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Suppose leadership rewards teams for increasing the percentage of “AI-assisted pull requests.” The dashboard rises. Did productivity improve, or did people learn which box to tick? Before launching that metric, I would run a consequence-mapping session: Intended behavior Plausible adaptation Counter-metric try useful assistance label trivial PRs as assisted retained task outcome ship faster split work into tiny PRs lead time per task share adoption avoid difficult non-AI work task-mix distribution accept suggestions reduce review scrutiny rollback and defect rate The metric card should make disagreement possible: name : ai_assisted_pr_share purpose : detect workflow adoption, not productivity owner : developer-experience known_game : self-label inflation counter_metrics : [ task_mix , review_minutes , rollback_rate ] review_date : 2026-08-19 retire_when : classification cannot be audited Then interview both high and low scorers without treating the score as performance. Ask what work disappeared, what new verification appeared, and what behavior the dashboard encouraged. Include an anonymous channel: a metric cannot reveal pressure if challenging it carries career risk. The SPACE framework argues that developer productivity cannot be captured by one dimension. That is especially relevant when AI telemetry is easy to count but verification and rework are harder to observe. My launch gate is not “the metric is accurate.” It is: teams can inspect its definition, challenge its interpretation, and show where it changes behavior. If the counter-metrics diverge, pause incentives before refining the chart. What behavior would your current AI dashboard accidentally reward?

24 hours. That's how long a contract lasted before someone pulled it because I was running Windows,...

As someone who studied stats undergrad and industrial engineering operations research grad, and who thinks about the practical business of ML components in software.... I get lost and a bit hopeless when I think about how to make useful software out of ML models in a reasonable amount of time, and in the current business environment. And when I look at the businesses where I have worked that have mountains of middle management running tiny bits of the ML model lifecycle (think feature extraction, data ingestion and integration, training infra, hosting infra, more hosting infra, applied science)... that only makes my head hurt even more. How do you go about making practical software out of ML components? Edit: I should mention that I mean from scratch ML components, not just a call to a third party hosted tool. submitted by /u/ConstructionBoth6461 [link] [留言]

