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This article is part of the Comprehensive Guide to Microservices Architecture in .NET Core, Cloud and Azure series. Asynchronous Messaging with Azure Service Bus Azure Service Bus provides enterprise-grade messaging infrastructure with advanced features for reliable message delivery, ordering guarantees, and complex routing scenarios. Service Bus vs Azure Queue Storage Azure Service Bus offers enterprise messaging capabilities including: Topics and subscriptions for pub/sub patterns Message sessions for ordered processing Transaction support across operations Dead-letter queues for failed messages Messages up to 100MB (premium tier) Advanced routing with filters and actions Azure Queue Storage provides: Simple FIFO queue operations Lower cost for basic scenarios Messages up to 64KB Best for simple point-to-point messaging When to Choose Service Bus Use Azure Service Bus when you need: Publish-subscribe patterns with multiple subscribers Guaranteed message ordering with sessions Transactional message processing Message size beyond 64KB Advanced routing and filtering Integration with hybrid or on-premises systems Implementation with .NET 9 .NET 9 introduces improved performance and simplified APIs for working with Azure Service Bus: // Producer using .NET 9 with improved performance public class OrderCreatedPublisher { private readonly ServiceBusSender _sender ; public OrderCreatedPublisher ( ServiceBusClient client ) { _sender = client . CreateSender ( "order-events" ); } public async Task PublishOrderCreatedAsync ( Order order , CancellationToken cancellationToken = default ) { var message = new ServiceBusMessage ( JsonSerializer . Serialize ( order )) { MessageId = order . OrderId . ToString (), Subject = "OrderCreated" , ContentType = "application/json" , // .NET 9: Better support for distributed tracing ApplicationProperties = { [ "CorrelationId" ] = Activity . Current ?. Id ?? Guid . NewGuid (). ToString (), [ "OrderDate" ] = order . CreatedAt . ToString ( "O" )

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Heat waves mess with your brain. Scientists are trying to figure out why. —Jessica Hamzelou It’s been hot in London this week. Really hot. A dangerous heat wave has hit Western…

The release of Grand Theft Auto VI is a singular moment, the kind of massive cultural phenomenon that makes people want to go out and buy a console to play it. It is the preeminent modern example of what's known as a "system seller." There's almost certainly a large audience of people who were waiting […]

Diagrid has announced the release of Dapr 1.18, introducing what it calls Verifiable Execution, a new set of capabilities designed to bring cryptographic trust, provenance, and tamper-evident execution records to distributed applications and AI agents. By Craig Risi

The Argo CD project released a v3.5 release candidate in June 2026. This version adds mutual TLS enforcement for internal components. It also includes Git commit signature verification for supply chain security and native ApplicationSet management in the UI. The release also graduates two significant features: impersonation and Source Hydrator, from alpha to beta. By Claudio Masolo

From October this year Samsung will roll out a variety of new paid tiers for access to its SmartThings API, including a $4.99 monthly plan for "non-commercial, individual developers." It won't just be developers that pay the price though. Some more advanced smart home users are likely to fall afoul of the rule change if […]

Apple is expected to shake up its usual Mac silicon release strategy, with Bloomberg's Mark Gurman reporting that there won't be Pro or Max versions of the upcoming M6 chip. Instead, Apple wants to "fast-track technologies that it originally planned to release later" with the M7 launch next year. The Cupertino company will reportedly only […]

We’ve arrived at the final day of Prime Day, which at this point should probably be called “Prime Week.” We’ve found discounts on all manner of gadgets, including TVs, smart home tech, chargers, headphones, and more. Some of the best deals have started selling out at some retailers, so if you’ve been craving a popular […]

After testing each of the MacBooks myself, here's my honest advice on which is right for you.

What began as a “flamingo revolution” to protest the $1.4 billion development on Sazan Island has spiraled into mass protests against a ruling party that thousands now want out.

From AirPods to on-ears, we’ve tested hundreds of pairs of headphones. Here are the best deals from Amazon’s biggest sale event.

OpenAI previews GPT-5.6 Sol, a next-generation model with stronger capabilities in coding, science, and cybersecurity, paired with its most advanced safety stack.

Why adaptability beats perfection in startup software development The Startup Trap: Building for a Future That Doesn't Exist Yet Many startup founders make the same mistake. They spend months building the "perfect" product architecture. The code is clean. The design patterns are flawless. The test coverage is near 100%. The infrastructure can scale to millions of users. There's just one problem: They don't have any users. In the startup world, survival depends on learning faster than competitors, not on creating the most elegant codebase. Product-market fit is uncertain. Customer needs change weekly. Business models evolve. Features that seemed critical last month become irrelevant the next. In that environment, the biggest advantage isn't perfect code. It's malleable code . Code that can bend, adapt, and evolve as the business learns. What Is Malleable Code? Malleable code is software that is easy to change. It isn't necessarily perfect. It isn't over-engineered. It isn't designed to solve every future problem. Instead, it's designed to support continuous experimentation. Malleable code allows teams to: Launch MVPs quickly Test assumptions rapidly Respond to customer feedback Pivot when necessary Add new features without major rewrites Remove failed features with minimal effort Think of it this way: Perfect code optimizes for certainty. Malleable code optimizes for uncertainty. And startups operate almost entirely in uncertainty. When you're still searching for product-market fit, the ability to adapt is often more valuable than technical elegance. Why "Perfect" Code Often Hurts Startups Software engineers love solving technical problems. It's natural. Building a scalable architecture feels productive. Refactoring code feels productive. Designing the perfect system feels productive. But startup success isn't measured by code quality. It's measured by business outcomes. Questions such as: Are customers using the product? Are they paying for it? Are they returning? A

