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Working late nights on server migrations and code architectures often means typing in low-light environments. While USB lamps or backlit keyboards are the standard solutions, they consume extra power and add physical clutter. I realized the ultimate light source was already directly in front of me: the monitor. With a clear vision in mind, I partnered with Google's Gemini AI to rapidly prototype and refine what became LightBar For Keyboard , a lightweight Windows application that creates a reflective light bar at the bottom of the screen to illuminate the keys. Here is how we built it using C# and WPF, tackled the Windows API to manage screen space, and optimized it for modern OLED energy consumption. The Core Challenge: Desktop Toolbars (AppBar) The simplest approach to creating a light bar is a borderless, top-most window. However, the immediate UX flaw is that maximized applications (like Chrome or Visual Studio) will either cover the bar or be partially obscured by it. To solve this, the application needed to behave like the Windows Taskbar. I implemented the native Windows Application Desktop Toolbar (AppBar) API using SHAppBarMessage from shell32.dll . Docked Mode: By registering the application as an AppBar and setting the edge to ABE_BOTTOM , Windows automatically recalculates the working area of the desktop. Result: Maximized windows are pushed upward, ensuring the light bar remains entirely visible and never covers any underlying application UI. Floating Mode: For users who need temporary access to the bottom of their screen, I added a state toggle that unregisters the AppBar and enables standard drag-and-drop window movement via MouseLeftButtonDown . Enforcing a Single Instance Because the app directly manipulates the desktop working area, launching multiple overlapping instances would cause UI glitches. To prevent this, I implemented a Mutex in App.xaml.cs to guarantee a single instance constraint. protected override void OnStartup ( StartupEventArgs e )

What Is My IP Address? IPv4 vs IPv6 Explained for Developers If you've ever debugged a CORS error, set up an IP allowlist, or wondered why req.ip returned something weird in your Express logs, you've run into the same question from a different angle: what actually is an IP address, and which one is "mine"? fastestchecker.com This post breaks down IPv4 vs IPv6, public vs private IPs, and how to reliably detect a user's IP address in your own code — plus a fast way to check yours right now. fastestchecker.com TL;DR IPv4 addresses look like 192.168.1.1 — four numbers, 0-255, separated by dots. There are about 4.3 billion of them, and we've run out. IPv6 addresses look like 2001:0db8:85a3::8a2e:0370:7334 — a much larger address space designed to replace IPv4. Your device usually has a private IP (local network) and shares a public IP (internet-facing) with everyone else on your router. You can check your current public IP instantly with a tool like FastestChecker's IP Checker — useful for confirming what your server or API actually sees. > IPv4 vs IPv6 : What's the Actual Difference IPv4 IPv4 has been the backbone of the internet since the 1980s. It's a 32-bit address, which caps the total number of unique addresses at roughly 4.3 billion. Given how many devices are online today, that pool has been effectively exhausted for years — which is why NAT (Network Address Translation) exists: it lets an entire household or office share one public IPv4 address. Example IPv4: 203.0.113.42 IPv6 IPv6 uses 128-bit addresses, which gives it an address space so large it's effectively unlimited for practical purposes (2^128 addresses). It was designed specifically to solve IPv4 exhaustion, and adoption has been climbing steadily — most major cloud providers and mobile carriers support it by default now. ** Example IPv6:** 2001:0db8:85a3:0000:0000:8a2e:0370:7334 Quick Comparison IPv4IPv6Address length32-bit128-bitFormatDotted decimal (192.168.1.1)Hexadecimal, colon-separatedTotal addre

