标签：#war

The Supreme Court's decision to limit geofence warrants is a win for privacy advocates, who called their use unconstitutional but sought an outright ban.

This is the fourth post in Craft & Code , a short Friday series about what carpentry can teach us about AI, skill and the future of software. Last week I worried about where the next generation's judgement will come from. This week, why we may not notice it is missing until it is too late. My father built me shelves in an alcove when I was small, and I mentioned in the first post that they may still be there for eternity. The other side of that story is the one every household knows: the shelf that is not quite right. The one that sags under a row of books, or sits a degree off true so that anything round rolls gently to one end. You do not need to be a carpenter to see it. A bad joint, a door that will not close, a shelf that dips — the material tells on the maker, immediately and to everyone. That is the comforting version of the analogy, and the one I expected to write: carpentry is honest about its failures because they are visible, while software can look polished and be rotten underneath. A wonky shelf looks wonky; bad software looks finished. It is a tidy line, and there is real truth in it. But it is only half the truth, and the more interesting half should worry us — because the moment you go up from a shelf to a serious piece of engineering, the comfort falls away completely. Consider two of the most admired structures of the last century. The Tacoma Narrows Bridge was designed by one of the leading suspension-bridge engineers of his day: elegant, slender, celebrated. It opened in the summer of 1940 and tore itself apart in the wind that November, twisting like a ribbon because the design had not reckoned with how the deck would behave aerodynamically. Nobody had seen a wonky bridge; it looked magnificent. The flaw was real, fundamental, and invisible until the wind found it. The Citicorp Center in New York, finished in 1977, was a triumph of structural engineering, raised dramatically on great columns at the midpoints of its sides. Only after it was compl

Pocket sells a $129 credit card-shaped puck, which sticks to the back of your phone, and promises unlimited recordings, transcriptions, and to-do items.

Flipper Device's new Busy Bar will retail for $249.

When I write code in unfamiliar territory, I write three lines, then I run it. Then I write three more lines, and I run it again. I've been doing this for twenty-four years. It's the most specific habit I have. I almost didn't write this article, because the habit feels too small to be worth describing — but then I noticed that it's the part of my way of working that I can never seem to explain to someone in real time. It needs writing down. Three principles The discipline rests on three things I believe about writing code. They're not deep. They've just stayed with me. 1. Trust nothing but your own code. If you can't trust the code you wrote yourself, what can you trust? Not a library, not a vendor's documentation, not your own assumption from yesterday. The only thing in the system whose behavior you can fully verify is the code you just typed, by running it. 2. Write in code, not in language. If you're describing what the code should do in Japanese or English, you're spending the same time you could have spent writing the code itself. By the time the code runs, the description is already done — by the code, in a more precise form than any language could give it. 3. Make three lines complete. The three lines you just wrote should be complete. Error handling included. Validation included. Logging included. Not "I'll add validation later." Not "I'll wrap it in a try-catch later." Three lines, complete, then run. (There's a small exception to this. Sometimes you do want to ignore every error and move on — for instance, when you're trying to understand whether the happy path works at all before you care about anything else. That's a different mode, used deliberately. It's not the same as "I'll handle errors later.") Why three lines Three lines is roughly the unit of thought I can hold completely. Five lines, and I start guessing what the third line did. Ten lines, and I'm reading the code as if it were someone else's. Three lines is the size that stays mine. When thre

The design doc is fourteen pages. Clean service boundaries, thoughtful API contracts, a deployment story that handles rollback without incident. Six months of work. The team is proud of it, and the work is genuinely good. Three questions will tell more about this system than all fourteen pages. Not questions about implementation details or technology choices. Questions about how the system was designed to age, to fail, and to be understood by someone who didn’t build it. Most architecture conversations never reach these questions, which is part of why the gap between a good system and a great one is often invisible until it is suddenly very visible. What does this make hard? Good architecture conversations focus on what a design enables: faster deployments, independent scaling, and clearer ownership. The question that separates architectural thinking from implementation thinking is the inverse . What does this make hard? Every decision forecloses options. The service boundary that gives teams autonomy makes cross-service transactions expensive. The data model that reads cleanly under expected load makes certain write patterns awkward. The abstraction that simplifies onboarding makes some categories of refactoring nearly invisible as possibilities. These are not arguments against the decisions. They are the other side of every decision, and that other side exists whether or not anyone names it. The design doc that holds up over time is not the one where nothing is difficult. It is the one where the difficult things are named. “This approach makes distributed transactions impossible, and here is why we have decided to accept that constraint.” That sentence, or something close to it, belongs in every significant architecture document. Its absence is not a sign that the constraint wasn’t considered. Often it was. But without the name, the constraint becomes invisible to everyone who wasn’t in the room, which eventually includes the original team. When joining a system s

The AI Engineer World's Fair has attracted a lot of adults, and they brought their kids too, for...

