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The EU AI Act's Article 50 transparency rules went enforceable on August 2, 2026. If you ship a generative AI feature that touches EU users, you now owe the regulator a machine readable marking on your output. The obvious shortcut, drop a C2PA manifest and call it done, does not survive contact with production. Here is what actually works, with the code to make it real. The Two Layer Reality Article 50(2) requires effective, interoperable, robust, and reliable marking. The EU Code of Practice interprets that as at least two layers: signed metadata (C2PA) plus imperceptible watermarking (SynthID or equivalent). Fingerprinting is optional layer three. The reason for two layers is not bureaucratic. It is a screenshot. C2PA lives in a JUMBF metadata box. X strips it on upload. CDNs strip it during optimization. Screenshots destroy it entirely. Microsoft admitted this openly in its February 2026 Media Integrity report: preventing every attack on provenance is not possible. Invisible watermarks embedded in the pixel content survive those operations but carry very little information. You need both. Generating a Signed C2PA Manifest (Node) // npm install c2pa-node import { createC2pa , ManifestBuilder } from ' c2pa-node ' ; import { readFile , writeFile } from ' node:fs/promises ' ; const c2pa = createC2pa (); async function signGeneratedImage ( inputPath , outputPath , generationMeta ) { const asset = { buffer : await readFile ( inputPath ), mimeType : ' image/jpeg ' }; const manifest = new ManifestBuilder ({ claim_generator : ' firesafe/1.0 ' , format : ' image/jpeg ' , title : ' ai-generated-image.jpg ' , assertions : [ { label : ' c2pa.actions ' , data : { actions : [{ action : ' c2pa.created ' , digitalSourceType : ' http://cv.iptc.org/newscodes/digitalsourcetype/trainedAlgorithmicMedia ' , softwareAgent : generationMeta . modelName , }], }, }, { label : ' com.firesafe.generation ' , data : { model : generationMeta . modelName , modelVersion : generationMeta . modelVer

If you are building anything that touches European business data — onboarding, invoicing, KYB, fraud checks — you will eventually ask the same question I did: which countries can I actually get company data from, for free, without an account? I could not find this written down anywhere, so I worked it out the hard way while building a supplier checker. Here it is. The baseline: VIES The European Commission runs VIES , which validates VAT numbers across all 27 member states plus Northern Ireland ( XI ). It is free, it needs no key, and it is the obvious starting point. Two things about it are worth knowing before you build on it. It answers one question: is this VAT number currently registered. It does not tell you the company is solvent, trading, or that it has not been struck off. A company in liquidation keeps a cleanly resolving VAT number for months, because deregistration and insolvency are run by different authorities on different timetables. Name and address are returned for 25 of the 28 jurisdictions, not all of them. Germany and Spain confirm registration but publish no company name through VIES. I tested three valid numbers for each before accepting that. For those two, a yes/no is genuinely all you can honestly show. Where you can go further, free Ten countries publish enough through a national register to add something meaningful on top of VIES: Country Free register Reports company state Reports VAT-active Romania yes yes yes Poland yes — yes Slovenia yes — yes Estonia yes yes — France yes yes — Greece yes yes — Bulgaria yes yes — Latvia yes yes — Czechia yes — — Finland yes — — Company state means the register tells you whether a business is inactive, in liquidation, bankrupt, insolvent, terminated or struck off. This is the valuable column, and only six countries have it. VAT-active matters more than it sounds. VIES cannot distinguish "this is a real company that is not VAT-registered" from "this number belongs to nobody". Three registers can. Note th

