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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.

Wispr Flow, a popular dictation tool, has released a live notetaker that transcribes and summarizes meetings. It joins a growing wave of AI notetakers for the workplace.

Uploading a PDF and calling a translation model looks like a two-step feature. In production, it is a job pipeline with untrusted input, two different extraction paths, several expensive stages, and an output that can be fluent while still being wrong. That distinction matters for a small SaaS team. The translation request may come from support, sales, or an internal operations task. Nobody wants to operate a document platform, but the workflow still needs to answer basic questions: Was the upload actually a PDF? Does the file contain selectable text or scanned page images? Can a retry create a second charge or a conflicting result? What happens when page 37 fails after the first 36 pages succeed? How do we know the translated PDF is not blank or visually broken? When are the source and result deleted? The translation model is one component. Reliability comes from the system around it. Define the Job Contract First I would not let a file reach an extractor until the API has established a narrow contract. For example, a translation request might include: type TranslationStyle = " general " | " technical " | " academic " ; interface CreateTranslationJob { uploadId : string ; sourceLanguage : string | " auto " ; targetLanguage : string ; style : TranslationStyle ; idempotencyKey : string ; containsRestrictedData : boolean ; } The request should be rejected when the source and target languages are identical, the upload is missing, the target language is unsupported, or policy says the document cannot leave an approved environment. File validation should also be explicit. Do not trust the filename or browser-supplied MIME type. Check at least: the actual byte size; the file signature; whether the parser can open the document; whether the PDF is encrypted; the page count; whether the job fits the account or product limit. A 20 MB limit is simple to explain in a user interface, but size alone is not a good predictor of work. A compressed 200-page text PDF can be smaller th

AWS introduced Kiro Crew on Tuesday as a new open-source orchestration platform. This tool aims to help businesses shift from interactive AI coding assistants toward autonomous engineering workflows. The system manages tasks across various repositories and developer tools over multiple work sessions to increase overall efficiency. Orchestrating autonomous development cycles Kiro Crew goes beyond simple code generation by coordinating multiple AI agents simultaneously. It schedules recurring work and maintains project context even when a session ends. This allows the system to integrate with standard developer tools for investigating incidents or monitoring pull requests. It triages tickets and automates software engineering tasks while developers are away from their workstations. The platform functions as an application layer that turns AI coding agents into self-learning teammates. It features persistent memory and multi-agent orchestration tools to ensure continuity. Security remains a priority with features like sandboxing and signed audit logs. Users can monitor activity through a dedicated web and desktop dashboard designed for transparency. Before its public release, the project existed inside Amazon as an internal tool named MeshClaw. More than 39,000 Amazon builders adopted it in less than six months. This internal success paved the way for the current open-source offering. Companies can deploy the platform entirely within their own environments, such as on local laptops or virtual machines. Reference applications and practical use cases AWS launched several reference applications to show how the platform functions in real-world scenarios. DevFleets manages worktrees, while Issue Radar handles the triage of pull requests and tickets. Task Runner focuses on executing engineering tasks that require a long duration to complete. These apps use specific interfaces combined with the core orchestration engine. These tools are not standalone products but rather exam

I built a free tool that checks a supplier before you pay them. The part that took most of the work, and taught me most, was reading ten national company registers instead of relying on the EU's own VIES service. This is what I found out, mostly so the next person doesn't have to. The problem with "the VAT number is valid" VIES — the European Commission's VAT Information Exchange System — answers one question: is this VAT number currently registered. That sounds like the question you want answered. It isn't. A company that has gone into liquidation keeps a cleanly resolving VAT number in VIES. So does one that has been struck off the register. Deregistration and insolvency are run by different authorities on different timetables, and the gap between "this company has stopped being a going concern" and "the VAT number stops validating" can be months. So you can check a supplier, get a green tick, and be looking at an insolvency estate. The national registers know. VIES doesn't ask them. Ten registers, and what each actually gives you I found free, public, machine-readable-enough sources for ten countries: Bulgaria, Czechia, Estonia, Finland, France, Greece, Latvia, Poland, Romania and Slovenia. They are not equivalent, and this is the thing I'd have liked written down somewhere before I started: Six of them report company *state * — inactive, in liquidation, bankrupt, insolvent, terminated, ceased, struck off: Romania, Estonia, France, Greece, Bulgaria, Latvia. This is the valuable one. Three report whether the company is actually VAT-active — Poland, Romania, Slovenia. That matters more than it sounds, because VIES does not distinguish "this is a real company that isn't VAT-registered" from "this number belongs to nobody". The rest give you a name and not much more. Czechia, for instance, is in the ten but in neither of the other two groups. It confirms a name. That's it. Worth knowing before you build a feature around it. Poland is the interesting one Poland is the

