NASA's TESS space telescope has discovered two 'super puffy' giant planets with the density of cotton candy.

A database of almost a million passports from around the world was leaked online. Note what happened. A high-value credential—a passport—was used in an ancillary low-value authentication system: ID verification for cannabis dispensaries. And it’s the low-value system that got hacked, putting the high-value credential at risk.

Is SpaceX planning to end its Transporter program?

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.

It's Day Four. Somehow it's still Prime Day. The deals are live. The deals are wild. The deals are almost feral. We are still here in the trenches digging up the deals and trends.

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Kids love 3D printers almost as much as K-Pop Demon Hunters. Just give in and get one.

Porn music videos have circulated on the fringes of the internet for years. Featuring everything from narrative-driven stories to hypnosis, they are proliferating across X as “bate fuel” for gooners.

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The author of a tell-all book says Meta is using her as an example to deter future whistleblowers.

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.

Qatar has become the place where FIFA experiments with the next generation of football technology. The results are already visible across this year’s World Cup.

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