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How Artificial Intelligence Disrupts Engineering Progression

AI is disrupting career progression by eliminating the learning opportunities at each rung while simultaneously enabling people to perform above their experience level, Alasdair Allan explained in his talk Engineering Progression When AI Ate the Middle at QCon London. Fewer junior developers join the industry, and AI slows hiring at the entry level. By Ben Linders

2026-08-13 原文 →
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Why Stream ring-maker Sandbar says the future of AI wearables is voice

AI notetaking hardware has taken off over the past couple of years, with credit-card-sized devices, pendants, pins, and even transcribing earbuds all promising to capture your meetings and turn them into summaries and action items. Now, a whole wave of wearables — rings especially — are betting people want to capture stray thoughts and ideas the same way. One of […]

2026-08-13 原文 →
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What Extended Support Actually Costs: The Cross-Vendor Pricing Reference for Post-EOL Software

The first cross-vendor pricing reference for post-EOL extended support. Microsoft ESU: $61/device doubling yearly ($30 one-time for consumers). AWS: EKS $0.60/cluster-hour (6x), RDS $0.100/vCPU-hour doubling in year three. Ubuntu Pro: $25 desktop / $500 server per year. And the honest quote-based rows — Red Hat ELS, Oracle, SUSE LTSS, CentOS ELS — where no public price exists. Every figure traced to a vendor price page or our verified coverage. The cross-vendor price table Product What it buys Pricing model Published price (verified 2026-08-12) Source Windows 10 — consumer ESU Critical + important security updates to Oct 12, 2027 Per device, one-time enrollment Free (settings sync) · 1,000 Microsoft Rewards points · or $30 one-time Microsoft consumer ESU page Windows 10 — commercial ESU Yearly security updates, max 3 years (to Oct 2028) Per device, per year — doubles annually, cumulative $61 yr 1 → $122 yr 2 → $244 yr 3 (≈$427 total) · $0 on Azure VMs / Windows 365 / AVD Microsoft ESU documentation Windows Server 2012/R2 ESU Final ESU year ends Oct 13, 2026 — then nothing, at any price Per year, via volume licensing or Azure Arc 100% of the full license price, each year — no year discount · $0 on Azure Per our analysis of Microsoft's ESU FAQ Amazon EKS 12 more months of Kubernetes version support Metered per cluster-hour — enrollment automatic, no opt-in $0.60 /cluster/hr vs $0.10 standard (6x) — ≈$438/mo vs ≈$73/mo per cluster AWS EKS pricing page Amazon RDS (MySQL / PostgreSQL) Up to 3 years past end of standard support Metered per vCPU-hour — enrollment automatic unless disabled $0.100 /vCPU-hr yrs 1–2, $0.200 yr 3 (AWS US East example rate) — Multi-AZ pays per instance AWS RDS pricing page Ubuntu — ESM (Ubuntu Pro) +5 years of patching past LTS standard support (20.04 to Apr 2030, 22.04 to Apr 2032) Per-machine subscription Free up to 5 personal machines · $25 /desktop/yr · $500 /server/yr (unlimited VMs) — support plans extra Canonical's Ubuntu Pro pricing page

2026-08-12 原文 →
AI 资讯

Presentation: Adopting Memory-Safety and Fine-Grained Compartmentalisation with CHERI

David Chisnall discusses how the CHERI hardware architecture redefines pointer safety to solve isolation and sharing challenges. He explains how CHERI enables spatial and temporal memory safety for C/C++, scales down to microcontrollers with CHERIoT, and replaces costly OS-level RPC mechanisms with lightweight, auditable compartmentalization - all without requiring massive codebase rewrites. By David Chisnall

2026-08-12 原文 →
AI 资讯

Windows 12 Is Still a Rumor — Windows 11 26H2 Is the Real Story

The Windows 12 rumors continue, but Microsoft's actual roadmap tells a different story. There is still no official Windows 12 release announcement for 2026 . Meanwhile, Microsoft is continuing to develop Windows 11, with version 26H2 appearing in Insider builds and the company maintaining its annual feature-update cadence. For developers and system administrators, this may be more important than the Windows 12 name itself. Windows Is Becoming a Continuously Updated Platform The old model was straightforward: Windows 10 ↓ Windows 11 ↓ Windows 12 The current model looks more like: Windows 11 ├── Security Updates ├── Feature Updates ├── AI Features ├── Hardware Support └── Platform Changes Microsoft says Windows 11 receives an annual feature update in the second half of each year, alongside monthly cumulative security updates. Windows 11 26H2 Microsoft has already exposed version 26H2 in Windows Insider builds. The June 2026 Insider release changed the version information shown by winver and Windows Settings to 26H2. Recent reporting expects the broader 26H2 rollout later in 2026, potentially as an enablement package for compatible Windows 11 systems. This is significant because an enablement-style update can make a major version transition much less disruptive. Why Developers Should Care The Windows version number is becoming less useful as a proxy for the actual platform capabilities. A developer increasingly needs to consider: Windows build number API availability security features hardware capabilities virtualization NPU availability driver versions application compatibility Two machines running Windows 11 may therefore have substantially different capabilities. Windows 11 | +---- CPU +---- GPU +---- NPU +---- VBS +---- HVCI +---- Secure Boot AI Could Define the Next Windows Generation The biggest change may not be Windows 12 itself. It may be the increasing integration of local AI and dedicated NPUs . That creates a new development model: Application | v Windows A