https://mdooai.com AI 모델을 다루다 보면 코드는 돌아가는데 왜 성능이 안 나오는지 이해하지 못하는 순간이 옵니다. 그 답은 대부분 수학에 있습니다. 실무 AI 개발자로 성장하기 위해 반드시 잡아야 할 최소한의 수학 개념과, 그것을 어떻게 공부해야 하는지를 구체적으로 살펴보겠습니다. 코딩만으로는 해결할 수 없는 AI 모델 성능의 열쇠 많은 주니어 개발자들이 비슷한 경험을 합니다. PyTorch나 TensorFlow로 튜토리얼 코드를 복사하고, 학습도 돌리고, 결과도 나옵니다. 그런데 모델이 왜 이렇게 예측하는지, 왜 학습이 멈추는지, 어떻게 하면 성능이 올라가는지는 설명할 수가 없습니다. 이 간극의 정체가 바로 수학입니다. 가령 모델이 과적합(overfitting)될 때 L2 정규화를 쓰라는 조언을 듣습니다. 코드 한 줄로 해결되지만, 그게 왜 작동하는지는 가중치 벡터의 크기를 제한한다는 선형대수와 확률론적 추론이 얽혀 있습니다. 이 원리를 이해하는 개발자와 모르는 개발자가 새로운 문제에서 만들어내는 솔루션의 질은 다를 수밖에 없습니다. 다행인 건, AI 개발에서 필요한 수학은 대학원 수준의 수리해석학이 아닙니다. 핵심 개념 몇 가지를 실용적 도구로 이해하는 것으로 충분합니다. 선형대수와 확률통계, 어떤 개념부터 공부해야 할까? 선형대수와 확률통계는 AI 수학의 두 축입니다. 그런데 이 두 분야 전체를 공부하려 들면 끝이 없습니다. 개발자 관점에서 실제로 반복해서 등장하는 개념만 추려내면 다음과 같습니다. 선형대수에서 우선해야 할 개념 벡터와 행렬의 연산 : 데이터는 사실상 전부 벡터와 행렬로 표현됩니다. 이미지는 픽셀값 행렬이고, 텍스트는 임베딩 벡터입니다. 행렬 곱(matrix multiplication)이 신경망의 순전파(forward pass) 그 자체입니다. 내적(dot product)과 유사도 : 추천 시스템, 어텐션 메커니즘(Transformer의 핵심)이 내적 연산 위에 서 있습니다. 고유값(eigenvalue)과 고유벡터(eigenvector) : PCA(주성분 분석)처럼 차원을 줄이는 기법을 이해하려면 반드시 필요합니다. 행렬 분해(SVD 등) : 추천 시스템과 자연어 처리의 기반 기술에 등장합니다. 확률통계에서 우선해야 할 개념 확률분포와 기댓값 : 모델의 출력이 확률인 이유, 소프트맥스(softmax)가 하는 일을 이해하는 기반입니다. 베이즈 정리 : 사전 지식을 데이터로 업데이트하는 논리 구조로, 생성 모델과 불확실성 추정에 직접 연결됩니다. 최대 우도 추정(MLE, Maximum Likelihood Estimation) : 모델 학습이 왜 손실 함수를 최소화하는 방향인지를 설명하는 원리입니다. 크로스 엔트로피 손실 : 분류 모델에서 가장 자주 쓰이는 손실 함수로, 확률론과 정보이론이 만나는 지점입니다. 분야 핵심 개념 AI 실무 연결 지점 선형대수 행렬 곱, 내적 신경망 순전파, 어텐션 선형대수 고유값·고유벡터 PCA, 차원 축소 선형대수 행렬 분해(SVD) 추천 시스템, NLP 확률통계 확률분포, 기댓값 소프트맥스, 분류 출력 확률통계 베이즈 정리 생성 모델, 불확실성 추정 확률통계 최대 우도 추정 손실 함수의 논리적 근거 확률통계 크로스 엔트로피 분류 모델 학습 활성화 함수와 역전파에서 미적분은 어떻게 작동하는가? 미적분은 신경망 학습의 핵심 메커니즘인 역전파(backpropagation)를 이해하는 데 필요합니다. 겁먹을 필요는 없습니다. AI 개발에서 실제로 필요한 미적분 개념은 크게 두 가지입니다. 1. 편미분과 기울기(gradient) 손실 함수는 모델의 가중치를 변수로 가지는 다변수 함수입니다. 학습이란 이 함수의 값을 줄이는 방향으로 가중치를 조금씩 조정하는 과정입니다. 이 '방향'을 계산하는 도구가 편미분이고, 모든 가중치에 대한 편미분을 모아놓은 것이 기울기(gradient)입니다. 경사하강법(gradient descent)은 이 기울기의 반대 방향으로 가중치를 업데이트합니다. 수식으로는 다음과 같습니다. 새 가중치 = 기존 가중치 − 학습률(lr) × 기

Last week I was reviewing the staking engine of a protocol before its mainnet launch. Deep in a 1,400-line contract, I found what looked like a serious bug. The reward math multiplied three values before dividing: uint256 delta = (lot.amount * rBase * midpointRate) / (RAY * RAY); Multiply-before-divide. If that intermediate product overflows uint256 , the whole epoch settlement reverts — and since every stake , withdraw , and setStake runs it, the post's funds get permanently frozen . That's a High-severity, fund-locking DoS. I had the finding half-written. lot.amount can reach 10M tokens ( 1e25 ). rBase grows with elapsed time. midpointRate can hit RAY . Multiply those and you blow past 2^256 ... I was ready to send it. Then I did the one thing that separates a real audit from false-positive spam: I checked the actual constants before writing the claim. uint256 private constant RAY = 1e18; // I'd assumed 1e27 RAY was 1e18 , not the 1e27 I'd been carrying in my head. And the interest rate had a hard cap — MAX_RATE_MAX_RAY = 5e18 , enforced even against a fully-captured timelock. I ran the numbers with the real values: For that multiplication to overflow, the protocol would need to go 2.3 × 10¹⁵ years without a single state update. Not reachable. The bug didn't exist. I deleted the finding. Why this matters more than the bug would have If I'd sent that report, here's what happens: the team's engineer clones the repo, plugs in the real constants, and realizes in ten minutes that I flagged an overflow that can't happen. Every other finding in my report now gets read with a raised eyebrow. My credibility — the entire product — is gone. This is the dirty secret of automated smart-contract auditing: the bottleneck isn't finding issues. It's not drowning the real ones in false positives. Anyone can run a scanner and paste 40 "criticals." A team that has to triage 40 flags to find the 2 that matter will — correctly — stop trusting you. The bar I hold: zero false positives o

