标签：#Computing

Spoiler: It's more Siri stuff.

As software engineers, we often architect solutions in a virtual ideal: fast networks, elastic resources, and servers that never physically degrade. But what happens when your carefully crafted systems need to interact with the messy, unpredictable physical world? Think factory floor monitors, real estate camera networks, or remote tracking devices. Suddenly, those cloud assumptions about infinite uptime and perfect connectivity crumble. My journey, particularly architecting and maintaining a continuous 24/7 camera livestream for a real estate group over six years, has been a masterclass in this reality. It's revealed that true reliability in the physical realm demands a hybrid approach – one that intelligently merges the power of edge computing with the scalability and data insights of the cloud. This isn't just about connecting devices; it's about building resilience into the very fabric of your architecture. In this article, I'll share the battle-tested strategies and design principles that enable systems to not just survive, but thrive, despite the harsh realities of physical deployment. 1. The Core Strategy: Smart Edge, Simple Cloud One of the most common pitfalls in hybrid architecture design is treating the edge device as a mere 'dumb' terminal, solely responsible for streaming raw data to a powerful cloud backend. This approach creates a critical single point of failure: if the network drops, the entire system grinds to a halt. Instead, I advocate for a Smart Edge, Simple Cloud architecture. This principle establishes a clear division of responsibility: The Edge : This is where the magic happens locally. The edge system should be robust enough to handle local processing , data filtering , buffering , and immediate hardware control . Critically, it must be capable of operating autonomously for extended periods without an active cloud connection. Think of it as a mini data center, designed for self-sufficiency. Benefits of a Smart Edge : Reduced bandwidth cost

Snap CEO Evan Spiegel sat with us to discuss the Snap Specs, privacy, parental controls and more. Spoiler: He says "computing" a lot.

Snap describes its news AR Specs as a "wearable computer."

The update brings a low-latency profile, speeds up search, and patches hundreds of flaws.

Microsoft Office 2019 for Mac will effectively become useless next month.

If you've ever spun up an EC2 instance for a side project, accessed a remote work desktop from your personal laptop, or stored files on Google Drive without thinking about the physical hard drive it lives on, you've used virtualization. As the foundational technology behind all modern cloud computing, virtualization transformed how we build, deploy, and manage IT infrastructure—cutting hardware costs significantly for enterprises and making on-demand scalability a reality for teams of all sizes. In this guide, we'll break down exactly what virtualization is, how it powers the cloud, the 6 core types of virtualization, and best practices to implement it safely and efficiently. Table of Contents What is Virtualization in Cloud Computing? Core Virtualization Concepts You Need to Know Role of Virtualization in Cloud Computing 6 Key Types of Virtualization (With Use Cases) Top Benefits of Virtualization for Teams of All Sizes Virtualization vs. Related Technologies Virtualization vs. Cloud Computing Virtualization vs. Containerization Common Virtualization Challenges and Mitigations Real-World Virtualization Use Cases Virtualization Best Practices Conclusion References What is Virtualization in Cloud Computing? Virtualization is a technology that creates virtual, software-based representations of physical hardware (servers, storage, networks, etc.) and abstracts these resources from the underlying physical machine. A software layer called a hypervisor separates operating systems and applications from physical hardware, allowing multiple isolated, self-contained systems called Virtual Machines (VMs) to run simultaneously on a single physical host. Each VM has its own virtual CPU, memory, storage, and network interface, and operates independently of other VMs on the same host. For cloud providers, this technology is the backbone of all on-demand infrastructure services, allowing them to share physical hardware across thousands of customers securely and efficiently. Core Vi

Navigating the tech space today often feels like walking a tightrope between two extremes: massive corporate monopolies holding all the keys, and idealistic local projects trying to build everything from scratch. But this doesn't have to be an "Us vs. Corporations" battle. We don’t need to completely eliminate corporate tools; we need to leverage them. The real pragmatic goal is to use localized, decentralized data-driven systems to solve real-world physical problems on the ground, in real time. When people hear the word "decentralized," they often assume it means chaotic fragmentation, isolation, or losing control of data. It doesn't. Decentralization does not mean losing data; it means movement. In fact, the paradox of modern tech is that More Decentralized Data = Centralized Utility. 1. Moving Beyond "App Consumption" to Localized Edge Data For too long, the cultural conversation around tech has been stuck in the clouds. We talk about "the cloud" abstractly, and the average consumer's tech vocabulary is limited to a handful of corporate app names. True tech pragmatism brings data collection back down to earth, turning communities from passive consumers into active, node-operating contributors. Here is what that looks like in practice: Hyper-Local Climate Grids: Instead of teaching students about weather patterns using generic data from an airport weather station 50 miles away, a school can deploy its own low-cost local weather station. Students learn from their immediate microclimate, and that real-time local data is fed back into a wider community grid. Optimized Infrastructure: Instead of spending millions on speculative traffic studies, we can use existing, low-cost edge cameras to count traffic patterns locally. This decentralized edge data tells planners exactly what kind of infrastructure—like traffic lights (or "robots" as we call them here) or bypass lanes—a specific zone actually needs. It is planning based on true utility, not guesswork. The Energy Grid