so I shipped a voice AI assistant that runs entirely on my machine. no cloud, no APIs, no latency nightmares. the original idea came from Alice in Sword Art Online — an AI that feels like an actual person, not a chatbot. mixed with JARVIS's anticipation and Neuro-sama's quirky personality. here's what actually went into getting from "wouldn't it be cool" to "this runs 24/7 without issues." the problem with voice AI most voice assistants are cloud-first: you speak → sent to server → processed → response → back to you. each hop adds latency. you're looking at 3-6 seconds before you hear anything. for a voice interaction, that's dead. it kills the feeling of talking to something intelligent. I wanted something faster. something that responds . the constraint: do it locally. use an 8GB VRAM GPU, run everything on-device, no external APIs except for the live2d avatar bits (because that's hard to render locally and still look good). the latency wall here's the reality: I have a GPU with 8GB VRAM. no budget to experiment with better cards or more models. so every architecture decision was forced by what actually fits. naive approach: chain multiple specialized models. User speaks → Transcription model (Whisper) → Planning model (3B: what should I do?) → Coding model (7B: generate implementation) → Verification model (4B: is this correct?) → TTS (speak the answer) math: 1s + 2s + 3s + 1.5s = 7.5s of latency before the user hears anything. nope. the problem isn't just that each model is slow. it's model loading overhead . every time you swap from one model to another, you: unload model A from VRAM load model B into VRAM stall while the GPU rearranges memory with only 8GB, this gets gnarly fast. the decision: one unified model the constraint was hardware. 8GB VRAM. no more, no less. that forced clarity: pick one model that does everything, or pick nothing. so I went with a single model (4B by default, with 7B/8B quality modes available) that does reasoning + code generation +

Quotient CEO Lizzie Matusov explains why soaring AI spend often fails to improve software delivery. She presents a research-backed AI maturity framework designed to help engineering leaders move beyond vanity metrics like token usage, align organizational AI adoption, and address critical bottlenecks across the software development life cycle to deliver measurable business outcomes. By Lizzie Matusov

When you start as a junior developer, you think software engineering is about writing code. A few years in, you think it's about choosing the right architecture and frameworks. After ten-plus years in the trenches - shipping features, surviving on-call disasters, and watching "perfect" codebases turn into unmaintainable monsters - you realize the truth: Software engineering is mostly about managing complexity, human communication, and trade-offs. Here are 25 mistakes I made, witnessed, or had to clean up over the past decade. Hopefully, reading them saves you a few years of painful trial and error. 1. Code & Architecture 1. Abstracting Too Early The DRY (Don't Repeat Yourself) principle is heavily drilled into beginners, but premature abstraction is far worse than duplicate code. Abstracting before you have 3–4 concrete use cases leads to rigid, over-engineered abstractions that are nightmare-inducing to change. Duplication is far cheaper than the wrong abstraction. 2. Falling in Love with "Clever" Code If your code requires a three-minute internal monologue or a complex diagram just to parse a single line, it's not smart - it's a liability. Write obvious, clear, and boring code. Your future self on a 2 AM incident response call will thank you. 3. Misunderstanding the Cost of Dependencies Adding a third-party library to solve a small problem feels like a quick win. In reality, every dependency is a contract you sign with an external team. You inherit their bugs, security vulnerabilities, breaking updates, and maintenance cycles. Ask yourself: Can we build the 5% of this library we actually need in 20 lines of code? 4. Over-Architecting for Scale You Don't Have Designing a system for 10 million daily active users when you currently have 500 is a classic trap. You end up with distributed microservices, message queues, and complex caching strategies that slow down development speed by 10x. Build for today's scale, but keep the boundary clean enough to refactor tomorrow

"A Stack isn't designed to store data. It's designed to make the most recent piece of work the easiest to access." In the previous article, we discovered a new kind of design problem. Some systems don't need to find the fastest item. Some don't need to process tasks in arrival order. Instead, they need to work with whatever happened most recently . That's exactly the problem a Stack solves. In this article, we'll understand how a Stack works and why its behavior appears naturally in many software systems. Imagine a Stack of Plates Think about a stack of dinner plates. Plate 4 ────────── Plate 3 ────────── Plate 2 ────────── Plate 1 ────────── When you need a plate, which one do you take? The one on the top. You don't pull out the bottom plate. Likewise, when placing a new plate, you put it on top. This simple rule defines the behavior of a Stack. What Is a Stack? A Stack is a data structure where both insertion and removal happen from the same end. The last item added is always the first one removed. This behavior is called LIFO (Last In, First Out). Push A ↓ Push B ↓ Push C ↓ Pop ↓ C Notice something important. A Stack isn't trying to preserve arrival order like a Queue. Instead, it preserves recency . The newest item is always the easiest to access. Every Data Structure Solves a Different Design Problem By now, we've seen several data structures, each answering a different question. A HashMap asks: Where is this object? A Heap asks: Which item has the highest priority? A Queue asks: Which task has been waiting the longest? A Stack asks: What happened most recently? Choosing the right data structure begins with identifying which of these questions your system needs to answer. Push and Pop Stacks are built around two simple operations. Push Adding a new item. Before Top ↓ B ↓ A Push C After Top ↓ C ↓ B ↓ A Pop Removing the most recent item. Before Top ↓ C ↓ B ↓ A Pop After Top ↓ B ↓ A Only the top item is removed. Everything below remains untouched. Real-World Examp