GitLab's 2026 AI Accountability Report highlights an AI Paradox: although 78% of developers say they code faster, overall software delivery has not accelerated due to downstream testing and review bottlenecks and new challenges for enterprise governance and traceability. By Sergio De Simone

Has Silicon Valley been building the wrong things?

Eager to find more public AI-related companies that may do as well as Nvidia, Wall Street investors think they've found a winner with Micron.

For some people, the ice in a beverage is almost as important as the drink itself. That’s the audience Govee had in mind when designing its latest ice maker, the GoveeLife Smart Nugget Ice Maker Pro. This $500 premium smart home gadget is aimed at those who crave what’s called “the good ice,” the soft, chewable […]

I’ve always been fascinated by PDFs. They look simple on the surface. Just a document you can open anywhere but underneath they’re a full layout engine, object graph, drawing model, and archival format all at once. I enjoy that mix of precision and complexity and that is exactly what led me to build PDF::Make (and yes I had some help from Claude LLM). I wanted a fully featured toolkit that could both generate PDFs and let me inspect/edit them programmatically. At the low level, PDF::Make exposes the raw building blocks of the format: PDF objects, pages, the drawing canvas, a parser/reader, and import/merge primitives. This is the layer you reach for when you need fine grained control or want to work with the structure of a document directly. For everyday document creation, PDF::Make::Builder sits on top of that foundation and provides a higher level API. It handles the boilerplate of page setup, fonts, text flow, and layout so you can produce a polished PDF in just a few lines of Perl. The same toolkit is also designed for post-processing. You can open an existing PDF, extract structured text along with its coordinates, and then draw annotations or overlays back onto the page, making it straightforward to build review, QA, or markup workflows on top of documents you didn’t originally generate. This post shows a practical two-step flow: Create a PDF Re-open it, extract text coordinates, and draw border highlights around matched words 1) Create a PDF with PDF::Make::Builder Script: #!/usr/bin/perl use strict ; use warnings ; use PDF::Make:: Builder ; my $pdf = PDF::Make:: Builder -> new ( file_name => ' source_demo.pdf ', configure => { text => { font => { family => ' Helvetica ', size => 12 , colour => ' #222222 ' }, }, }, ); $pdf -> add_page ( page_size => ' Letter ') -> add_h1 ( text => ' PDF::Make blog demo ') -> add_text ( text => ' PDF::Make builds and edits PDF files directly from Perl. ') -> add_text ( text => ' In the next step we extract text coordinates and

The Chinese supercomputer LineShine was ranked as the fastest in the world, despite not using any GPUs.

Santa Clara, 2029. A speculative fiction about hegemony, sanctions, and the playbook nobody followed.

Paul Meade, the Apple vice president in charge of the Vision Pro headset, is reportedly leaving the company to join OpenAI’s hardware team.

Habit stacking is one of my goals this year. I've been so focused on improving my software...

Hello Devs, for months, I felt confident about system design interviews. I'd watched endless YouTube videos. I'd studied architecture diagrams. I could explain how Netflix builds recommendation systems. I understood Kafka, Redis, load balancers, and microservices. I'd memorized the designs of Twitter, Uber, YouTube, and TinyURL. Then I sat down for my first real system design interview and froze. The interviewer asked: "How would you design a notification system?" I had memorized notification systems. I knew about push notifications, email queues, delivery workers, and retry logic. I could recite architectural patterns. But suddenly, none of that helped. I didn't know which questions to ask first. I started designing before understanding the actual requirements. I built architecture for problems that didn't exist. I missed obvious bottlenecks. I couldn't articulate why I made specific trade-offs. When the interviewer pushed back, I had no framework to adjust. I failed that interview. But that failure taught me something crucial: System design interviews aren't about knowing technologies. They're about knowing how to think. After that, I went back and systematically practiced 20 system design problems. Not passively watching solutions. Actually designing. Making mistakes and refining my approach. And somewhere around problem 12, a pattern emerged. The best candidates didn't know more technologies than anyone else. They had a framework . They asked the same questions in the same order. They structured their thinking consistently. They could handle curveballs because their framework was flexible. They reasoned through trade-offs explicitly. Here's the framework that finally made it click for me. The Problem with Memorization Before I share the framework, let me explain why memorizing designs fails. When you memorize " How to Design Twitter," you learn: Use relational databases for users and tweets Use NoSQL for timelines Cache with Redis Use message queues for fanout S

