标签：#war

Memory Chips Supply chain strategy from electronics production engineering, 500–50k units/year Introduction "Order from Digi-Key" is a prototyping strategy, not a production strategy. The 2020–2023 IC shortage demonstrated that supply chain resilience must be designed in — not improvised when lead times hit 52 weeks. The Sourcing Tier Structure Tier Examples MOQ Price Premium Lead Time Risk Authorized dist. Digi-Key, Mouser, Newark 1 pc +25–40% 1–3 days (stock) Lowest Franchise dist. Arrow, Avnet, TTI 100–1k Baseline 2–8 weeks Low Manufacturer direct TI, Infineon, ST portals 1k–10k+ −10 to −30% 8–20 weeks Low Regional aggregators IC-Online, local dist. Mixed Variable Variable Medium Spot market Brokers, eBay 1 pc +50 to +500% Days High Never use spot market for ICs without incoming inspection. Counterfeit STM32, ESP32, and common analog ICs are well-documented. Volume Pricing Reality Illustrative for a $2.50 MCU: Volume Digi-Key Arrow/Avnet Manufacturer Direct 100 $3.10 $2.65 N/A 1,000 $2.75 $2.15 $1.85 10,000 $2.40 $1.70 $1.25 50,000 $2.10 $1.40 $0.90 The franchise/direct savings are material at 1k+ units. Establishing Arrow or Avnet relationships pays for the admin overhead within 2 production cycles. BOM Resilience Framework For each critical component, document: Primary source : authorized distribution or direct Secondary distributor : alternative channel for same part Alternate part : functionally equivalent, different manufacturer, validated Buffer stock : target weeks at production rate Lead time worst-case : historical peak, not current During normal periods: 4-week buffer, one secondary source, one qualified alternate. For 5+ year product lifecycles: qualify the alternate before you need it. Practical Sourcing Mix: 500–5k Units/Year Component Type Primary Secondary Notes Commodity passives Digi-Key/Mouser + Yageo/Walsin Arrow Annual pricing agreements MCUs < $3 Arrow direct IC-Online for gap fills 90-day POs, buffer stock MCUs $3–$10 Manufacturer direct + A

Security Team: “I have a major Grype...with what I Syfted out of your provided image." Developer: “Well your Grype is slowing me down...let’s tone it down a notch.” While deploying bookstack into my local environment, this issue surfaced. It is true for many organizations today deploying images and packages in their environment. How can this noise fatigue in the software supply chain be remedied? Add a .gype.yaml file to the root directory of your project. This will allow grype to ignore certain CVE's that do not execute or pose a threat in your environment. The yaml config can be as simple as below: Linux Environment # grype.yaml ignore : - vulnerability : CVE-2026-32631 reason : " Platform-specific false positive. Git for Windows only; not applicable to this Linux-based image." - vulnerability : CVE-2016-2781 reason : " Chroot escape via ioctl. Containers rely on namespaces/cgroups, not chroot, so this path isn't exploitable here." OR # grype.yaml ignore : - vulnerability : CVE-2026-32631 - vulnerability : CVE-2016-2781 This will help developers and security engineers get along better. 😃 Grype config reference: https://oss.anchore.com/docs/reference/grype/configuration/

A new video shows the final production version of the upcoming Clicks Communicator, a BlackBerry-like smartphone that runs modern apps.

You have seen this loop before. An agent starts a “simple” task, say scrape listings, refactor a repo, research a market, or whatever. It fails, it retries, it re-reads context, it apologizes and tries all over again. Twenty minutes in and the dashboard shows six figures of tokens and zero useful outputs or deliverables. The model did not misbehave on purpose. The orchestrator never had a hard budget gate with an ROI in mind. Skillware v0.4.0 ships a new skill for exactly that gap: monitoring/token_limiter . It lets you monitor and limit any agent’s token budget in real time — Gemini, Claude, OpenAI, DeepSeek, Ollama, custom Python loops, you name it. Same skill, same JSON, any runtime. What Skillware is in a nutshell Skillware is an open registry of installable agent capabilities . Each skill is a bundle: skill.py — deterministic Python ( execute() returns JSON) instructions.md — when the model should call the tool manifest.yaml — schema, constitution, issuer Tests and docs — shipped in the wheel You load by ID, adapt for your provider, call execute() on tool use. The model decides when , the skill decides how , predictably, every time. That split matters for budget control. You do not want the LLM guessing whether it is “allowed” to spend more tokens. You want a small, auditable function that answers: continue, warn, or stop. Meet the Token Limiter This skill is a budget gate , not a kill switch wired into OpenAI or Anthropic. After each model turn, your host loop passes cumulative usage. The skill returns one of three actions: Action Meaning CONTINUE Under the soft threshold — keep going WARN Approaching the limit (default 80%) — tighten scope FORCE_TERMINATE Hard ceiling hit — stop the loop Important nuance: the skill does not cancel API sessions or kill processes. It returns a structured decision. Your orchestrator must act on it. That is by design — Skillware skills stay portable and provider-neutral. No skill-specific API keys. No network calls. Pure Python m