How an unexpected regional constraint forced us to deeply understand Azure GPU VM families, naming conventions, and workload fit. Introduction As architects, we often assume that infrastructure decisions are straightforward: "The workload is already running successfully in Region A. Let's deploy the same Kubernetes workload in Region B." That's exactly what we thought. Our workload consisted of a Visual Element Detection (VED) service hosted on Kubernetes. The application uses a PyTorch model to analyze images and detect various visual elements in an image file. The service was already running successfully on a node pool backed by Azure's NVads_A10_v5 GPU VMs. Then we hit an unexpected challenge. The target region did not offer NVads_A10_v5 instances. What looked like a simple deployment exercise became a deep dive into Azure GPU virtual machine families, GPU architectures, VM naming conventions, and workload characteristics. This article shares what I learned in the hope that it helps others who find themselves evaluating Azure GPU SKUs for AI inference workloads. I am relatively new to the world of MLOps, Model deployments, GPU Workloads etc and equally interested and excited to learn more on this front. The Workload Before discussing VM selection, let's understand the workload characteristics: Model Type : PyTorch Model Size : less than 200 MB (.pth) Image Resolution : ~2000 x 2000 Expected Throughput : 5-7 requests/sec Platform : AKS (Kubernetes) Workload Type : Inference only This is important because GPU sizing should always start from the workload and not from the VM catalog. Step 1: Understanding Azure GPU VM Families Many engineers first encounter Azure GPU machines through names like: NV12s_v3 NV6ads_A10_v5 NC4as_T4_v3 ND96isr_H100_v5 The naming can be intimidating. The first breakthrough was understanding that Azure organizes GPU VMs into three primary families: N-Series ├── NV ├── NC └── ND NV Series – Visualization and Graphics NV-series VMs are designe

Every DevOps engineer has done this dance: you've got a chunk of YAML or a Terraform file that looks right, something's rejecting it, and you want a fast sanity check. So you paste it into some random online validator — and a small voice asks, wait, where did that config just go? That config often has structure, comments, sometimes internal hostnames or resource names in it. Pasting infrastructure definitions into an unknown server is a habit worth breaking. So I built a set of validators that never send your config anywhere — they run entirely in your browser. What they are Free, browser-based validators for the formats DevOps folks paste-and-pray most: YAML — catches the indentation and structure errors that make Kubernetes and CI configs fail with cryptic messages Kubernetes manifests — schema-aware checks beyond "is it valid YAML," so you catch the wrong apiVersion or a misplaced field before kubectl apply does Terraform / HCL — structural validation for the syntax slips that terraform validate flags only after you've context-switched away The one design decision that matters 100% client-side. No upload, no signup, no server round-trip. Your config is parsed by JavaScript running in your own tab — it never leaves your machine. You can literally open dev-tools, watch the network panel, and see nothing go out. Turn off your wifi and they still work. This isn't a privacy gimmick — it's the correct architecture for a tool that handles infrastructure definitions. A validator has no business seeing your config on a server it doesn't need to. Why I bother Two reasons, honestly. One: I kept wanting this exact thing and kept not trusting the options. The nth time I hesitated before pasting a manifest into a stranger's website, I decided to just build the version I'd trust. Two: fast feedback loops are the whole game in this job. The gap between "save the file" and "find out it's malformed" is pure friction — and the tighter that loop, the less of your working memory it b

The Iranian government exploited well-known flaws in cellphone networks to locate and then strike U.S. military personnel in the build-up and beginning of the war.

Meta recently open-sourced Brain2Qwerty v2, a noninvasive Brain–Computer Interface (BCI) that can decode sentences from thoughts using electroencephalography (EEG) or magnetoencephalography (MEG) signals from the brain. In evaluations, the system achieved a word accuracy rate 61% on average, compared to 8% for other non-invasive methods. By Anthony Alford

Native XDP and generic SKB-mode XDP are not the same thing in practice. The same BPF program can pass the verifier and still behave differently depending on which mode the kernel uses, this could be a different verdict, different frame bytes, or different metadata. This post ships three things: an open differential test harness, a fixed eleven-packet corpus, and a simple way to classify the differences it finds. A tagged release lets anyone reproduce the virtio/veth baseline on Linux 6.8. The operational risk is straightforward. A firewall or rate-limiter validated only under native XDP can fall back to generic mode on an unsupported driver, a veth port, or after a reload. You keep the same bytecode, but behaviour can change, often without a clear error line. What this release includes: A harness loop: corpus → inject on the RX path → native vs generic sweep → xdpdump capture → compare.py manifest, comparing both the captured frame bytes and the XDP verdict ( PASS / DROP / TX / REDIRECT ). A deterministic corpus with eleven embedded test IDs ( 0xA001 – 0xA005 , 0xA007 – 0xA00C ; 0xA006 is intentionally omitted as a reserved gap in the generator). An operational divergence taxonomy (Class A / B / C). A virtio/veth smoke gate on Linux 6.8; now gating on frame bytes and verdict agreement that shows the full path is reproducible end to end. Scope for this post: native vs generic XDP on the virtio_vm profile only (five BPF programs, pinned manifests). This is part 1 of 2; it establishes the harness and an instrument-validity baseline; a follow-up post covers bare-metal divergence results. Physical NIC results are not part of this baseline. Ordinary conformance checks stop at “did the program load?” Differential testing asks a sharper question: given identical input packets, do the backends produce the same observable outcome at the hook? Background: native vs generic XDP Both modes load the same BPF object. They diverge at the hook point and in how the packet is represen