1. Language Architecture in Ghidra When Ghidra loads an architecture (such as the MOS 6502), it parses the .ldefs manifest file, which declares metadata and binds three foundational specification pillars: The .pspec (Processor Specification): Defines the processor’s hardware context. It declares special-purpose registers (e.g., stack pointer SP , status/flags registers), default memory maps (RAM, ROM, I/O), and hardware interrupt vectors. The .cspec (Compiler Specification): Defines the ABI and calling conventions (e.g., parameter passing mechanisms), stack alignment rules, and return value handling. This is the critical building block enabling the decompiler to reconstruct assembly into readable C code. The .sla / .slaspec (SLEIGH Specification): .slaspec : The human-readable source file describing the instruction set architecture (opcodes, instruction formats, and p-code semantics). .sinc (SLEIGH Include): Modular inclusion files (typically used to split complex architectures like ARM or x86, or isolate instruction subsets like Thumb). Given the simplicity of the 6502, everything is defined directly within the .slaspec file. .sla : The compiled binary version of the .slaspec (generated by the Sleigh compiler). Ghidra loads this compiled .sla file into memory at runtime for optimal performance. 2. The Challenges of Reverse Engineering Undocumented Binaries When dealing with a binary compiled for an undocumented processor, Ghidra's default paradigm faces major limitations: The .slaspec file is unavailable. Ghidra attempts to aggressively disassemble everything. Analyzing an undocumented target requires a strict two-phase approach. 3. Missing .slaspec File Without a valid .slaspec definition, Ghidra renders ?? for every opcode. The primary objective when tackling an unknown CPU is precisely to reconstruct this missing .slaspec specification. 4. Overcoming Ghidra's Aggressive Disassembly By default, Ghidra (like most disassemblers) employs an exhaustive strategy (usin

Introduction As software applications grow in size and complexity, managing object creation becomes challenging. Creating objects directly using constructors can result in tightly coupled code that is difficult to maintain and extend. The Factory Method Design Pattern solves this problem by separating object creation from object usage. It provides a flexible and reusable approach for creating objects, making applications easier to modify and scale. What is the Factory Method Design Pattern? The Factory Method Design Pattern is a Creational Design Pattern that provides an interface for creating objects without specifying their exact classes. Instead of directly instantiating objects using the new keyword, a factory class creates and returns the required object. Definition Factory Method Design Pattern: A creational design pattern that defines an interface for creating objects while allowing subclasses or factory classes to decide which object to instantiate. Why Do We Need It? In traditional programming: The client creates objects directly. Code becomes tightly coupled. Adding new object types requires modifying existing code. Maintenance becomes difficult. The Factory Method pattern solves these problems by centralizing object creation inside a factory class. How It Works The client requests an object from the factory. The factory checks the requested type. The appropriate concrete object is created. The factory returns the object to the client. The client uses the object without knowing how it was created. Java Example interface Shape { void draw(); } class Circle implements Shape { public void draw() { System.out.println("Drawing Circle"); } } class Rectangle implements Shape { public void draw() { System.out.println("Drawing Rectangle"); } } class ShapeFactory { public Shape getShape(String type) { if(type.equalsIgnoreCase("Circle")) return new Circle(); if(type.equalsIgnoreCase("Rectangle")) return new Rectangle(); return null; } } public class FactoryPatternDem

If you've ever wondered whether that wild Pokemon you just caught has competitive potential, you've probably heard the term IVs (Individual Values) thrown around. IVs are the hidden genetics of every Pokemon — the 0–31 numbers baked into your Pokemon at birth that determine how strong it can ultimately become. But here's the thing: the game never tells you what your IVs are. You have to reverse-engineer them. In this post, I'll walk you through exactly how IV calculators work under the hood — from the official stat formula, to the nature modifier trick, to why you often get a range instead of a single number. Live Tool: Try the calculator at randompokemongenerator.me/iv-calculator — free, no sign-up required, supports Gen III through Gen IX. What Are IVs, Exactly? Individual Values are six hidden integers between 0 and 31 , one for each stat (HP, Attack, Defense, Sp. Atk, Sp. Def, Speed). They represent the genetic potential of a Pokemon and are permanently set when the Pokemon is encountered or hatched — they can never be changed by leveling up or any in-game action. A stat with 31 IVs reaches its maximum possible value at level 100. A stat with 0 IVs starts at its theoretical minimum. In competitive play, players typically hunt for Pokemon with at least 3–4 perfect (31) IVs , with some strategies deliberately using 0 IVs in Defense or Speed for tactical advantages. The IV system as we know it today started in Generation III (Ruby/Sapphire/Emerald). Gen I–II used a predecessor called DVs (Determinant Values) , which only covered four stats and worked differently — so if you're playing on Virtual Console or Gen I/II, this calculator won't apply. The Stat Formula (Gen III+) The foundation of everything is the official stat calculation formula introduced in Generation III and still used today: For HP: HP = floor(((2 × BaseStat + IV + floor(EV / 4)) × Level) / 100) + Level + 10 For all other stats: Stat = floor((floor(((2 × BaseStat + IV + floor(EV / 4)) × Level) / 100

