标签：#war

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

Mesh came out of stealth in February with a $50 million Series A.

There is a popular misconception that if you do TDD, your design also stays correct. That if the tests pass, quality is guaranteed. In AI-assisted development, this misconception is the kind that quietly accumulates — the more tests you have, the more invisible damage builds up underneath. All tests passed. The design was still broken. Here is what happened today. A function called safe_post.py had its signature changed. Two arguments — notify_sh and doctor_sh — were removed. The test suite passed in full. But the callers were still using the old signature. They were silently broken. Why did the tests pass? Because the test code itself was using the old signature. The tests had been written (by AI) at a time when the design intent was already misunderstood. The misunderstanding was baked into the tests from the start. Tests passing and the design being correct are two different things. "All tests pass" tells you only one thing: the implementation matches what the tests expect. Whether the tests express the right design intent is a separate question. TDD verifies "implementation against tests" — nothing more Let me restate the TDD definition. Red → Green → Refactor. Write a test. Write the implementation that passes the test. Refactor. In this loop, what the test verifies is whether the implementation meets the test's expectation. That is one verification — and only one. What TDD does not verify is whether the test itself correctly expresses the design intent. The structure looks like this: Design intent → Tests (← this link is not verified) ↓ Implementation (← this link is verified by tests) If the person writing the tests misunderstands the design intent, the tests will pass and the design will still be wrong. Machine learning engineer Hamel Husain calls this the "Gulf of Specification" — the gap between what you intended to measure and what your metric actually measures. Optimize hard against a flawed metric and you optimize hard in the wrong direction. The same d

Ask someone to name the most manufactured object in human history and you will hear guesses like the nail, the brick, or maybe the smartphone. The real answer is something almost nobody can name out loud: the MOSFET. This tiny transistor, invented at Bell Labs in 1959, is the on/off switch inside every microprocessor, memory chip, and connected sensor. An estimated 13 sextillion of them have been built since 1960, making the MOSFET not just the foundation of modern electronics but the most-produced artifact our species has ever made. What a MOSFET actually is MOSFET stands for metal-oxide-semiconductor field-effect transistor. Strip away the jargon and it is an electrically controlled switch with no moving parts. A small voltage on one terminal, the gate, controls whether current can flow between the other two. Billions of these switches flipping on and off billions of times per second is, quite literally, what computation is. The genius of the design is that it scales: shrink the transistor and you can pack more of them onto a chip while using less power per switch, the trend that drove decades of Moore's law. The breakthrough came from two engineers at Bell Labs, Mohamed Atalla and Dawon Kahng, who fabricated the first working MOSFET in 1959. Their key insight was using a thin layer of silicon dioxide, ordinary glass, to insulate the gate from the silicon underneath. That oxide layer turned out to be the unlock that made silicon the dominant material in electronics, edging out the germanium used in the very first transistors of the late 1940s. Why it beat every earlier transistor The point-contact transistor demonstrated in 1947 and the integrated circuit of 1958 were both monumental, but neither was easy to mass-produce by the standards we take for granted today. The MOSFET was different. It was simpler to fabricate at scale, drew far less power in its complementary (CMOS) configuration, and lent itself to the photolithographic processes that let manufacturers pr