With residential proxies all the rage, CISA urges router users to be vigilant.

I released php-quic 1.0.0, a PHP extension that gives you raw QUIC transport with first-class access to streams. It links against the same OpenSSL that PHP already links against, and that is the whole trick. Why This Was Awkward Before QUIC is not a protocol you bolt on in userland. It is TLS 1.3, congestion control, loss recovery, and stream multiplexing, all riding on UDP, and all of it has to be right before you can send a single useful byte. You could get there from PHP if you were willing to bind to a foreign QUIC library like ngtcp2 or quiche through FFI. That works, but now your PHP app carries a second TLS stack, a second set of CVEs to track, a build story that involves Rust, and a version matrix that has nothing to do with the one your distro maintains. For a language whose entire deployment story is "the package manager handles it," that is a lot of rope. What Changed OpenSSL 3.5 shipped a native QUIC stack. Client and server, in the library PHP is already built against. That reframes the problem. The extension is no longer "embed a QUIC implementation into PHP." It is "expose the QUIC that is already sitting there." No new TLS stack, no FFI layer, no Rust toolchain in your build. If your OpenSSL is patched, your QUIC is patched. The requirements fall out of that directly: PHP 8.4 or newer (8.5 on Windows), NTS or ZTS OpenSSL 3.5.0 or newer, built with QUIC support Transport, Not HTTP/3 The thing I most wanted to avoid was shipping an HTTP/3 client and calling it a QUIC library. QUIC is a transport. HTTP/3 is one protocol that runs on it, and it is not the interesting one for everybody. DNS-over-QUIC runs on it. So does anything you want to invent that needs multiplexed, ordered, loss-recovered streams without head-of-line blocking across them. So php-quic hands you connections and streams, and stays out of the framing business. If you want HTTP/3, you build HEADERS frames and QPACK on top of it, and the README has a worked example. If you want something

Chipset makers and router manufacturers are talking about Wi-Fi 8, but what is the new standard, and when will it arrive?

The creator of TV Time is building a successor app that will let users import their watch histories and preserve the community that formed around discussing their favorite shows.

If you have ever logged into a corporate computer, searched for a colleague in your company’s email directory, or used a single set of credentials to access dozens of different internal applications, you have likely interacted with LDAP . Standing for Lightweight Directory Access Protocol , LDAP is an open, vendor-neutral, industry-standard application protocol for accessing and maintaining distributed directory information services over an IP network. In simpler terms, it is the underlying language that allows different systems and applications to communicate with a central directory to find information about users, devices, and permissions. Think of LDAP as a highly organized, digital phonebook. When an application needs to know if "John Doe" is a valid user and what his password is, it uses LDAP to ask the phonebook. How LDAP Organizes Data Unlike traditional relational databases (like SQL) that store data in tables, LDAP stores data in a hierarchical, tree-like structure known as the Directory Information Tree (DIT) . This makes it incredibly fast at reading and searching for information, which is exactly what an authentication system needs to do millions of times a day. Here are the core components of this structure: Root: The top level of the directory tree, usually representing the organization (e.g., dc=example, dc=com ). Branches (Organizational Units - OU): Categories or departments within the organization (e.g., ou=Marketing , ou=Servers ). Leaves (Entries): The actual objects being stored, such as a specific user, printer, or computer. Attributes: The specific pieces of data tied to an entry. For a user entry, attributes might include givenName (first name), mail (email address), and userPassword . Every entry in an LDAP directory has a unique identifier called a Distinguished Name (DN) . It acts like an absolute file path. For example, John Doe’s DN might look like this: cn=John Doe, ou=Marketing, dc=example, dc=com How Applications Talk to LDAP When an