Understanding the Prototype Design Pattern in Java Introduction When developing software, there are situations where creating a new object from scratch is expensive or time-consuming. For example, an object may require complex initialization, database access, or extensive configuration. In such cases, instead of creating a new object every time, we can duplicate an existing object. This is where the Prototype Design Pattern becomes useful. The Prototype Design Pattern is one of the Creational Design Patterns in Java. It allows developers to create new objects by cloning existing ones rather than instantiating them using constructors. What is the Prototype Design Pattern? The Prototype Design Pattern creates new objects by copying an existing object, known as the prototype. This approach improves performance by avoiding repeated initialization and allows developers to create multiple similar objects efficiently. In Java, cloning is commonly implemented using the Cloneable interface and overriding the clone() method. Why Use the Prototype Pattern? The Prototype Pattern offers several benefits: Reduces the cost of object creation. Improves application performance. Simplifies the creation of complex objects. Avoids repeated initialization code. Makes object creation more flexible. Real-World Example Imagine an online shopping application where thousands of product objects share similar properties. Instead of creating every product from scratch, the application can clone a prototype product and modify only the required attributes such as name or price. Other real-world examples include: Document templates Game characters Employee records Vehicle configurations Graphic design objects UML Structure The Prototype Design Pattern generally includes: Prototype Interface – Declares the clone operation. Concrete Prototype – Implements the cloning functionality. Client – Creates new objects by cloning existing prototypes. Java Implementation Step 1: Create the Prototype Class cla

📑 Table of Contents Introduction Why We Created ValidX? Why Choose ValidX? 5-Minute Quick Start Multilingual Support Important: Null/Empty String Handling Thread Safety Supported Validation Annotations Quick Reference Table Basic Validation Identity Validation Financial Validation Education/Professional Qualification Network Validation China-Specific Validation Automotive Validation Book-Related Validation Mobile Device Validation More Validation Annotations Contribution Introduction ValidX is an open-source Java validation library focused on Chinese business scenarios, making validation simple, elegant, and reliable. Built on JSR-380 standards with 90+ specialized annotations for Chinese identity cards, phone numbers, bank cards, and more. 💡 Why We Created ValidX? When developing applications for Chinese users, we frequently encountered these challenges: Pain Point 1: Java Has Too Few Built-in Validation Rules, Far Less Than Other Language Frameworks If you've used web frameworks in other languages, such as PHP's ThinkPHP or JavaScript's Validator.js, you'll notice they come with incredibly rich built-in validation rules: mobile , idcard , zip , alphaNum , etc.—ready to use out of the box, simple and convenient. But in the Java world, standard Bean Validation only provides a handful of generic annotations like @Email and @Pattern . For common Chinese business scenarios—identity cards, phone numbers, bank cards, unified social credit codes—there's absolutely no support. This forces every Java project to reinvent the wheel: Writing complex regular expressions yourself Implementing Luhn algorithm for bank card validation Handling identity card check digit calculations Copy-pasting validation code found online Why can't Java validation be as ready-to-use as other frameworks? This is why ValidX was born. Pain Point 2: Scattered Validation Logic Difficult to Maintain As projects grow, validation logic becomes scattered across: Manual validation in Controller layer Busine

Additional details about OpenAI's mysterious new AI device make it sound like a pricey smart speaker.

Researchers linked the latest malicious activity to a Chinese company, after one of the spyware's operators placed an order with KFC using their real name and office address.