.The V8 engine speeds up JavaScript by dynamically compiling frequently run bytecode into optimized native machine code. However, if you pass inconsistent argument types to these optimized functions, V8 panics and deoptimizes back to bytecode. Keeping your functions monomorphic (single-typed) prevents this costly deoptimization loop, ensuring maximum runtime execution speed. If you’ve spent as much time digging into V8 execution flags as I have, you quickly realize that JavaScript is constantly rewriting itself under the hood. We like to think of JavaScript as a dynamically typed scripting language. But at runtime, engines like V8 are working tirelessly to turn your code into a highly optimized, statically typed powerhouse. When we violate that type stability, we pay a massive performance tax. How does the V8 engine optimize JavaScript at runtime? V8 uses a multi-tiered compilation pipeline that starts with an interpreter for fast startup times, then upgrades hot functions to optimized machine code using a JIT compiler. By tracking runtime type patterns, the engine can safely make assumptions to skip expensive dynamic lookups. When I look at V8’s execution pipeline, I see two primary systems working in tandem: Ignition (the interpreter) and TurboFan (the JIT compiler). Initially, Ignition compiles your raw JavaScript into bytecode so your app can boot instantly. As this bytecode executes, V8 allocates a data structure called a Feedback Vector for each function. Inside this vector are Feedback Slots (managed by Inline Caches, or ICs). These slots act as recorders, capturing the exact types (or "shapes") of the variables passing through your code. Once a function runs frequently enough to cross an execution threshold, V8 marks it as "hot" and hands it to TurboFan. TurboFan reads those feedback slots, assumes the types will remain identical in the future, and compiles a highly streamlined, native machine code version of that function. What happens when you pass differe

Modern web applications demand responsive, non-blocking user flows. This tutorial demonstrates a production-grade implementation for handling form submissions without full-page reloads using the native Fetch API and clean async/await syntax. The Complete Source Code Create a file named app.js and drop in the following event-driven architecture: JavaScript document.getElementById('registrationForm').addEventListener('submit', async (event) => { event.preventDefault(); // 1. Stop full page reload const form = event.target; const formData = new FormData(form); const submitBtn = form.querySelector('button[type="submit"]'); const responseMessage = document.getElementById('responseMessage'); // 2. UI Feedback: Disable button during network request submitBtn.disabled = true; submitBtn.textContent = 'Processing...'; responseMessage.textContent = ''; try { // 3. Asynchronous Fetch Request const response = await fetch(form.action, { method: 'POST', body: formData, headers: { 'X-Requested-With': 'XMLHttpRequest' } }); // 4. Status Code Validation if (!response.ok) { throw new Error(`HTTP error! Status: ${response.status}`); } const result = await response.json(); // 5. Dynamic UI State Handling if (result.success) { responseMessage.style.color = '#155724'; responseMessage.textContent = result.message; form.reset(); // Clear form on success } else { responseMessage.style.color = '#721c24'; responseMessage.textContent = result.error || 'Submission failed.'; } } catch (error) { // 6. Global Error Catching responseMessage.style.color = '#721c24'; responseMessage.textContent = 'A network error occurred. Please try again.'; console.error('Submission tracking error:', error); } finally { // 7. Reset UI State Guaranteed submitBtn.disabled = false; submitBtn.textContent = 'Submit Data Securely'; } }); The Problem with Synchronous Form Lifecycles Traditional submissions trigger a synchronous navigation cycle: the browser constructs a payload, issues a full HTTP request, and replaces the