Python is an excellent language for automating cloud infrastructure, but the official Kubernetes Python client leaves developers with an important architectural decision: Where should Kubernetes manifests live? Should they be constructed directly with Python objects? Embedded as multiline strings? Or stored as external files and rendered at runtime? Each approach works, but they have very different implications for readability, maintainability, and long-term operational cost. The key is recognizing that deployment logic and platform configuration evolve on different lifecycles. Your deployment code, the part that authenticates to Kubernetes, renders templates, and applies resources, may remain unchanged for months. Your manifests, however, often change weekly as applications evolve, resource limits are tuned, cloud-provider annotations are added, or networking requirements change. When those two concerns are tightly coupled, even a configuration, only change forces you to modify, test, and redeploy the delivery or application code itself. Over time, this increases maintenance costs, slows platform changes, and makes configuration drift and production mistakes more likely. This is a familiar software engineering principle: separate concerns that evolve independently. The same thinking that keeps application configuration separate from executable code also applies to Kubernetes manifests. Treating manifests as first-class configuration artifacts allows them to evolve independently from the Python code that delivers them. In this article we'll compare three ways of deploying Kubernetes resources with the official kubernetes-python-client , ranging from tightly coupled implementations to a design that cleanly separates deployment logic from platform configuration. The Landscape at a Glance The comparison below assumes a common application deployment scenario, where the desired state is largely known ahead of time. Controllers and Operators have fundamentally different r

This is Part 1 of a 5-part series on Cisco EEM. We start here with the fundamentals and a few working applets, then build toward self-healing networks, automated diagnostics, compliance guardrails, and a complete real-world deployment. Ask ten network engineers what they use EEM for, and nine will say the same thing: "Oh, I have an applet that auto-recovers err-disabled ports." Then they never touch it again. That's a shame, because Embedded Event Manager is one of the most capable tools already sitting inside every Cisco IOS device you own — and almost nobody uses more than 1% of it. It's a full automation engine that lives on the box . No external server. No API gateway. No orchestration platform. Just the router or switch, watching itself, ready to react the instant something happens — even if the WAN is cut, the NMS is down, and it's three in the morning. This series is about using that other 99%. By the end you'll have a toolkit of applets you can deploy and, more importantly, a way of thinking about on-box automation. But we start at the foundation: what EEM actually is, how it's put together, and why "on the box" is a bigger deal than it sounds. What EEM actually is Embedded Event Manager is an event-driven automation framework built into Cisco IOS, IOS-XE, and NX-OS. Strip away the jargon and it's a very simple idea: When something happens, do something about it — automatically, on the device itself. That "something happens" is an event . That "do something" is one or more actions . Bundle an event with its actions and you have an applet — the basic unit of EEM. That's the whole model. The power comes from how many different things can be an event , and how much an action can do. Events EEM can watch for include: A syslog message matching a pattern (an interface flapping, an HSRP state change, a config being saved). An SNMP OID crossing a threshold (CPU over 85%, a power supply going absent, temperature rising). A CLI command being entered (someone typing wr

It's unclear how the planet avoided its star's bloated red giant stage.

I built CreditMeter, a small Windows tray app that shows GitHub Copilot AI-credit usage like a taxi meter. Why I built it Agentic coding makes AI usage feel invisible until you look at the bill. Constraints no WinForms no WPF no backend no telemetry no dependency-heavy architecture Tech stack C# / .NET 9 Native AOT raw Win32 / PInvoke GitHub REST API DPAPI for local PAT storage What I learned For tiny tools, architecture is also about knowing what not to add. Repo https://github.com/cdilorenzo/CreditMeter