If you are just getting started with Azure CLI and Bash scripting, this post is for you. I will walk you through how I automated the creation of Azure resource groups for multiple environments using a single Bash script — something that was taking a cloud admin several manual steps every week. This is Project 2 in my TechRush Cloud Engineering bootcamp series. If you want to see where this journey started, you can read my previous post where I tackled deploying a web app across two Azure regions for the first time . That project involved real blockers — quota limits, CLI version mismatches, and a deep dive into Azure Resource Providers. This one went smoother, and I think that is because the previous project was the hard school. The Problem Imagine a cloud administrator who has to create five resource groups every single week, one for each active project: Project-A-RG Project-B-RG Project-C-RG Project-D-RG Project-E-RG Every week. By hand. Management's response was simple: automate it. But here is where the task gets more interesting. Instead of creating one flat resource group per project, the better approach is to create four resource groups per project — one for each environment: Dev Test UAT Production This matters because each environment needs its own access controls, cost tracking, and lifecycle rules. You do not want your Development environment sharing a resource group with Production. Keeping them separate is a real-world cloud best practice, not just a bootcamp exercise. What You Will Need Before running this script, make sure you have the following set up: Azure CLI installed on your local machine. You can follow the official installation guide . An active Azure account . A free account works fine for this. A terminal that runs Bash — Linux, macOS, or WSL on Windows. Understanding the Design The core idea behind this script is parameterization . Instead of hardcoding project names, the script accepts a project name as input and uses it as a prefix for ev

Microsoft announced the general availability of Microsoft Discovery, its Azure-based platform for deploying autonomous AI agent teams in scientific R&D. The platform powered the development of Majorana 2, a topological quantum chip with 1,000x reliability improvement and 20-second qubit lifetimes. Microsoft now targets a scalable quantum computer by 2029, halving its original timeline. By Steef-Jan Wiggers

LiteRT-LM brings native support for Gemma 4 Multi-Token Prediction (MTP) drafters, enabling up to 2.2x faster inference. The framework is expanding beyond Kotlin and C++ adding support for new Swift and a JavaScript APIs. By Sergio De Simone

Qisquiz: A Qiskit v2.X Certification Prep App I built Qisquiz , a web app for learning Qiskit v2.X and preparing for the IBM Certified Quantum Computation using Qiskit v2.X Developer - Associate certification exam. You can try the app here: https://qisquiz.vercel.app/ The GitHub repository is here: https://github.com/dorakingx/qisquiz The concept of Qisquiz is simple: Master Qiskit, one quiz at a time. In other words, Qisquiz is a quiz-based certification prep app that helps learners study Qiskit one question at a time. The target exam is: Exam C1000-179: Fundamentals of Quantum Computing Using Qiskit v2.X Developer Why I Built Qisquiz Qiskit is one of the most important development tools for learning and building quantum computing applications. It is useful for creating quantum circuits, running simulations, using IBM Quantum hardware, and experimenting with quantum algorithms. However, Qiskit v2.X includes several APIs and concepts that learners need to understand carefully. For example, certification prep requires knowledge of topics such as: Qiskit Runtime SamplerV2 EstimatorV2 PUBs, or Primitive Unified Blocs BackendV2 backend.target Transpilation ISA circuits Dynamic circuits OpenQASM 3 Result object handling Little-endian and big-endian interpretation These topics can be learned by reading documentation, but I felt that active practice through quizzes is especially useful for exam preparation. That is why I built Qisquiz , a quiz-based learning app focused on Qiskit v2.X. What Is Qisquiz? Qisquiz is an independent quiz-based learning app for Qiskit v2.X. The current version is organized around the 8 sections of the IBM Qiskit v2.X Developer certification exam. The current question bank includes: 120 original questions 44 code-based questions 40 hard questions 8 sections 15 questions per section Qisquiz is not an official IBM or Qiskit product. It is an independent learning tool that I built to help myself and other learners prepare more effectively. Covered E

Quantinuum, a quantum computing startup, is losing millions. Investors want in anyway.

Some quantum computing companies we've covered have done recent progress updates.

The National Center for Atmospheric Research won't be losing its supercomputer.

The company demoed Solara on an Echo Show-style smart display and a smart key badge.

Intel bet big on its new Series 3 chips, made on its new 18A process. But can it get enough of those chips out of the door to laptop manufacturers?

Meta-Optimized Continual Adaptation for coastal climate resilience planning with zero-trust governance guarantees It started with a nagging feeling of inadequacy. I was deep into a research project on adaptive AI for infrastructure planning, studying how reinforcement learning agents could optimize sea-wall placements and evacuation routes. The models worked—beautifully, in fact—on static datasets. But the moment I fed them real-time satellite imagery of a rapidly eroding coastline or a sudden storm surge, they stumbled. They forgot previous strategies, overfit to the new event, or, worse, made decisions that violated basic safety constraints. I realized then that the problem wasn't just about better AI; it was about trust and adaptation in the face of chaos. My exploration of this challenge led me down a rabbit hole of meta-learning, continual learning, and cryptographic governance. What emerged was a framework I now call Meta-Optimized Continual Adaptation (MOCA) with zero-trust governance guarantees—a system designed not just to learn, but to learn how to learn in dynamic, high-stakes coastal environments, all while ensuring that every decision is auditable and tamper-proof. This article shares that journey, the technical breakthroughs, and the hard-won lessons from my experiments. Technical Background: The Three Pillars of MOCA The core insight behind MOCA is that coastal climate resilience planning requires three seemingly contradictory properties: Continual adaptation – The system must update its models as new data streams in (e.g., sea-level rise, storm frequency, erosion patterns) without catastrophic forgetting. Meta-optimization – It must learn the learning algorithm itself, so that adaptation becomes faster and more sample-efficient over time. Zero-trust governance – Every model update and decision must be cryptographically verifiable, with no single point of failure or authority. In my research, I found that existing approaches tackled these individually

ASUS revealed new convertible Windows laptops and three Zenbook 14 models with ARM64 and x86 processors.

NVIDIA claims the new RTX Spark chip for PCs will offer 1 petaflop of AI computing power.