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A visual explainer on how copy-on-write makes fork() cheap. A 10 GB process can fork almost instantly because Linux copies the page tables, shares the physical pages, marks them read-only, and only copies a page when one of the processes writes to it. It also covers fork() + exec() , how Redis snapshots work, Android Zygote, lazy zero pages, and some CVEs submitted by /u/Ok_Marionberry8922 [link] [留言]

Welcome back to TechCrunch Mobility, your hub for the future of transportation and now, more than ever, how AI is playing a part.

I’ve been out of context with my computer science degree for years, and honestly, it feels overwhelming. I want to change that, so I'm starting this log to document my comeback story, my failures, and my progress. What I did today: Acknowledged the starting line. Took 10 minutes to think about where I left off and what I need to tackle first. Decided to write daily logs. This is not my first attempt to finish the degree, but hopefully with some public eyes on it, it will be the last. 10 subject left. Starting with Mathematical analysis. The biggest challenge right now: Feeling completely out of context and fighting the urge to get distracted by everything else. Many things have changed since i was actively going to classes. My generation graduated many years ago and i feel like getting all the information I need will be extremely difficult since i don't know anyone. Getting information from the university websites and emailing professors will be slow. Next steps: Tomorrow, I'm going to try to overcome my overdramatic anxiety and post in the student groups I was in, in hope that somebody might have new information about the subjects. I will go start lesson #1. Goal is to ease into the material and start a habit, rather than pressuring myself to do everything at once. Come back tomorrow.

This is a submission for DEV's Summer Bug Smash: Clear the Lineup , powered by Sentry . Project Overview I built LeakLens Control Room to keep AI away from the financial math. Python calculates every dollar. The optional model explains the evidence. A human decides whether to act. LeakLens is a live, functional restaurant margin-review application built with Python, SQLite, HTML, CSS, and JavaScript. It uses synthetic data in the public demo. The optional application narrative provider defaults to GPT-5.6, but every rate, threshold, ranking, and dollar scenario comes from deterministic code. Then a date string proved that deterministic code can still lie. I had let a raw CSV field decide which operating day counted as "current." Bug Fix or Performance Improvement The symptom LeakLens groups channel metrics by date, sorts those dates, and analyzes the last one against prior days. That works when every input is canonical ISO data such as 2026-07-09 . The loader only checked that the date field was not blank. This input slipped through: metrics = [ DailyMetric ( " 2026-7-9 " , " direct " , 500 , 250 , 0 , 0 , 125 ), DailyMetric ( " 2026-07-10 " , " direct " , 1000 , 50 , 0 , 0 , 250 ), ] July 10 is later than July 9. Python's string ordering sees something else: sorted ([ " 2026-07-10 " , " 2026-7-9 " ]) # ['2026-07-10', '2026-7-9'] LeakLens selected the final string, 2026-7-9 , as the current day. That mistake spread through the report: expected current date: 2026-07-10 actual current date: 2026-7-9 actual gross sales: 500.00 sales-loss scenario: 500.00 discount scenario: 225.00 The input had not shown a current sales collapse or discount leak. The analyzer compared the days backward and produced $725 of false scenario impact from synthetic data. For a restaurant operator, this is worse than a visible crash. A crash asks for attention. A plausible financial report can send someone to audit the wrong promotion, question the wrong manager, or change a schedule that was

TL;DR: Passing a historical blockNumber to eth_call , eth_getBalance or eth_getLogs silently routes your request to the archive tier of hosted RPC providers. In our production metrics, archive calls cost on average 26.7x more compute units than the same call at latest . This post explains why, shows the exact client code pattern that triggers it, and gives you three Prometheus queries to measure your own archive exposure in under a minute. Full cross provider measurements are published in the OpenChainBench RPC benchmarks . Last week our RPC cost dashboard flagged an overage projection above four thousand dollars for a single billing cycle on a single provider. On paper, our services were doing normal eth_call operations. In practice, one small pattern buried in three separate indexers had multiplied our compute unit consumption by more than an order of magnitude, and nothing in the code review process had surfaced it. This post breaks down what an archive multiplier is, why it silently inflates blockchain RPC bills across every major hosted provider, and how to detect it in your own Prometheus stack before the next overage alert lands in Slack. What does "archive" mean at the Ethereum node level? Every request that reads the state of a smart contract, whether through eth_call , eth_getBalance , eth_getStorageAt , eth_getCode , or a batch of these, requires the RPC node to reconstruct the world state at a specific block height. Ethereum clients handle this in two modes. Full node mode. The state trie is kept in memory or on fast SSD for the tip of the chain plus a rolling window of recent blocks. On Geth default settings that window is 128 blocks deep. Any query targeting latest , pending , or a block within that window resolves in a few milliseconds against the current state. Archive node mode. The client preserves every intermediate state trie since genesis. Answering a query at a block from months or years ago requires reading historical trie data off disk and re

