标签：#analysis

It's been two weeks since Anthropic took its Mythos-class models offline after a Friday evening ultimatum from the Trump administration. The company sprang into action immediately, sending a barrage of executives to Washington, DC. But updates have been suspiciously lacking, with no resolution in sight. Anthropic declined to comment multiple times this week about the […]

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 […]

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

I wasn't surprised when I got the call that my dad was dying, even though we'd been estranged for many years. He'd suffered addiction for decades and eventually ran out of time, which also meant he ran out of time to reconcile with me. About 15 years after we stopped talking, my aunt and uncle […]

Just days after a gunman killed conservative activist Charlie Kirk, it became clear that President Donald Trump would use the assassination to fuel a crackdown on free speech. To avenge Kirk's death, the administration vowed to go after so-called "antifa" (otherwise known as antifascist) terrorists. Now that promise is bearing fruit. This week, eight Texas […]

The Steam Machine is here, with a base price of $1,049. Yes, that's nearly twice the price of a PS5, but what you're buying here isn't a console but a full-on PC - and a tiny one at that. Valve says it's selling the Steam Machine basically at cost. So we asked ourselves: What would […]

It's no secret that just about every aspect of video games is getting more expensive. Game consoles are getting regular price hikes, PC components are spiking in cost, and the golden age of affordable handhelds is over, all largely due to the global RAM shortage, which has had a similarly costly impact on PC gaming, […]

A static-analysis approach to unifying 46 repositories (37 air-closet-side + 9 mall-side) of legacy production code into one knowledge graph. Why simply 'letting AI read the code' isn't enough, why I had to chase down boundary nodes (API endpoints, DB tables, Event topics), how I dealt with framework and library diversity, and what 3 months of trial and error solved or didn't solve — looking back through actual git history.

2026 AI Coding Agents Are Making Developers Forget How to Code: Why the Convenience Trap Threatens Innovation As AI‑driven platforms like Atoms, Devin, Windsurf, Cursor, and Warp reshape software engineering, the real cost may be a gradual erosion of core programming fundamentals. The latest MarkTechPost comparison shows AI coding agents moving from novelty to mainstream. Teams report faster feature cycles, fewer lines of manual boilerplate, and a shift toward intent‑first workflows. Yet beneath the productivity headlines lies a subtle trade‑off: every hour spent letting an agent write code is an hour not spent exercising the mental muscles that let us reason about edge cases, optimize performance, or invent novel algorithms. The Rise of Intent‑First Development Modern agents excel at turning a natural‑language description into a runnable diff. Atoms uses multimodal reasoning to interpret UI sketches; Devin can autonomously open pull requests after a high‑level prompt; Windsurf lets engineers edit across files with conversational commands. This paradigm reduces the cognitive load of syntax hunting and lets engineers focus on what the software should do, not how to type it. Measuring the Productivity‑Skill Trade‑off Data from early adopters shows a 38% cut in boilerplate typing and a 22% boost in sprint velocity. However, internal surveys reveal a 15% drop in self‑reported confidence when debugging low‑level concurrency bugs, and a 20% increase in reliance on agent‑generated explanations rather than personal code walkthroughs. The numbers suggest a growing dependency that mirrors the calculator effect seen in mathematics education. Second‑Order Shifts: From Craftsmanship to Orchestration As routine typing fades, engineers spend more time validating AI output, refining prompts, and orchestrating multi‑agent pipelines. Traditional code reviews evolve into “prompt reviews,” where the gatekeeper judges whether the AI captured the business intent. New roles—AI Interaction

Let me confess something a little creepy. I have a habit of peeking at other people's dev posts. Not stealing the writing — relax. I run a tiny read-only job that fetches the public pages on dev.to, Zenn, and Qiita and counts only the boring parts: titles, post times, like counts. Who published what, at what hour, and how far it traveled. Then it tallies the lot. The reason is petty: my own posts weren't landing. The content is already in my hands — so I wanted to know how much the rest, the when and how you publish , actually moves the needle. By the numbers, not by gut. So I counted across three platforms. And the conditions that make a post fly turned out to be roughly mirror images between Japan (Zenn / Qiita) and the English-speaking world (dev.to). Here's the story. First, my most important disclaimer This post is full of numbers, so let me put up a guardrail before any of them. This is correlation, not causation . A result like "weekend posts don't do well" could mean the weekend itself is bad — or it could mean people who post on weekends are just dashing something off on the side. The data can't separate those. Please read it that way. Also, I only keep aggregate numbers I computed myself . I don't store or reuse anyone's article body (read-only GET, count the features, throw the page away). I peek, but only at the overall shape . Nobody gets singled out here. With that out of the way — four findings I enjoyed. 1. The best hour to publish is just your readers' time zone This one came out cleanest. On Qiita , posts published in the morning win (+32pt in the GOOD group). Midday is +14pt. Evening is -32pt, late night -14pt. Zenn likes midday too (+27pt). Late night is -15pt. dev.to is the exact opposite. Late night Japan time scores +7pt — Japanese evening is actually weak. The trick is obvious once you see it. dev.to's readers are English-speaking, mostly US. Late night in Japan is the US working day. Zenn and Qiita readers are in Japan, so the Japanese morni

Yesterday, Snap debuted its new $2,195 Specs glasses. In an interview with CNBC, Snap CEO Evan Spiegel described the Specs as something the company had been working on for more than 12 years, an attempt to "bring computing into the world" and "make it more human." He positioned them as a device to help people […]

Horror is having a moment. In 2026, the genre is especially well-represented: new blood is dominating the box office through films like Backrooms and Obsession, established names like Sam Raimi and Damian McCarthy are at the top of their game, and long-running franchises like 28 Years Later and Resident Evil continue to stay relevant. But […]

By this time next year, Fox Corporation CEO Lachlan Murdoch intends to have added Roku to his already expansive media empire. Should the acquisition go through, Fox will gain control of Roku's modest library of original programming, and the newly combined company will become "the third-largest player in U.S. television" in terms of viewing share. […]

At Washington's request, Anthropic suddenly took its newest and most powerful AI models offline over the weekend. The American company said it had little choice after the White House demanded it block access for all foreign nationals, including its own employees. Abroad, the incident offered a sobering reminder that the US not only dominates frontier […]

In 2026, Marvel and Mattel are both releasing projects designed to capitalize on people's love for iconic animated heroes from their childhoods. Masters of the Universe has put a live-action He-Man on the big screen, and the second season of X-Men '97 is about to fling some of Charles Xavier's mutants into an apocalyptic future. […]

I turned off Siri on the Mac years ago and never looked back. Similarly, I found Apple Intelligence so fruitless I never engage with it. But the new Siri AI coming to macOS 27 Golden Gate has at least got me slightly rethinking things. I'm still early in testing Siri AI, as I've only had […]

Three years ago, when the women's World Cup kicked off in Australia and New Zealand, my social feeds were in a strange place. Twitter had just transformed into X, newcomer Threads was seemingly ascendant, and places like Bluesky had yet to garner much momentum. It left me with an odd, and admittedly silly, dilemma: I […]

Electricity demand from AI data centers is pushing everyone — including automakers like GM and Ford — into the energy storage business.

Electricity demand from AI data centers is pushing everyone — including automakers like GM and Ford — into the energy storage business.

Cities are dynamic, not static grids, and urbanization is a "spiky," cyclical, and asynchronous process.