Scan a menu, pay a bill, onboard a smart plug, and you are using a piece of technology that was never meant for any of those things. The QR code was invented in 1994, and its original job was tracking car parts on a factory floor. Understanding why it was built the way it was explains why it now shows up on nearly every connected device. Who invented the QR code The QR code was created in 1994 by a team led by engineer Masahiro Hara at Denso Wave, a subsidiary of the Toyota group in Japan. At the time, the automotive industry ran on standard one-dimensional barcodes, the striped labels still seen on grocery products. Those barcodes held very little data, usually around 20 characters, and a busy assembly line often needed a worker to scan ten different labels on a single box of components. It was slow, and it was error-prone. Hara wanted a code that could store far more information and be read much faster. His answer was to go two-dimensional: instead of encoding data only in the width of vertical bars, a QR code (short for "Quick Response") stores data across a grid of black and white squares, packing in thousands of characters in a fraction of the footprint. The board game that shaped it The most famous detail of the QR code's origin is where Hara found his design inspiration. He was reportedly playing the board game Go on a lunch break, staring at the black and white stones arranged on the grid, when the idea of encoding information in a two-dimensional matrix of light and dark cells clicked into place. The harder problem was speed. A scanner needs to find the code and figure out its orientation before it can read anything, and in a factory a label might be rotated any which way. Hara's team solved this with the three distinctive square markers in the corners of every QR code. Those "position detection patterns" use a ratio of black to white areas that almost never occurs by chance in printed material, so a scanner can instantly locate the code and work out its an

Web Accessibility for Startups: 5 Small Wins That Scale Palak jain Palak jain Palak jain Follow Oct 7 '25 Web Accessibility for Startups: 5 Small Wins That Scale # webaccessibility # webdev # frontend # startup 21 reactions 4 comments 4 min read

GenPage is a generative AI system developed by Netflix to replace its traditional multi-stage recommendation pipeline by directly generating personalized user homepages. GenPage leverages user history and request context as a prompt to produce the entire page, resulting in improved user engagement and reduced serving latency. By Sergio De Simone

On the latest episode of Equity, we debate whether Apple's lawsuit will cast over OpenAi's much-discussed plans to get into hardware and go public.

My Zero to Blockchain Engineer journey — Week 2 update Last week I announced I was teaching myself blockchain engineering from zero. This week I went deep into Cyfrin Updraft — and I have to be honest, it changed how I think about learning Web3. Here's everything I covered, what surprised me, and why I think this platform is the best free resource for anyone serious about becoming a blockchain developer. What I covered this week I'm currently 45% through the Blockchain Basics course on Cyfrin Updraft. Here's exactly what the lessons covered: Section 1 — What Is A Blockchain? This section answered the question properly — not in a surface-level way, but by starting with the problem blockchain solves. The lesson on centralized control hit differently. Think about it in a Nigerian context — banks freezing accounts, payment processors blocking transactions, elections where results can't be independently verified. These aren't hypothetical problems. They're things we live with. Blockchain's answer: a shared ledger where truth is verified by math, not by a middleman. That framing made everything click for me. Key lessons I completed: What Is A Blockchain — centralized problems vs decentralized solutions History Of Blockchain — from the double-spend problem to Bitcoin to Ethereum Benefits Of Blockchain — permissionless, immutable, credibly neutral Use Cases — DeFi, cross-border payments, DAOs, digital ownership Many Many Chains — L1s, L2s, Mainnets vs Testnets The Oracle Problem — why smart contracts can't access real-world data alone The Purpose Of Smart Contracts — math-based promises vs trust-based agreements What Is The EVM — Ethereum's computation engine explained simply Benefits Of Smart Contracts — decentralization, immutability, transparency Section 2 — Sending Transactions This is where things got hands-on — and where Cyfrin pulled ahead of every other platform I've tried. Key lessons: What Is A Wallet — your public address as a digital mailbox Setting Up A Wallet