For a long time, I thought programming wasn't for people like me. Not because I wasn't interested in technology. Not because I didn't enjoy solving problems. But because I kept hearing the same thing over and over again: "You need to be good at math to become a programmer." The more I heard it, the more I believed it. Whenever I saw developers building websites, apps, or cool projects, I assumed they were all math experts. 🧮 I imagined them solving complex equations all day while I struggled with basic math concepts. So before I even wrote my first line of code, I had already convinced myself that programming probably wasn't for me. And honestly, I think many beginners feel the same way. 🤔 The Fear Was Bigger Than The Reality When I finally started learning programming, I expected math to be my biggest challenge. It wasn't. My biggest challenge was understanding why things weren't working . I spent hours trying to figure out: Why isn't this button working? 🖱️ Why is this variable undefined? 🤨 Why did this code work yesterday but not today? 😅 Why did fixing one bug create three new bugs? 🐛 Very quickly, I realized that programming wasn't testing my math skills nearly as much as it was testing my patience and problem-solving ability. Most of the time, the challenge wasn't: "Can you solve this equation?" It was: "Can you figure out what's causing this problem?" 🧠 Logic Matters More Than Most People Think One of the biggest lessons I learned is that math and logic are not exactly the same thing. Yes, math uses logic. But you don't need to be a math genius to think logically. Programming is often about breaking a big problem into smaller, manageable pieces. For example: If a user clicks a button, what should happen next? If data is missing, what should the application do? If an error occurs, how should it be handled? That's logic. You're constantly thinking: "If this happens, then what should happen next?" And honestly, that's a huge part of software development. Some of

I've been digging into malware analysis lately, and one thing became clear pretty fast: before you ever touch a debugger or run a suspicious binary, you need to understand the landscape — what malware actually is, how it's classified, and what a safe, repeatable analysis workflow looks like. This post is my attempt to organize that foundation. No flashy exploit walkthrough here — just the core concepts I think anyone starting out in malware analysis needs to internalize first, because skipping this step is how people either get sloppy or get burned (sometimes literally infecting their own host machine). Problem Statement If you search "malware analysis tutorial," you mostly get tool-specific guides — "how to use Ghidra," "how to use Process Monitor" — without context on why you'd choose static vs. dynamic analysis, or how to build a lab that won't accidentally compromise your real network. I wanted to write down the methodology layer first: the classification of malware, the four analysis approaches, and the non-negotiables of lab isolation. This is the stuff that makes the tool-specific tutorials actually make sense later. What Malware Analysis Actually Is Malware analysis is the study of a malicious program's behavior — the goal is to understand what it does, how it got in, and how to detect/eliminate it across an environment, not just on one infected machine. A few concrete objectives that stuck with me: Determine the nature of the malware — is it an infostealer, a keylogger, a spam bot, ransomware? Understand the compromise — how did it get in, and what's the blast radius? Infer attacker motive — banking credential theft usually points to financial motive; persistence + C2 beaconing might point to espionage. Extract network indicators — domains, IPs, User-Agent strings — for network-level detection. Extract host-based indicators — registry keys, dropped filenames, mutexes — for endpoint-level detection. This connects directly to something called the Pyramid of P

California governor Gavin Newsom worked with the state's employment department to roll out an AI job loss tracker.

Use it with Claude, ChatGPT and more! Discussion | Link

A GitHub repository called design.md has been trending recently, accumulating over 1,400 stars. The concept is straightforward: provide AI agents with a persistent design document they can reference throughout their work. This approach addresses a practical challenge in agent development that many teams encounter. The Context Challenge When working on complex tasks, AI agents need to understand the broader picture. What's the architecture? What constraints exist? What approaches have been tried before? Typically, agents get context from: Current conversation (limited window) Code comments (often outdated) Documentation (if it exists) The issue is that this context is fragmented and temporary. When conversation moves forward, earlier context disappears. When documentation is outdated, agents make incorrect assumptions. A design.md provides a single source of truth that persists across sessions. What Belongs in design.md An effective design.md answers these questions: What are we building? Beyond feature lists, document the core purpose. Why does this project exist? What problem does it solve? What are the key architectural decisions? Document major choices and their rationale: "PostgreSQL was chosen over MongoDB because ACID guarantees are required for financial transactions" "Microservices architecture was adopted because components have different scaling requirements" What constraints exist? Technical constraints (performance requirements, browser support), business constraints (budget, timeline), and regulatory constraints (GDPR, HIPAA). What has been tried before? Document failed approaches to prevent agents from suggesting rejected solutions. What are the current challenges? Known issues, technical debt, areas needing improvement help agents prioritize work. How Agents Use design.md When starting a task, agents can: Read design.md to understand context Make decisions aligned with documented architecture Avoid solutions violating constraints Reference design.md i