A 26-year-old Canadian man once described as one of the most consequential cybercrime threat actors of 2024 has pleaded guilty to computer fraud and conspiracy to hack and extort more than 165 organizations that used the cloud data storage provider Snowflake. Connor Riley Moucka, of Kitchener, Ontario, also admitted to stealing call and text history records of more than 100 million AT&T customers.

GitHub malware advisories no longer stop at npm. Here's how we wired OpenSSF's malicious-packages data into the Advisory Database, and why we built the pipeline paranoid. The post How we took malware advisories beyond npm appeared first on The GitHub Blog .

A reverse proxy sits between clients and one or more upstream services. Instead of clients communicating directly with your application, every request first passes through the proxy before being forwarded to an upstream. Mature reverse proxies such as Nginx, Envoy, and HAProxy do much more than simply forward requests. They perform tasks such as load balancing, health checks, rate limiting, metrics collection, and much more. I wanted to better understand how some of these concepts work in practice, so I built a reverse proxy in Go. Along the way I implemented request forwarding, multiple load-balancing strategies, health checks, circuit breakers, rate limiting, request logging, metrics, and graceful shutdown. If you'd like to explore Proxify as we go, you can find the project here: https://github.com/Rahmannugar/proxify Table of Contents Request Lifecycle Project Structure Configuration Reverse Proxy Load Balancing Health Checks Circuit Breakers Middleware Graceful Shutdown Running Proxify with Docker 1. Request Lifecycle At a high level, every request follows the same path through the reverse proxy. A client sends an HTTP request to Proxify instead of communicating directly with an upstream service. Proxify receives the request, selects a healthy upstream using the configured load-balancing strategy, forwards the request, waits for the upstream's response, and finally returns that response to the client. Client │ ▼ +---------------+ | Proxify | +---------------+ │ Select Healthy Upstream │ ┌───────┴────────┐ ▼ ▼ Upstream A Upstream B │ ▼ HTTP Response │ ▼ Client Although the overall flow is straightforward, every step introduces additional considerations. Which upstream should receive the next request? What happens when an upstream becomes unhealthy? How can requests be distributed efficiently across multiple upstreams? How do we prevent a failing upstream from continuing to receive traffic? The remainder of this article answers those questions by gradually buildin

We had a checkout test at my last job that everyone called "the coin flip." Green for a week, red twice on a Tuesday, green again. Someone eventually wrapped it in a retry and it sat like that for eight months before anyone looked at it again. Turned out the real bug was a webhook that occasionally fired before the order record finished writing to the DB - a two-hundred-millisecond gap that only showed up under load. The test wasn't broken. It was the only thing in the entire pipeline that noticed. That's usually the story. Someone blames the test - bad selector, missing wait, a sleep(2) some intern left in there three years ago, and half the time they're right. But when a test flakes repeatedly and nobody can explain why, the test is rarely the actual problem. It's just the part of the system rude enough to say something. A few places I keep finding the real cause hiding. Tests that quietly depend on each other Test A writes a row, Test B reads it and never knew it needed to. Run B by itself, it passes. Run the suite in a different order, or in parallel, and B fails for no reason anyone can point to. I've lost a full afternoon to this exact thing more than once - a cache value from Test 12 leaking into Test 47. The actual fix is annoying and unglamorous: every test gets its own fixtures, its own scoped data, no assumptions about what ran before it. If your suite only goes green in one specific order, you don't have a flaky test. You have an undocumented dependency graph, and it's going to bite someone eventually. The app is racing, not the test Click a button, immediately assert on the result - that's a bet that the UI update lands the instant the click handler returns. It usually does, on your machine, on a good day. Add a debounce, a background job, or just enough network latency and that bet stops paying off. This one's frustrating because the test isn't being paranoid. The app genuinely has a race condition. The test just runs the interaction often enough, acro