Learning software engineering in the past was a straight forward process, you learn the programming language, you build projects in your portfolio, apply to companies, get a job and life goes on. Doing that in the current times might be a little bit different, as when applying for jobs, you can see some new requirements other than your programming skills and portfolio project such as Prompt Engineering, Work with Agents, Claude Code, and others. You may ask yourself, what are those? And if I am new to Software Engineering, will that change my learning path? Lets discuss all this below. Software Engineering in the Past For a long time, the path into software engineering was clear. You picked a language, maybe Java, Python, or JavaScript. You spent a few months learning the syntax, then the fundamentals: data structures, algorithms, how a database works, how the web sends and receives data. After that, you built things such as A todo app, weather app, clone of a website you liked. These projects went into a portfolio, usually a GitHub profile and a simple personal site. Then you applied to companies, passed a technical interview, and started your first job, so the skills you needed were stable, if you learned React in 2018, React was still useful in 2021. Tools changed, frameworks came and went, but the core idea stayed the same: you write the code, you understand what you wrote, and you fix it when it breaks. When Did AI Start Becoming Something Required The shift did not happen in one day. It came in steps. The first step was autocomplete , around 2021, tools like GitHub Copilot started suggesting the next line of code while you typed. Most developers saw it as a nice helper, nothing more. It saved you from writing boilerplate, but you were still the one thinking. The second step was chat , when ChatGPT and Claude became popular, developers started using them to explain errors, review code, and write small functions. Still a helper, but a much stronger one. At this

You've got a Next.js app on one domain and your API on another. The login works, the dashboard fills up, everything looks fine. Then one day you open the Network tab and there it is: your access token, in plain text, in a request header the browser sent all by itself. At that point every line of JavaScript on the page can read it. Not just your code. The analytics snippet you added last week, the twelve transitive dependencies you've never opened, whatever an XSS bug manages to slip in. And you never really decided this. It just happened, because calling the API straight from the component was the path of least resistance, and nothing complained. The browser is not a trusted client The version that gets you here looks completely reasonable: // ❌ a Client Component talking to your API ' use client ' ; export function Profile () { useEffect (() => { fetch ( ' https://api.example.com/me ' , { headers : { authorization : `Bearer ${ token } ` }, }) . then (( r ) => r . json ()) . then ( setProfile ); }, []); } Look at what that actually signed you up for. The token is in JavaScript, so the protection an HttpOnly cookie would have given you is simply gone. The call is cross-origin, so you're now on the hook for CORS: preflight requests, an allowed-origins list to maintain, Access-Control-Allow-Credentials , and the quiet worry that you've opened it wider than you should. And because the request leaves from the browser, anyone with devtools can read your API's base URL, its routes, and how it expects to be authenticated. That's a decent map of your backend, handed out to every visitor. What makes this hard to catch is that none of it breaks. It works in the demo, it works in production, it reviews clean. It just sits there as a liability until the day it stops being quiet. The browser talks to your server. Your server talks to your API. The Backend-for-Frontend pattern draws one line: the browser only ever talks to your Next.js server. The Next.js server talks to your API.