Modern applications, especially AI agents and distributed systems, need more than logs to understand what is happening. That's why I explored SigNoz, an open-source observability platform built on OpenTelemetry. Setting up SigNoz with Docker was simple. After connecting a sample application, I could view logs, metrics, and traces from a single dashboard within minutes. My favorite feature is distributed tracing. Instead of guessing where requests slow down or fail, SigNoz clearly shows the complete request journey across services, making debugging much easier. The built-in dashboards provide valuable insights into CPU usage, memory, request latency, throughput, and error rates. Having centralized logs alongside metrics and traces saves time by eliminating the need to switch between multiple tools. I also liked the alerting feature, which helps detect issues before they affect users. For AI applications, observability is essential. AI agents make multiple API calls, use tools, and perform complex workflows. SigNoz makes it easier to understand each step, identify failures, measure latency, and optimize performance. Overall, my experience with SigNoz was excellent. It combines logs, metrics, traces, dashboards, and alerts into one intuitive platform. Among all its features, distributed tracing impressed me the most because it provides deep visibility into application behavior and simplifies troubleshooting. I'm excited to use SigNoz in future AI and cloud-native projects.

The status of the controller As of March 2026, the Kubernetes SIG Network stopped maintaining ingress-nginx. That is the controller a lot of clusters have been running for years. A CNCF blog post published July 9 walks operators through the state of play. The headline for anyone still on it is short: unpatched CVEs, and no more feature work. The post names two operational risks explicitly. New security issues will not receive upstream fixes. Feature updates and community support have stopped. If your ingress plane is a piece of infrastructure you have not touched in a while, this is the reason to pull it up in this quarter's planning doc. What it means at 3am An ingress controller sits between the internet and your services. When it drops a request, you find out from your users. When it takes a CVE and no one is patching, you find out from a scanner or from a report. Neither is a good discovery path. The controller also carries the exact set of annotations, TLS defaults and rewrite rules your workloads rely on. Nothing about a retirement changes the version you have in production today, so the immediate blast radius is zero. The risk is on the calendar, not on the pager. That is the kind of risk teams reliably defer until a scanner flags an unpatched CVE. The two paths CNCF lays out The post frames the choice as a fork. Path A is a lateral swap to another Ingress controller. The example named is Contour, described in the post as Envoy-based. This keeps you on the Ingress API and mostly moves the problem of who is patching. Path B is modernization to the Gateway API, described in the post as the upstream-backed successor to Ingress. The CNCF post points at ingress2gateway to automate the translation, and recommends an incremental rollout: run the new plane in parallel and move non-critical workloads first. The stopgap version is a mix. Adopt Contour to buy time on maintained code, then schedule the Gateway API move on your own calendar rather than under duress. What

Quando voce cria um container e depois outro, eles podem nao se enxergar. O motivo quase sempre e a rede. Entender os tipos de rede que o Docker oferece resolve isso de uma vez. Comece listando as redes que ja existem no seu Docker. docker network ls A rede bridge e a rede host vem de fabrica. Nenhuma delas oferece resolucao de nomes entre containers. Para isso voce precisa criar a sua propria rede. docker network create minha-rede Agora os containers que entrarem nessa rede conseguem se comunicar pelo nome do container. docker run -d --name app1 --network minha-rede nginx docker run -d --name app2 --network minha-rede alpine sleep 3600 Teste a comunicacao. docker exec app2 ping app1 -c 2 O ping funciona. O DNS interno do Docker resolve app1 para o IP interno do container. Isso resolve 90% dos problemas de comunicacao. A rede bridge padrao nao tem esse recurso. Crie sua propria rede sempre que precisar que containers se enxerguem. Agora a rede host. Ela elimina o isolamento de rede. O container usa a placa do host diretamente, sem NAT e sem mapeamento de porta. docker run -d --name web-host --network host nginx O nginx fica acessivel em http://localhost:80 direto. Sem -p 80:80. Mas so um container pode usar cada porta, porque a porta e a do host de verdade. Para ambientes com mais de uma maquina, existe a rede overlay. Ela precisa do Docker Swarm ou de um cluster. docker swarm init docker network create -d overlay rede-overlay docker service create --name api --network rede-overlay --replicas 3 nginx Os containers em maquinas diferentes se enxergam pelo nome como se estivessem na mesma maquina. No dia a dia, a rede bridge personalizada resolve quase todo caso de uso. Host e para performance ou casos especificos. Overlay e para quando voce escala para mais de uma maquina. That's all for now. Thanks for reading!