For decades, desktop computers followed a familiar formula: a large case, powerful components, dedicated graphics cards, and plenty of space for upgrades. But the way we use computers is changing. Today, many users are looking for something different: a computer that is powerful enough for their daily needs, consumes less energy, takes less space, and can adapt to modern workflows. This is where Mini PCs are becoming one of the most interesting trends in personal computing. What is a Mini PC? A Mini PC is a compact computer designed to provide desktop-like functionality in a much smaller form factor. Unlike traditional desktop towers, Mini PCs integrate most components into a small chassis while still offering modern performance. A typical Mini PC includes: Modern processors from AMD or Intel Integrated Radeon or Intel graphics RAM and SSD storage Multiple connectivity options Compact cooling solutions Companies such as Minisforum have helped accelerate this trend by creating small computers powered by modern Ryzen and Intel processors, showing that compact hardware can still deliver impressive performance. Why are Mini PCs becoming popular? Efficiency matters more than ever One of the biggest advantages of Mini PCs is their efficiency. Traditional desktop computers can require significant power depending on the hardware configuration. In comparison, many Mini PCs provide enough performance for everyday tasks while maintaining lower energy consumption. For many users, reducing power usage without sacrificing productivity is becoming increasingly important. Small computers, new possibilities A smaller computer changes how we think about desktop setups. Mini PCs can be used for: Software development environments Home servers Media centers Student workstations Office computers Compact gaming setups A powerful computer no longer needs to occupy a large space on or under your desk. Modern processors changed the game The biggest reason Mini PCs are becoming more capable i

On July 27, 2026, the Caribbean Development Bank announced that it had approved a US$100,000 grant to the CARICOM Private Sector Organization to support the first phase of a study examining the feasibility and possible design of a regional stock exchange for participating states of the CARICOM Single Market and Economy. Together, the Caribbean Development Bank and the Inter-American Development Bank are contributing US$324,700 towards Phase I. [1] The proposed study will examine market demand, legal and regulatory requirements, international exchange models and the needs of public- and private-sector stakeholders. It will also consider how regional capital markets could become more connected, improve liquidity, lower financing costs and expand access to capital for Caribbean businesses. [1] These are important economic goals. However, achieving them would depend heavily on the technology supporting the exchange. More Than a Trading Website When people hear the term “stock exchange”, many may picture a website displaying company names, share prices and complex charts. This mental image is, by no means, incorrect, but it admittable fails to grasp the complex financial infrastructure that must be put in place to support a proper exchange. Behind the website with the complex charts, lies systems which process orders, match buyers to sellers, record and broadcasts trades, protect investor information and maintain an accurate history of every market. The birth of a regional exchange would require a great deal of thought, since it would need to operate across multiple Caribbean jurisdictions. Investors in Guyana, Jamaica, Barbados, Trinidad and Tobago and other participating states should be able to interact with the same market without the barrier of geography. This would require several closely connected systems, including: A high-performance order-matching engine Secure investor and broker portals Real-time market-data services Trade clearing and settlement infrastructu

For nearly two years, researcher Vangelis Stykas has maintained access to North Korean hackers’ servers. His work shows they pulled off intrusions in a shocking number of systems across the globe.

🎯 What a “Stacked PR” Is (and Why You’ll Want One) A stacked pull request (sometimes called a stacked PR , stacked diff , or dependent PR ) is a series of PRs that build on top of each other, each one containing a small, logically‑isolated change. main ──► A ──► B ──► C │ │ │ │ │ └─ PR‑C (depends on B) │ └─ PR‑B (depends on A) └─ PR‑A (directly on main) A is based on main . B is based on A (its head). C is based on B , etc. When you eventually merge the stack in order (A → B → C), each change lands cleanly, and reviewers can focus on one cohesive piece at a time. Why Stack PRs? Problem Stacked PR Solution Huge, monolithic PRs that are hard to review & cause long CI times Break the work into bite‑size PRs (e.g., “feature flag”, “data model”, “UI”) Inter‑dependent changes (e.g., a new API + its consumer) Each dependent change lives in its own PR, but they still get tested together because they are built on top of each other Rebasing on main constantly drags in unrelated changes Only the bottom PR needs to be rebased onto main ; the rest stay on top of it Need to ship part of a larger change early Merge the first PR in the stack; the rest stay pending until they’re ready CI resources Only the bottom PR runs the full suite against main ; higher PRs can run a lighter subset because they already passed lower‑level tests 📦 The Landscape of Tools (as of 2026) Tool / Service Key Features Installation / Setup Typical Workflow ghstack (GitHub CLI plugin) - Creates stacked PRs automatically from a series of commits. - Handles base‑branch updates, resolves merge conflicts, and can re‑stack after rebases. - Works with GitHub's GraphQL API, so you get “dependent PR” links in the UI. pip install ghstack (or brew install ghstack ). Requires a personal access token with repo scope. bash git checkout -b feature/stacked\n# create many commits …\nghstack push\n# later, after rebasing on main\nghstack rebase . | | GitTown (aka git-town ) | - git town ship can ship a stack of dependent br