The line between editorial content and paid promotion has been eroding for years. On most major retail platforms, in countless lifestyle publications and across the breadth of social media, content that presents itself as independent assessment is frequently underwritten by the very brands being assessed. This is not a new problem, but it is a worsening one. The consequences are practical. Consumers who believe they are reading a review and acting accordingly may be making purchasing decisions on the basis of what is, in effect, an advertisement. The financial cost of that mistake is modest in most individual cases. The cumulative effect on consumer trust is not. Understanding what separates a genuine review from disguised promotional content requires more than checking for a disclosure label. It requires looking at structure, incentive, methodology and language - and recognising that some of the most effective adverts are those that most convincingly resemble reviews. What a Review Is Actually Supposed to Do A review serves one primary function: to help a reader make an informed decision about a product, service or brand. That function is incompatible with advocacy. A reviewer who is trying to help a reader decide cannot simultaneously be trying to persuade that reader to buy. This distinction sounds obvious. In practice, it collapses quickly when the person writing the review has a financial relationship with the brand, when the product was provided free of charge without conditions, or when the publication depends on advertising revenue from the sector it covers. None of those arrangements automatically invalidates the content produced. But each one creates a structural incentive that pushes in a specific direction - and that direction is rarely towards harder scrutiny. The Disclosure Problem Regulatory frameworks in the UK, including guidance from the Advertising Standards Authority and the Competition and Markets Authority, require that paid-for content be clea

Over the weekend, I participated in the OpenAI Build Week Hackathon with a simple goal: Build something I would actually use as a developer. By Friday evening, I had an idea. By Sunday, that idea became an open source project called Arcovia . It wasn't just another AI coding experiment. I wanted to solve a problem I've faced while working on large React applications for years. The Problem We have excellent tools for code quality. ESLint catches code smells. Prettier formats code. SonarQube reports issues. AI reviews code and suggests improvements. But none of them answer questions like: Is my architecture healthy? Which modules are becoming bottlenecks? Where is technical debt accumulating? Which files should I refactor first? Why did my project receive this score? Architecture is often something we discuss during code reviews or realize too late when the codebase becomes difficult to maintain. I wanted a tool that could measure architecture health before it became a problem. Introducing Arcovia Arcovia is an Architecture Intelligence tool for React and Next.js projects. Instead of generating hundreds of isolated warnings, it analyzes the project as a whole and produces an interactive HTML report. It includes: 📊 Architecture Health Score 🕸️ Dependency Graph 🎯 Architecture Hotspots 📈 Explainable Score Breakdown 🛠️ Rule-based Findings 📉 Maintenance Burden ✅ Actionable Recommendations The goal isn't to replace linting. The goal is to help developers understand the bigger picture. How It Works Internally, Arcovia follows a deterministic analysis pipeline. Project │ ▼ Scanner │ ▼ AST Parser │ ▼ Project Model │ ▼ Dependency Graph │ ▼ Rule Engine │ ▼ Score Engine │ ▼ Interactive HTML Report The analyzer currently detects things like: God Modules High Fan-In High Fan-Out Orphan Modules Large Components Deep JSX Nesting Oversized Modules Duplicate Imports Unused Exports Instead of simply reporting findings, Arcovia combines them into an explainable architecture score. Determ

Try asking this in your next team meeting: "Who on our team is the best at using AI?" There will probably be a moment of silence. Someone thinks of the person who uses the tools the most. Someone thinks of the person who's good with prompts. Someone thinks of whoever recently produced something impressive-looking with AI. But none of these answers feels convincing. There's a gut sense that something is off — and yet nobody knows what the right criterion would be. Nearly every company is standing in front of this wall right now. AI competence is rising to the top of hiring and performance criteria, and there's no yardstick to measure it with. This essay proposes one. The short answer is a set of five axes I call ABCD2. But before that, we need to fix the question itself. The very idea of "good at using AI" is wrong The title of this essay says "good at AI" — but that phrasing is itself the trap. The word "using" carries a hidden frame: AI is a tool, and the person is a user. It puts AI on the same shelf as Excel and Photoshop — something you get skilled at operating. On top of that frame, evaluation naturally becomes a measure of tool proficiency. How many features do you know? How fluently do you operate it? But AI is not a hammer. AI is an engine that judges and executes. It's less like a tool and more like a junior colleague you can delegate work to. And once you think of a junior colleague, the frame flips. Behind every junior who performs well, there is invariably a senior who makes them perform well — someone who explains the context of the work, lays out the criteria for judgment, makes it safe to ask about ambiguities, and verifies the output. That senior isn't "using" the junior. They are making the junior do the work well . AI is exactly the same. The person who truly gets the most out of AI is not the person who is good at using AI, but the person who helps AI do the work well. And what do they help with? Two things, broadly. One is knowledge building — pr