When most people hear Bitcoin , the conversation usually starts with price. But for developers, Bitcoin is much more than a chart. Bitcoin is a distributed system operating without a central authority. It combines networking, cryptography, game theory, economics, and software engineering into a protocol that has remained operational for years while processing value globally. As a software developer, what fascinates me most is not speculation it’s the architecture. Some concepts every developer can appreciate: ⚡ Distributed Consensus Thousands of nodes independently verify the same rules without trusting each other. 🔐 Cryptography in Practice Digital signatures make ownership verifiable without revealing private keys. ⛏️ Proof of Work A mechanism that converts computation into security and coordination. 🌍 Open Source at Global Scale Anyone can inspect the code, run a node, contribute, or build on top of the ecosystem. 📦 Immutability Through Design Data integrity is achieved through incentives, validation rules, and chained blocks. Studying Bitcoin changes how you think about: System reliability Security models Network design Incentive structures Building software that survives failure Whether you plan to build in blockchain or not, Bitcoin is worth studying because it teaches principles that extend far beyond finance. Curious to hear from other developers: What concept in Bitcoin architecture changed the way you think about software systems?

If you've ever wondered how to visualize, teach, or explore keyboards without owning physical hardware, a keyboard simulator is the answer. In this in-depth guide, we explore what keyboard simulators are, how they work, and why they are changing the way people learn to type. Defining a Keyboard Simulator A keyboard simulator is a software application that digitally recreates the visual, functional, and interactive behavior of a physical keyboard. Unlike a simple on-screen keyboard that merely serves as a typing aid, a true keyboard simulator renders the keyboard in detail — often in three dimensions — and responds to keystrokes in real time, creating an immersive and educational experience. The best keyboard simulators go far beyond static images. They animate individual key presses, replicate the visual design of specific keyboard models, support multiple layouts (QWERTY, Dvorak, AZERTY), and even show animated hands performing the typing — making them extraordinarily useful for remote teaching, accessibility testing, content creation, and learning to type. 💡 Did you know? The Keyboard Simulator by Roboticela is one of the most advanced free and open-source keyboard simulators available today, featuring 3D interactive rendering powered by React Three Fiber, five authentic laptop keyboard models, and eight beautiful visual themes. The Core Components of a Keyboard Simulator A fully-featured keyboard simulator typically includes several key components that work together to create a complete experience: 🎮 3D Rendering Engine: Displays the keyboard model from any angle with smooth rotations and zoom capabilities. ⌨️ Real-Time Key Feedback: Every keystroke on your physical keyboard mirrors instantly on the 3D model. 🖐️ Hand Animation: Animated hands show proper finger placement and movement as you type. 📝 Document Editor: A built-in text editor captures your input and links it to the keyboard visualization. 🎨 Theme System: Multiple visual themes make the experience beau

TL;DR Anthropic recently published When AI Builds Itself, an essay explaining how AI is...

A device that resets in the field is not always the hardest problem. The harder problem is a device that resets, comes back online, and leaves no evidence about what happened before the reboot. That is where a firmware black box becomes useful. This is the DEV.to edition of a Silicon LogiX technical article. The canonical English source is linked at the end. What a firmware black box is A firmware black box is a small diagnostic subsystem inside the firmware. Its job is to preserve enough information to support post-mortem analysis after a reset, watchdog event, HardFault, panic or unexpected reboot. It does not need to record everything. It needs to record the data that helps answer the first diagnostic questions: why did the device reset? how long had it been running? which firmware build was installed? what state was the application in? which task was active? did the watchdog fire? did memory, stack or heap margins collapse? did the network, modem, BLE, Wi-Fi or OTA flow fail just before the reboot? Without that data, every field reset deletes most of the evidence. Why sporadic resets are expensive Rare embedded bugs are often more expensive than obvious failures. A crash that happens every time in the same function can usually be analyzed with a debugger, logs and a repeatable test. A reset that appears once every ten days on a customer device is different. The cause may depend on a combination of: temperature unstable power brown-out cable length enclosure heating network drops modem state memory fragmentation stack exhaustion long uptime race conditions a peripheral that stops responding an OTA edge case In the lab, the product may look clean. In the field, the environment changes. The customer report often becomes: "it rebooted", "it stopped communicating", or "we had to power-cycle it". That is not enough for firmware diagnosis. What to capture A good first version does not need to be large. Start with a compact structure that survives the next boot: reset r