A Deep Dive into the Memory Model From Source Code to Machine Instructions A five-part journey through compilers, executables, virtual memory, and the CPU Introduction: What Really Happens When Code Runs Consider a simple C program: include <stdio.h> int value = 10; int add(int a, int b) { return a + b; } int main() { int x = 5; int result = add(x, value); printf("%d", result); return 0; } Most programmers look at this and see only the visible outcome: 5 + 10 = 15 But behind that single printed number lies a much deeper story. Where does the data actually live? Who moves it from one place to another? How does the CPU find the instructions it needs to run? And how does the result finally make its way to the screen? Answering these questions means understanding a concept that many programmers use daily but rarely examine closely: the memory model. What Is a Memory Model, Really? Ask most developers what a "memory model" means, and the answer usually comes back in two words: stack and heap. That answer isn't wrong - it's just incomplete. A memory model is really a description of five things at once: How data is stored How data is accessed How long data exists Who is responsible for managing that lifetime How different parts of a system communicate through memory A program never leaps directly from C source code into RAM. Several distinct layers sit between the two, each one translating the layer below it into something the layer above can reason about. This article walks through all of them, one at a time, and then reassembles the full picture. The Four Layers, at a Glance Layer What It Deals With Typical Concepts 1. Programming Language Human-readable code scope, lifetime, ownership 2. Compiler Translating code to instructions registers, optimization, assembly 3. Operating System Running the program as a process virtual address space, .text/.data/.bss 4. CPU Architecture Executing raw instructions registers, cache, pipeline, ALU The rest of this article follows a sing

"A Trie isn't designed to store words. It's designed to make finding everything that shares the same beginning incredibly efficient." In the previous article, we explored a different kind of software problem. Some systems don't search using complete values. Instead, users provide only part of the information they know. The system must immediately suggest possible matches. Once you recognize that requirement, another question naturally follows. How should the system organize data so prefix searches become fast and natural? This is exactly the problem a Trie solves. Think About a Dictionary Imagine opening a physical dictionary. Suppose you're looking for the word: Application Do you start reading from page one? Of course not. You first go to the words beginning with: A Then you narrow further. Ap Then: App Every additional letter reduces the search space. A Trie works in a very similar way. Instead of repeatedly searching through every word, it follows the characters one by one. What Is a Trie? A Trie is a tree-like data structure where each node represents a character. Words that begin with the same characters share the same path. Consider these words. car card care cart A Trie stores them like this. Root ↓ c ↓ a ↓ r ├── end ├── d → end ├── e → end └── t → end Notice something interesting. The prefix: car is stored only once. Every longer word simply continues from that shared path. Every Data Structure Answers a Different Question By now we've seen several data structures, each solving a different design problem. A HashMap asks: Where is this exact object? A Heap asks: Which item has the highest priority? A Queue asks: Which task should happen next? A Stack asks: What is the current working context? A Trie asks: What begins with these characters? Choosing the right data structure starts with identifying which question your software needs to answer. Inserting a Word Imagine inserting: cat The Trie creates a path. Root ↓ c ↓ a ↓ t Now insert: car The beginning alread

Anthropic is building a team for designing its own custom AI chips. The Claude maker said it would co-design hardware and models to help its technology run faster and more efficiently.