**🙄 فى كتاب chapter الصدمة دي هي بالظبط اللي حسيت بيها وأنا بقرأ أول “operating system: three easy Pieces “ ‘OSTEP’. 🖤اهلا بيكم ! يارب تكونوا بخير 💯 حابه اشارك معاكم رحلتى مع قراءة الكتاب ونستفيد منه سوا قبل ما نعرف هو !!!! ولا لا OS is virtual machine 🤔 !!!!دا يعنى ايه ووظيفته ايه OS تعالوا الاول نعرف ال موجودة فى الكمبيوتر ولا لا ؟؟؟؟ RAM وال CPUزى ال HARDWARE وهل هو قطعة ::هنبدأ من الاخر موجود على الكمبيوتر SW هو عبارة عن HW يسيدى مش قطعة OSال .على الجهازINSTALL ولازم يبقى ووظيفته ايه؟؟؟ OS طب يعنى ايه ‘OS’ اختصار “OPERATING SYSTEM”. .“يعنى نظام تشغيل “ .👁‍🗨👁‍🗨 تعالوا نعرف معنى ” نظام تشغيل” ونفسرها نظام تشغيل يعنى مسئول عن 1_ “HWالنظام “من غيره مش هيبقى فيه نظام خصوصا لقطع ال . يعنى بناخد منه الاذن للعمليات HW ببعضه وكمان بيدير ال HWيعنى هو بيربط ال 👏 . (Order)من الاخر بيخلق “النظام” 2_.”التشغيل “هو المسئول عن تشغيل البرامج Learn about Medium’s values هو اللي بيتحكم في دورة حياة البرنامج بالكامل؛هو اللي بيبدأ تشغيله في الذاكرة،OSيعني الـ وبيحدد هو محتاج إيه من إمكانيات الجهاز عشان يشتغل بكفاءة وبيفضل مراقبه ويحميه طول ما هوشغال ، ولحد ما تقفليه بنفسك 🌹 .ويقوم هو بتنظيف الذاكرة وتوفير المساحة لغيره 🧐::وظيفته بقا بيخلي البرنامج يشتغل بسهولة:1️⃣ لأنه بيخلق للبرنامج بيئة وهمية مريحة، وبيخفي عنه كل التعقيدات والأسلاك بتاعة الهاردوير عشان يشتغل بسلاسة 2️⃣: بيسمح للبرامج إنها تشارك الذاكرة بين كل البرامج بالعدل RAM بيشتغل كعسكري مرور بيوزع مساحة الـ . وبيحمي كل برنامج في مساحته الخاصة 3️⃣: بيمكن البرامج إنها تتعامل مع الأجهزة بيعمل كـ “وسيط” أو مترجم بيوصل كود البرنامج بالقطع الحقيقية (زي الشاشة أو الهارد) .عشان ينفذ أوامره فوراً بيعمل التلات حاجات دول إزاي في نفس الوقت وبمنتهى السلاسة؟ ؟؟؟OS طب الـ بيعملهم يسيدى عن طريق أكبر خدعة سحرية في عالم الكمبيوتروهي الـ Virtualization👌 كنت حابة جداً أكلمكم المرة دي عن خدعة الـ “Virtualization” 💎 😌👁👁اللي اتكلمنا عليها في العنوان، بس لقيت البوست هيبقى طويل أوي عليكم ✔🔥!!وعشان متعبكمش معايا.. هنخليها للبوست الجاي إن شاء الله 💯✅!!بيعمل الخدعة دي إزاي OSاستنوا البوست الجاى عشان نعرف الـ 💋.دمتم بخير **

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Clap on. Clap off. Well, more like, Clap, pause for half a beat but no longer because otherwise it'll stop hearing you, clap again because you waited too long, clap louder and faster, that didn't work, clap two more times, and suddenly: on. The Clapper didn't always work - and even when it did, it […]