Every engineer has fantasized about it: starting over. Throwing out the old system and building something clean. No legacy constraints. No accumulated compromises. Just pure, intentional design. It never works that way. You can delete all the code. You can architect from scratch. You can make the best technical decisions possible. But you can't delete the organizational memory. You can't unlearn what the last system taught you. You can't escape the patterns that already run through the business, the workflows people have shaped themselves around, the problems you've already paid the cost of understanding. The new system will look clean. But it will be haunted. What rewrites actually inherit A rewrite isn't a fresh start. It's archaeology pretending to be innovation. The constraints don't go away. The old system wasn't overcomplicated because engineers were bad. It was overcomplicated because of customer requirements, regulatory expectations, performance demands, and edge cases that took years to discover. A fresh rewrite finds all those edge cases again. Slower this time, because you don't have documentation—you have broken customers and escalations. The system gets layers of protection again, but now it looks like paranoia instead of learned caution. The organizational memory becomes invisible. Someone fought for that data model three years ago. There was a reason. A business rule that couldn't be violated. A data consistency requirement that cost a quarter to figure out. The new system doesn't have the battle scars that explain why things are the way they are. So they get rebuilt differently, until they hit the same requirement at 2am on a Saturday. The workflow is already baked in. Users have shaped their behavior around the old system. Sales has built their pitch around certain capabilities. Support has written documentation and runbooks. Customers have automation that depends on specific behaviors. The new system is technically cleaner, but it forces change on

AI เขียนโค้ดแทนเราได้แล้ว — แล้วเราจะเหลืออะไรให้ทำ? มีประโยคที่ได้ยินบ่อยขึ้นทุกวัน: "เดี๋ยวนี้ใครยังไม่ใช้ AI ช่วยเขียนโค้ดบ้าง?" คำตอบคือ — แทบไม่มีแล้วครับ ตั้งแต่ GitHub Copilot, Cursor, Claude, ChatGPT ไปจนถึง agent ที่เขียนโค้ดเองได้ทั้ง project — เราใช้ AI ใน level ที่ต่างกัน: Level หน้าตา ตัวอย่าง 🎵 Vibe Coding พิมพ์สิ่งที่อยากได้ กด accept อย่างเดียว "เขียนหน้า login ให้หน่อย" → กด tab tab tab 🧩 Prompt-Guided คิดก่อน ถามทีละส่วน ตรวจทุกอย่าง "สร้าง UserService ที่ใช้ bcrypt hash password" 🛠️ Skill/Lint-Guided ใช้ AI เป็น editor ชั้นสูง — lint, refactor, test "refactor function นี้ให้เป็น table-driven test" 🏗️ Agent-Based ให้ AI run ทั้ง project — spawn subagent, PR, deploy "พอร์ต microservice นี้จาก Express ไป Fastify" แล้วคำถามคือ — ถ้า AI ทำทั้งหมดนี้ได้ แล้วมนุษย์อย่างเราเหลืออะไร? Unit Test — ตัวอย่างที่เห็นชัดที่สุด ลองดู unit test ที่ AI เขียนให้: // 🤖 AI-generated test func TestCalculateDiscount ( t * testing . T ) { tests := [] struct { name string input float64 expected float64 }{ { "zero" , 0 , 0 }, { "normal" , 100 , 90 }, // 10% discount { "max" , 1000 , 800 }, // 20% discount } for _ , tt := range tests { t . Run ( tt . name , func ( t * testing . T ) { result := CalculateDiscount ( tt . input ) if result != tt . expected { t . Errorf ( "got %v, want %v" , result , tt . expected ) } }) } } ดูเผิน ๆ — สวย, table-driven, ถูกต้องตาม Go convention 1 แต่ถามหน่อย — test นี้บอกอะไรเกี่ยวกับ business? "ส่วนลด 10% สำหรับยอด 100 บาท" — ทำไมต้อง 100? เป็นกฎจากที่ไหน? "ส่วนลด 20% เมื่อยอดถึง 1000" — แล้วถ้าลูกค้าเป็น member ได้เพิ่มอีก 5% ล่ะ? input: 0, expected: 0 — test นี้ cover edge case หรือแค่ cover บรรทัด? AI test ได้ถูกต้องตาม function — แต่มัน ไม่รู้ว่า business จริง ๆ คืออะไร AI ไม่รู้ Business Context — และจะไม่มีวันรู้ นึกภาพระบบ e-commerce: ลูกค้าซื้อสินค้า → ระบบตัดสต็อก → คำนวณส่วนลด → คิดค่าส่ง → ออกใบเสร็จ AI แยก test ทีละ function ได้: ✅ TestDeductStock — "ตัดสต็อก 1 ชิ้น" ✅ TestCalculateDiscount — "ส่วนลด 10%" ✅ TestCalculateShipping —