Reading a public website and changing something inside a signed-in account are not the same kind of job. I wanted an AI agent to collect the first 10 Hacker News stories, explain how it gathered them, and place that work inside my DEV editor. Save the article as a draft. Do not publish it. The hard part was not finding the stories. It was giving the agent enough access to finish inside my signed-in account without giving it permission to publish. That instruction contained two jobs with different access needs: Read public data from Hacker News. Change private account state by creating a draft in my signed-in DEV account. Using a full browser for the first job would add machinery and access that the result did not need. The public fetch path had neither the authenticated context nor the authority required for the second job. The useful question was not “Which browser tool should do everything?” It was “Where does this task actually cross into a browser?” Disclosure: I work on Unchained, SearchAgentSky, and Unbrowser. This article was prepared with AI assistance and reviewed before publication. The public step needed only fetch I initially opened Hacker News in a lightweight page session and queried its story links. It worked—but that did not mean it was necessary. Hacker News has a public API . The same result could be produced with ordinary HTTP: const base = " https://hacker-news.firebaseio.com/v0 " ; const ids = await fetch ( ` ${ base } /topstories.json` ). then ( r => r . json ()); const stories = await Promise . all ( ids . slice ( 0 , 10 ). map ( async ( id , index ) => { const story = await fetch ( ` ${ base } /item/ ${ id } .json` ). then ( r => r . json ()); return { rank : index + 1 , title : story . title , url : story . url ?? `https://news.ycombinator.com/item?id= ${ id } ` }; })); console . log ( JSON . stringify ( stories , null , 2 )); At 2026-07-19T03:19:04Z , the page query returned 30 Hacker News story anchors. At 2026-07-19T03:35:29.670Z , fetch

Bluetooth may seem the obvious next step in the remote's evolution, but there are reasons that infrared hasn't died out.

**🙄 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 **

In 2017, I bought an old Magnus chord organ off Craigslist for $10. It's one of my favorite music gear purchases. Electric chord organs let you play full chords with just a press of a button, making them perfect for aspiring singer-songwriters or fooling around at home. But chord organs largely fell out of favor […]

Clock drift, deadline mismatches, retries, and TTL behavior keep causing real incidents. Network companion: https://blog.gaborkoos.com/posts/2026-07-01-Beyond-Happy-Path-Engineering-the-Network/ submitted by /u/OtherwisePush6424 [link] [留言]

"Everybody knows the Greeks are inside."

Current AI, a non-profit building AI that leaves no one culture behind, has made remarkable progress across devices, AI chat and more.

OLED displays are the best of the best in many ways, but in some scenarios, "lesser" technology really shines.

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 […]

Part of a series on building cz-agents → under the hood. Where we left off In June, I compared the three camps of agentic payments here—x402, card tokens, and banks—and argued that x402 will take machine-to-machine micropayments, while cards and banks split the rest. I won't repeat the basics of the protocol, how the 402 status code works, or why cards don't add up economically on tiny amounts; anyone who needs a refresher will find it in that article. This piece is about something else. A few weeks have passed; the numbers and the big-player backing have both jumped by an order of magnitude, and for the first time, I can compare those figures against what I actually see in my own logs. The gap between the two is the entire point of what follows. The numbers are hard to miss I'll start with what speaks for x402, because that's the more honest approach. According to aggregate Chainalysis data, the protocol has processed over 169 million payments so far, between roughly 590,000 buyers and 100,000 sellers. That's no longer a conference demo. More interesting than the volume is the structural shift. The share of transactions above one dollar rose from 49% in early 2025 to roughly 95% in early 2026. In other words: x402 is ceasing to be a toy for micro-cents and is starting to handle amounts that actually show up on the books. Anyone who wrote the protocol off as a curiosity for paying fractions of a cent per API query is looking at an old snapshot. Above all, a lineup has assembled that is hard to dismiss as crypto-bubble enthusiasts: Stripe launched x402 support on February 10, 2026, in the preview of its Machine Payments product (USDC on the Base network). Ripple added native x402 to the XRP Ledger on June 21, 2026. AWS built x402 into Bedrock AgentCore Payments—together with Coinbase and Stripe—and also lets you monetize agent traffic on CloudFront and WAF. Sites behind Amazon's edge can charge agents right at the edge, without touching their core application. Google