Introduction Artificial Intelligence has evolved far beyond answering questions and generating code. Modern AI systems can search databases, interact with APIs, read files, execute commands, access cloud services, and even coordinate multiple tools to complete complex tasks. This shift has given rise to AI agents - systems that don't just generate responses but can actively perform work on behalf of users. However, enabling an AI model to interact with external tools introduces a challenge. Every application, service, and API exposes its capabilities differently. Without a common standard, every AI platform would need custom integrations for every tool it wanted to support. This is where the Model Context Protocol (MCP) comes in. MCP provides a standard way for AI models to discover, understand, and use external tools, data sources, and services. Instead of building separate integrations for each AI model and every application, developers can expose capabilities through a common protocol that different AI clients can understand. In this article, we'll explore what MCP is, why it matters, how it works, and how it's changing the way developers build AI-powered applications. The Problem Before MCP Imagine you're building an AI assistant that needs to interact with: GitHub Slack Google Drive PostgreSQL Jira Notion Local files Internal company APIs Without a shared protocol, every integration becomes a custom implementation. For each tool, you need to define: Authentication API endpoints Request formats Response parsing Error handling Documentation Now imagine supporting multiple AI models. Every model may require different integration logic, increasing development effort and maintenance costs. This creates unnecessary complexity. What Is MCP? At its core, the Model Context Protocol (MCP) is a communication standard between AI models and external systems. Instead of hardcoding every integration, MCP defines a consistent way for an AI client to: Discover available tools U

Author: Skydu Summary: A form engine may look like the most basic capability in a low-code platform, but it is really the entry point for business modeling, data structure, permissions, workflows, and future AI understanding. Opening In the previous post, I wrote about why INFORMAT is not meant to be only a low-code tool. Starting from this post, I want to go into specific modules. The first module I want to write about is the form engine. The reason is simple: in a low-code platform, forms look basic, but a form is not just a page. Many enterprise business systems begin with a form. Customer registration, contract approval, project initiation, purchase requests, inventory receiving, equipment inspections, and production reporting are all, at their core, ways to collect, organize, and move business data. So a form engine is not about dragging a few input boxes onto a canvas. It is the entry point for the platform's business modeling capability. The initial requirement looked simple Before building the form engine, my most straightforward idea was this: users should be able to create business forms, configure fields, and let the system automatically generate data-entry pages and data lists. That idea does not sound complicated. A form name, a group of fields, a save button, and a data list seem like enough. But once implementation begins, a series of questions appear quickly. What field types should exist? Can fields be grouped? Can fields depend on each other? Should data be validated? Should a workflow be triggered after submission? Can different people see different fields? How will form data be used by reports, automation, and AI? When these questions stack together, the form engine stops being only a frontend component. It becomes a core module that connects the data model, permission system, workflow system, and automation system. A form is not a page, but a business model I gradually became more certain of one judgment: forms in a low-code platform should not

Modern backend applications handle thousands or even millions of requests every second. Users perform actions simultaneously: buying products, transferring money, updating profiles, sending messages, and more. But what happens when two requests try to modify the same data at the same time? This is where race conditions appear — one of the most subtle and dangerous problems in backend development. A race condition can cause incorrect data, security issues, financial losses, and unpredictable application behavior. Understanding how race conditions happen and how to prevent them is an essential skill for backend developers. What Is a Race Condition? A race condition occurs when multiple processes or requests access and modify shared data at the same time, and the final result depends on the order in which those operations execute. The problem is that the developer expects operations to happen in a specific sequence, but the computer executes them based on timing, network delays, database speed, and system load. Simple Example: Bank Account Withdrawal Imagine a user has: Account Balance: $100 Two withdrawal requests arrive at the same time: Request A: Withdraw $80 Request B: Withdraw $50 The backend checks the balance: Request A: Balance >= 80? Yes Request B: Balance >= 50? Yes Both requests continue because they saw the original balance of $100. The system processes: $100 - $80 = $20 $100 - $50 = $50 The final balance might become: $50 instead of: -$30 (which should have been rejected) The application has allowed money to be withdrawn that does not exist. This is a race condition. How Race Conditions Happen in Express.js Express.js applications are often built around asynchronous operations: Database queries API calls File operations Background jobs Message queues Consider this simple inventory system: app . post ( " /purchase " , async ( req , res ) => { const product = await Product . findById ( req . body . productId ); if ( product . stock > 0 ) { product . stock -

$100 million deal gives 50,000 Ukrainian drones US-developed AI capabilities.