🚀 Build Your First Space Shooter Game with Limn Engine A Complete Step-by-Step Tutorial for JavaScript Beginners Welcome! In this tutorial, you'll build a complete space shooter game using Limn Engine — a zero‑configuration 2D game engine that runs in your browser. What you'll build: A spaceship that moves, shoots bullets, fights waves of enemies, and keeps score. All in about 100 lines of code . By the end, you'll understand: How to create a game loop How to handle keyboard input How to detect collisions How to use particles for visual effects How to manage game state (lives, score, game over) 🎮 Want to play the finished game? Click here to play Space Shooter Live! Before We Start What You Need A text editor (VS Code, Notepad, or any code editor) A web browser (Chrome, Firefox, Edge) Limn Engine — download epic.js from limn-engine-doc.vercel.app What You Should Know Basic JavaScript (variables, functions, arrays, if-statements) How to open an HTML file in a browser No game development experience required! Step 1: The HTML Structure Every Limn Engine game starts with a simple HTML file. <!doctype html> <html> <head> <script src= "asset/epic.js" ></script> </head> <body> <script> // All your game code goes here </script> </body> </html> What's happening: <script src="asset/epic.js"> — loads the Limn Engine library Everything inside the second <script> tag is your game code Save this as game.html and open it in your browser. You should see a blank canvas with a blue gradient background. Step 2: Setting Up the Game The first thing we need is a Display — this is the engine that creates the canvas, runs the game loop, and handles input. const display = new Display (); display . perform (); // Activates performance mode (dual-canvas rendering) display . start ( 800 , 600 ); // Creates an 800×600 canvas What's happening: new Display() — creates the engine display.perform() — turns on high-performance mode display.start(800, 600) — creates a canvas 800 pixels wide and 600 p

Look at almost any piece of electronics on your desk and you will find a small light staring back at you. A router with a row of blinking status lights. A power brick with a steady green dot. A development board with a tiny red point that flickers every time it does something. We barely notice these lights anymore, but each one descends from a single laboratory breakthrough in 1962, when an engineer at General Electric coaxed a sliver of semiconductor into glowing visible red for the first time. Who invented the first visible LED The engineer was Nick Holonyak Jr., a consulting scientist at General Electric's lab in Syracuse, New York, and a former student of John Bardeen, one of the inventors of the transistor. On October 9, 1962, Holonyak demonstrated the first practical visible-spectrum light-emitting diode. It emitted red light, and it worked at room temperature, which made it genuinely useful rather than a laboratory curiosity. What made his approach different was the material. Other researchers in the early 1960s were building diodes that emitted infrared light, which is invisible to the human eye. Holonyak gambled on a different alloy, gallium arsenide phosphide, and it paid off with the first light a person could actually see coming out of a semiconductor. He was so confident in the idea that he predicted LEDs would one day replace the incandescent bulb. At the time that sounded outlandish. Today it is simply how lighting works. Why a tiny red light mattered so much The incandescent bulb that Thomas Edison commercialized makes light by heating a filament until it glows. That is wildly inefficient, because most of the energy escapes as heat rather than light, and the filament eventually burns out. An LED works on a completely different principle. When current flows across a specially engineered semiconductor junction, electrons release their energy directly as photons. There is no filament to burn out, almost no wasted heat, and the device can switch on and o

SCOTUS falls short of deeming geofence warrants unconstitutional, though.

The world's two largest memory chip companies vow to build more memory lab fabs as South Korea positions itself as an AI tech powerhouse country.

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

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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