codes within 30 seconds to avoid claim denials, while the analytics team wants similarity search for rare disease cases in the last 12 months. The pipeline must: Extract structured entities with ≥95% accuracy. Redact PHI in transit and at rest. Provide audit logs

.NET 10 NU1015 turns a PackageReference without a version into a restore error. I like the stricter default because an unbounded direct dependency can quietly resolve the lowest package version. The catch is that versionless XML is also the correct shape for NuGet Central Package Management (CPM). A mechanical “add Version everywhere” repair can undo the policy your repository intended to enforce. I use a simple split: first decide who owns the version, then make restore prove the answer. Why .NET 10 NU1015 stops the build Before .NET 10, NuGet reported NU1604 when a direct reference had no inclusive lower bound. Restore could continue and select the lowest version available from the configured sources. Starting with .NET 10, the same mistake produces NU1015 and restore fails. Microsoft documents this as a stable behavioral change in the .NET 10 compatibility guidance . Here is the ambiguous project entry: <ItemGroup> <PackageReference Include= "Demo.Greeting" /> </ItemGroup> If this is a normal direct reference, the project is missing its version. If CPM is active, the project is correct and the version should live elsewhere. The NU1015 diagnostic reference calls out a common failure mode: a project that expected CPM was copied into a location where CPM is disabled or its props file is no longer discovered. That distinction matters more than silencing the error. It tells me whether the project file or the repository-level package policy is broken. The timing can be misleading. An SDK upgrade may expose an old direct reference that had always relied on lowest-version resolution, while a repository move may break a previously valid CPM import. I inspect the failing project's evaluated inputs, nearby props files, and recent path changes before editing package metadata. That keeps a restore migration from turning into an accidental package-management migration. Fix the owner, not only the XML For a direct reference, I add an explicit version: <PackageReference Include=

Control Plane (Master) & Worker Nodes Control Plane components: API Server Scheduler Control Manager etcd Worker Node components: Container Runtime Kubelet Kube-proxy Node Processes Each node has multiple Pods on it. 3 processes must be installed on every node — used to schedule and manage those Pods. Nodes are cluster services that actually do the work. Container Runtime Examples: Docker, containerd, CRI-O. containerd is used in worker nodes — it's lightweight in nature. This should be installed on every node because application Pods need to run containers inside the node. Kubelet The process which schedules the Pods and containers underneath is Kubelet. Kubelet interacts with both the container and the node. Kubelet starts the Pod with the container inside. Communication between two nodes is because of Services. Creation of Pod: Kubelet insures the Pod is always running — if not, it will inform etcd. Kube-proxy Kube-proxy forwards the request from Pod to Service. Makes use of the communication, with load balancing. Provides networking (container ID, IP address). Load balancing — basically using IP tables. It makes sure to send the request to the same machine instead of sending it to others (from same node communications). So, how do you interact with this cluster? Schedule the Pod Monitor Re-schedule/restart the Pod Join a new node Managing processes are done by master nodes (the control plane). API Server When you, as a user, want to deploy a new application in a Kubernetes cluster, you interact with the API server using some client — could be UI or CLI. It's a cluster gateway — it gets the initial request of any update into the cluster, even the queries from the cluster. It also acts as gatekeeper for authentication. It means when you want to schedule new Pods, deploy new applications, create new services, or any other components — you have to talk to it first. Flow: Some request → API server → Validates request → Other processes → Pods Only one entry point to t

The .NET 10 JSON console logging change is small enough to miss during an upgrade: the formatted message still exists, but a typical record no longer duplicates it at State.Message . A collector, script, or snapshot test that reads only that nested property can start returning null while the application continues logging normally. I treat console JSON as a schema whenever another process parses it. That means a runtime upgrade deserves a contract test, not just a visual check in a terminal. The practical fix is to read the top-level Message , keep State for structured values, and retain a narrow fallback for older records. Why .NET 10 JSON console logging breaks nested-message parsers Before .NET 10, a normal AddJsonConsole record commonly repeated the rendered text: { "Message" : "Order 42 moved to ready." , "State" : { "Message" : "Order 42 moved to ready." , "OrderId" : 42 , "Status" : "ready" , "{OriginalFormat}" : "Order {OrderId} moved to {Status}." } } In .NET 10, the typical shape keeps one rendered message at the top level: { "Message" : "Order 42 moved to ready." , "State" : { "OrderId" : 42 , "Status" : "ready" , "{OriginalFormat}" : "Order {OrderId} moved to {Status}." } } Microsoft documents this as a behavioral breaking change and recommends that parsers use the top-level property. The official compatibility note also gives an essential caveat: State.Message may still appear when its content differs from the top-level value. I therefore do not reject a record merely because both properties exist. This is not a loss of structured logging data. OrderId , Status , and {OriginalFormat} remain useful fields inside State . The part that changed is where a consumer should get the rendered sentence. Prefer the top-level Message and keep State structured A legacy-only extractor is brittle because it assumes the duplicate is the contract: static string ? ReadLegacyOnly ( JsonElement root ) => root . TryGetProperty ( "State" , out var state ) && state . TryGetPro