Most developers treat SEO like that one dependency you know you need but keep putting off. You build a fast, clean site with solid architecture, then hand it off to a "marketing person" who asks you to add keyword-stuffed meta descriptions. Here's what changed in 2026: search engines place heavy emphasis on Core Web Vitals, which measure loading performance, interactivity, and visual stability of web pages. The technical foundation you're already building? That's 80% of modern SEO. Let me break down what actually matters when evaluating SEO services as a developer. The Technical Reality Check Technical SEO is the foundation that everything else sits on. On-page optimization and link building amplify a technically sound site. Applied to a technically broken site, they produce unpredictable, often disappointing results. If an SEO service can't speak your language about INP metrics, structured data, or mobile-first indexing, run. What Dev-Focused SEO Services Should Cover Core Web Vitals (Not Just PageSpeed Scores) Core Web Vitals (LCP, CLS, INP) are confirmed ranking factors — INP replaced FID in March 2024. Any SEO service still talking about First Input Delay is using outdated information. What to look for: Field data analysis from real users (not just lab tests) Specific fixes for Interaction to Next Paint Understanding of when to optimize vs. when to rebuild Crawlability and Rendering Google now clarifies that pages returning non-200 status codes (like 4xx or 5xx) may be excluded from the rendering queue entirely. If you're running a JavaScript-heavy framework, this matters. Red flag: SEO services that don't understand Server-Side Rendering (SSR) or Static Site Generation (SSG). Structured Data Implementation Structured data helps search engines understand what your content is about, not just what it says. In 2026, this matters for traditional search and AI search alike. Schema markup isn't just about rich snippets anymore. It's how AI systems like ChatGPT and Per

Why 40V Input and 500mA Output Matter in Noise-Sensitive Designs You’ve probably fought a power rail that looked clean on a multimeter but still trashed your 24‑bit ADC readings. The culprit is rarely the DC level—it’s the broadband noise, switching artifacts, and line‑frequency ripple that ride on top. In precision analog, RF, and sensor signal chains, even 50 µV of supply noise can bury a 1 mV sensor signal or degrade an RF PLL’s phase noise by 10 dB. The MAX20151R addresses this head‑on with a combination that’s hard to find in a single LDO: a 40 V input range, 500 mA output drive, and just 6.5 µV RMS output noise (10 Hz–100 kHz). That wide input headroom lets you power sensitive circuitry directly from a 12 V or 24 V industrial rail, an automotive battery, or a noisy intermediate bus without a pre‑regulator. You eliminate an entire buck converter stage, saving board space and avoiding the switching noise that would otherwise require heavy filtering. The 500 mA output current is equally important. Many ultra‑low‑noise LDOs top out at 200 mA or 300 mA, forcing you to split rails or add a discrete pass transistor. With 500 mA, the MAX20151R can comfortably supply a mixed‑signal chain—an MCU, a precision ADC, a low‑jitter clock, and a handful of op‑amps—from a single quiet rail. And because the device maintains its noise performance across the full load range, you don’t have to derate your noise budget as current increases. Field experience shows that transient events on 24 V vehicle buses can easily exceed 40 V during load dump. The MAX20151R’s 40 V absolute maximum input rating, combined with integrated reverse‑voltage protection down to –40 V, gives you a robust front end that survives those spikes without external clamping. This is a practical necessity for any design that must pass ISO 7637‑2 or similar automotive transients, and it’s a key reason engineers are migrating from lower‑voltage LDOs to the MAX20151R in harsh electrical environments. Key Takeaway: If