I'm a Computer Science student about to start my 4th semester this September in Pakistan. My long-term goals are: - Maintain a high GPA because I want to pursue a fully funded Master's abroad. - Eventually work at a top tech company (FAANG or similar). - Become a genuinely good software engineer rather than just someone who can build projects. A bit about me: I actually enjoy programming. I like logic, problem-solving, debugging, and understanding how things work under the hood. My initial plan for the rest of this year (August–December) was to focus on: - Java - Spring Boot - Backend development - LeetCode and DSA - SQL - System Design (starting with the basics) - Building projects and putting them on GitHub However, my brother (he's also studying CS) has a very different opinion. He's heavily into AI, automations, AI agents, and vibe coding. He told me that spending so much time learning to code deeply is becoming less valuable because AI can already generate entire applications. He even mentioned one of his friends vibe-coded a complex website with AI that was supposedly extremely secure and feature-rich. His argument is that I should focus more on AI workflows and automation instead of traditional software engineering. My opinion is a little different. I feel like AI is an amazing tool, but someone still has to understand: - Architecture - System Design - Databases - Security - Scalability - Performance - Debugging - Clean code - Software engineering principles My thinking is that AI can generate code, but it can't replace understanding why the code works or making good engineering decisions. Now I'm questioning whether I'm becoming outdated before I've even started. So I'd really appreciate advice from people already working in the industry. Some questions I'd love honest answers to: If you were a 4th-semester CS student in 2026, what would you spend the next 4–6 months learning? Is investing heavily in Java, Spring Boot, DSA, and backend development still wort

🚀 AI is becoming smarter every day. But intelligence alone isn't enough—it also needs a standardized way to communicate with tools, applications, and data. That's exactly what Model Context Protocol (MCP) provides. 🚀 Introduction The AI landscape has evolved rapidly over the past few years. We've moved from simple chatbots to: 🤖 AI coding assistants ⚙️ Autonomous agents ☁️ Cloud automation 📊 Infrastructure monitoring 🔄 Intelligent workflows But one major challenge still exists: How can AI securely communicate with external tools like GitHub, AWS, Docker, Kubernetes, Slack, databases, and local files? Until recently, every AI company built custom integrations. That meant: duplicated engineering effort inconsistent APIs difficult maintenance poor interoperability To solve this problem, the AI ecosystem is adopting a new open standard called Model Context Protocol (MCP). 🤔 What is Model Context Protocol (MCP)? Model Context Protocol (MCP) is an open protocol that standardizes how AI models communicate with external tools, APIs, databases, applications, and services. Instead of every AI assistant creating custom integrations for every service, MCP provides one common language. Think of MCP as: 🔌 USB-C for AI applications. Just as USB-C lets different devices communicate using one standard, MCP allows different AI assistants to connect to external systems in a consistent way. ❌ The Problem Before MCP Imagine you're building an AI DevOps assistant. It needs access to: GitHub Docker Kubernetes AWS Terraform Jenkins Prometheus Grafana Local Files Internal Documentation Without MCP, you'd need to: Learn every API separately Build authentication repeatedly Maintain multiple SDKs Handle different response formats Continuously update integrations Every AI application repeats the same engineering work. This approach is: ❌ Time-consuming ❌ Expensive ❌ Difficult to maintain ❌ Hard to scale ✅ How MCP Solves This Problem MCP introduces a standardized communication layer between AI m

A salamander can lose a leg and grow a new one. Cut a zebrafish's fin and it simply builds another. Mammals, us included, got the consolation prize: a scar. For a century, biologists assumed that somewhere on the evolutionary road to becoming warm-blooded, fast-moving animals, we traded regeneration away for good. Two research teams working on opposite sides of the planet have just made that assumption look wrong. The headline is almost hard to believe: the ability to regrow lost body parts may not have been deleted from our biology at all. It may simply have been switched off, and switches can be flipped back on. The genetic "remote control" that stopped working The first clue comes from a team at the National Institute of Biological Sciences in Beijing, working with genomics powerhouse BGI-Research. Publishing in Science , they zeroed in on a gene called ALDH1A2 , the instruction sheet for an enzyme that turns vitamin A into retinoic acid, a molecule that acts like a foreman on a construction site, telling cells where to go and what to build during tissue repair. Animals that regenerate freely crank this gene up at the wound site. Mice, it turns out, still carry the same gene. They've just lost the genetic "remote controls," the regulatory DNA that tells the gene to fire after an injury. The hardware is intact; the software command was disconnected somewhere in evolution. So the researchers reconnected it. By reactivating that dormant switch and restoring the flow of retinoic acid, they got mice to regenerate damaged outer-ear tissue, something a normal mouse simply cannot do. In their own words, they had found "a genetic switch involved in the evolution of regeneration." Meanwhile, in Texas, they regrew a limb joint The second piece of evidence lands the point with force. At Texas A&M, a group led by Dr. Ken Muneoka took a different route to the same destination. Instead of editing a genetic switch, they used a precisely timed sequence of two signaling proteins.

This is The Stepback, a weekly newsletter breaking down one essential story from the tech world. For more on video games and physical media, follow Jay Peters. The Stepback arrives in our subscribers' inboxes on Sunday at 8AM ET. Opt in for The Stepback here. How it started As a kid, I relished trips to […]

submitted by /u/DataBaeBee [link] [留言]