Anthropic's engineers ship eight times more code than they did a few years ago. And they had to start scheduling lunches so people would talk to each other. Fiona Fung, who leads the Claude Code team, said it on Lenny's Podcast last week. Working with agents all day had started to feel isolating. The team was fast, but they'd stopped running into each other. So they added pairwise programming lunches and hackathons — rituals to put back the thing that used to happen on its own. Eight times the output. Scheduled conversation. That ratio is worth sitting with. Whatever goes missing here doesn't show up in the metrics. It doesn't throw an error. It just quietly stops being available. Here's the part that bugs me most. Ask an AI whether your approach is sound and it mostly tells you it is. Not because it's lying — because it's answering the prompt. No stake in the outcome, no history with the system, no memory of the last three times this exact idea was tried and quietly failed. A colleague pushing back is a different thing. They've got context you never typed into the window, because they were there when it was earned. They're going to maintain this too. They might be wrong — but wrong in a direction you hadn't thought of. An agent can't disagree with you like that. It agrees faster. Same with scope. The agent builds what you ask for, all of it, thoroughly. It won't mention that the third feature is the one nobody will use, or that "good enough" happened two iterations ago, or that something next door already solves most of this. Knowing when to stop comes from someone who's watched a codebase rot under a hundred individually-reasonable decisions. And it only knows what you put in front of it. The person who worked on payments remembers the edge case you're about to recreate. The junior who joined three months ago still sees the thing everyone stopped noticing. That gap — between what's in the window and what isn't — is where the expensive mistakes live. Then the part

I noticed something a few months ago. I was talking less to my colleagues. Not because anything was wrong. I had a question, I described it to an AI, I got something useful back. Why loop in a human if the loop is already closed? It took a while to name what was actually happening. There's a version of the AI story where the interesting work disappears. The agent implements. The spec session produces the plan. Humans review the output. What's left? Ticket hygiene and rubber stamping. Engineering as a series of approvals. I think that's wrong. But I understand why it feels true. Here's what I think is actually happening instead. The agent produces the increment. But the agent doesn't decide what the increment should move toward. It doesn't know whether this library is the right bet for the next three years. It doesn't know which of two implementation approaches leaves options open and which quietly closes them. It doesn't know whether the architectural call made today creates a problem nobody will notice until the system is under load eighteen months from now. That work — giving the project direction, validating trade-offs, deciding what the system becomes — isn't specable. You can't write a ticket for it. And it's not going away. The craft didn't disappear. It moved. Direction is the word I keep coming back to. The agent executes well. It implements against a spec. It generates options when you ask for them. But it doesn't carry a point of view about where the system should go. It doesn't have a stake in the decision. It will implement the wrong architectural direction just as confidently as the right one, if that's what the spec says. Someone has to hold the direction. Someone has to know enough about the codebase's history, the team's constraints, and the product's trajectory to say: not that library, we've been down that road. Not that pattern, it doesn't survive the load we're heading toward. This approach now, that refactor later, in this order, for these reaso

"A fool with a tool is still a fool." — often attributed to Grady Booch I keep coming back to this quote when I watch teams adopt AI. In my last post ( https://schrottner.at/2026/06/18/The-Wrong-End-of-the-Problem.html ) I wrote about shifting the engineering process left — spec sessions, autonomous agents, humans reviewing output rather than writing it. A few people asked the obvious follow-up: if an agent implements and an AI reviews, why do I need a team at all? It's a fair question. And I think the answer is in that quote. The agent validates against your prompt. That's it. If your thinking is muddled, the output will be muddled — just faster and at greater cost. An agent doesn't tell you that you're solving the wrong problem. It solves whatever problem you gave it, thoroughly and without complaint. Most AI usage right now treats AI as a tool. Which means the quality of the output is bounded by the quality of the thinking that went into the prompt. A fool with a tool is still a fool. The tool just makes the foolishness more expensive. The team is the check on intent. Not after the agent has burned three sprints on the wrong thing — before it starts. That's what mob planning actually is, when you think about it. Not a meeting. Not process overhead. It's the place where bad ideas get caught before they get expensive. Where someone asks "wait, why are we building this" before an agent runs with it for a week. But there's something else happening in that room that I think gets underestimated. It's where the learning happens. Not just prompting. System thinking. Architectural patterns. How to decompose a problem. Why a certain approach fits this codebase and another doesn't. How a senior frames a problem before an agent ever touches it — the mental model that makes the output actually good. Right now that knowledge isn't transferring. Everyone is heads-down with their own tools, developing their own habits in isolation. Engineer A gets dramatically better output than