At LinkedIn's scale, relying solely on human reviewers or simply putting an off-the-shelf AI reviewer in front of GitHub is not an effective way to manage PRs. To address this, LinkedIn engineers built a multi-agent AI code review platform that understands the organization’s coding context, treats code review as production infrastructure, and minimizes hallucinations and low-signal feedback. By Sergio De Simone

tailscale up --tun=userspace-networking gets you a green dot in the admin console and almost nothing else. The node appears in your tailnet, tailscale status looks healthy, and then you try to SSH into that container from your laptop and the connection hangs until TCP gives up. Two lines in the LXC config file fix it, and the container stays unprivileged. That's the whole post, really. But those two lines only make sense once you understand why every guide pushes you toward userspace mode in the first place, and what you're giving up by staying there. Who should care Anyone running services in unprivileged LXC containers on Proxmox who wants those containers to be real tailnet members with their own 100.64.0.0/10 address. Not reachable through something else. Reachable directly, over WireGuard, with a kernel network interface that ip addr can see. If you're already routing everything through a subnet router, you have a working setup and this is an optional upgrade. I covered that pattern in Tailscale Subnet Routers . Treat this as the next rung on the ladder: instead of one node advertising routes on behalf of everyone else, each container carries its own identity, its own ACL surface, and its own direct path to peers. What userspace networking actually costs you Every LXC-and-Tailscale guide I've read lands on the same instruction: pass --tun=userspace-networking and move on. It works because it sidesteps the problem entirely. Rather than asking the kernel for a TUN device, tailscaled runs a userspace TCP/IP stack (gVisor's netstack) inside its own process and never opens /dev/net/tun . Those costs stay invisible until you trip over one. Outbound traffic needs a proxy. In userspace mode, tailscaled exposes SOCKS5 and HTTP proxies on a local port. Nothing on the system routes to 100.64.0.0/10 automatically, because there is no interface and no route. Every client has to be told about the proxy: # userspace mode: this is the only way out export ALL_PROXY = socks5://l

Introduction: Kubernetes can feel overwhelming when you first hear terms like Pods, Services, and Deployments thrown around. In this first post of my Kubernetes series, I’ll break down the fundamentals — what Kubernetes actually solves, and the core building blocks you need to understand before going further. What is Kubernetes? Kubernetes is an open-source container orchestration tool , originally developed by Google. It helps manage containerized applications across different environments — physical machines, virtual machines, and cloud environments — which makes it a great fit for hybrid deployment setups. Why Kubernetes? The Problem It Solves To understand why Kubernetes exists, look at the trend that led to it: Applications moved from monolith to microservices. That shift drastically increased the number of containers teams had to manage. Managing hundreds of containers by hand became unsustainable — teams needed a proper way to orchestrate them. Key Features High Availability — no downtime Scalability — scale up or down based on load and performance needs Disaster Recovery — backup and restore built into the ecosystem Main Kubernetes Components Pods: Abstraction over containers Services: Stable networking & communication Ingress: Routes external traffic into the cluster ConfigMaps & Secrets: External configuration Volumes : Data persistence Deployments & StatefulSets: Replication (stateless vs. stateful) DaemonSets: One Pod per node, auto-scaled with the cluster

Good news for Blue Eye Samurai fans: Netflix has shared the first trailer and release timeline for the second season of the animated series, and confirmed the series' return for a third and final season. The second season's new teaser ends with the announcement that it'll be available to stream on Netflix in January 2027, […]