2026-08-12 原文 →
AI 资讯

API الخاص بك يزيل بيانات C2PA الوصفية: كيفية كشف ذلك بالاختبار

يقوم Claude الآن بإرفاق بيانات تعريف العزو (provenance metadata) المشفّرة وفق C2PA بالملفات التي ينشئها. وينطبق الأمر نفسه على نماذج الصور من OpenAI و Gemini . هذا يعني أن إشارة العزو تصل سليمة إلى نقطة التحميل لأول مرة، لكن سلسلة معالجة الصور لديك قد تحذفها قبل أن يراها أي شخص. جرّب Apidog اليوم لا يحدث ذلك بنية سيئة؛ بل يحدث افتراضيًا. فمثلًا، تنشئ sharp().resize() ملفًا جديدًا بلا بيانات تعريف ما لم تطلب الاحتفاظ بها صراحةً. وينطبق ذلك أيضًا على ImageMagick وPillow ومعظم شبكات CDN الخاصة بالصور. يدخل الملف، ويخرج JPEG أصغر، ولا تخبرك السجلات أن بيانات العزو اختفت. هذه مشكلة قابلة للاختبار. ستتعلم هنا كيف تحدد المرحلة التي تحذف بيانات C2PA، وتثبت ذلك عبر رحلة رفع وتنزيل حقيقية، وتضيف فحصًا في CI يمنع عودة المشكلة. يتولى Apidog تنسيق سيناريو الـ API، بينما يتولى c2patool التحقق من صحة البيانات على مستوى البايت. ما الذي يتم تدميره بالفعل؟ بيان C2PA هو كتلة موقعة تشفيريًا ومضمّنة داخل حاوية الملف. يسجل من وقّع الأصل وما الذي ادعاه عنه. وبما أنه موقّع، فإن تغيير البايتات دون إعادة التوقيع يكسر التوقيع بطريقة يستطيع أي مدقق اكتشافها. النقطة المهمة هنا هي حاوية الملف : عندما تعيد كتابة الحاوية، قد يختفي البيان. العملية هل يبقى البيان افتراضيًا؟ نسخ أو نقل بايت ببايت نعم sharp().resize().toBuffer() لا ImageMagick عبر convert أو magick لا Pillow عبر Image.save() لا تحويل PNG إلى WebP أو JPEG إلى AVIF لا التحسين التلقائي في CDN للصور غالبًا لا لقطة شاشة لا إعادة الحفظ من محرر صور لا رفع إلى S3 دون تحويل نعم كل عنصر في عمود لا هو إجراء شائع في تطبيقات الويب: إنشاء صور مصغرة، توليد صور متجاوبة، التفاوض على التنسيق، أو إزالة EXIF لأسباب الخصوصية. كل خطوة منطقية بمفردها، لكنها قد تنهي سلسلة العزو بصمت. انتبه أيضًا إلى أن استخدام -strip لإزالة بيانات EXIF قد يكون مقصودًا، لأن EXIF قد يحمل إحداثيات GPS أو أرقامًا تسلسلية للكاميرا. لكن إزالة جميع البيانات الوصفية للتخلص من بيانات الموقع تزيل بيان C2PA كذلك. الحل هو إزالة البيانات الحساسة بشكل انتقائي، لا حذف الكتلة كاملة. أثبت المشكلة في دقيقتين قبل تغيير خط الأنابيب، تحقق من وجود المشكلة فعلًا. تحتاج إلى ملف واحد يحتوي على بيان

2026-08-12 原文 →
AI 资讯

The Celery Lifecycle: How a Task Gets Registered, Queued, and Run

If you have ever needed to send an email, process a payment, or generate a report without making your user wait, you have probably run into Celery. Celery is a tool that lets you run jobs in the background, away from your main app. This article breaks down how it works, step by step, in plain language. What Is Celery, In Simple Terms Think of Celery like a restaurant kitchen. Your app (the waiter) takes an order from a customer. Instead of cooking the food itself, the waiter drops the order into a queue (the kitchen order rail). A cook (the worker) picks up the order from the rail and prepares it. When the food is ready, it goes to a pickup counter (the result backend) where anyone can come check if it's done. Celery has four main players: The Producer - your app, the one that creates tasks. The Broker - the message queue that holds tasks until a worker is free. The Worker - the process that picks up and runs the tasks. The Result Backend - where results are stored, if you need them later. In short: your app sends a task message to the broker. The broker holds it until a worker is free. The worker picks it up, runs the actual function, and (if you set one up) writes the result to the result backend. Your app can then go back and check that result backend to see what happened. Now let's go through each part. 1. How Tasks Get Registered Before Celery can run a task, it needs to know the task exists. This is called registration , and it happens the moment your Python code is imported - not when the task runs. The @app.task decorator You create a Celery app instance, then decorate any function with @app.task . That decorator does not run the function immediately. Instead, it wraps the function and adds it to a task registry - basically a dictionary that Celery keeps internally, mapping a task name to the actual function. from celery import Celery app = Celery ( " myproject " ) @app.task def send_welcome_email ( user_id ): # logic to send an email print ( f " Sending wel

2026-08-12 原文 →
AI 资讯

Microsoft Plugs Nearly 400 Security Holes

Microsoft today released updates to remedy at least 398 security vulnerabilities in its Windows operating systems and supported software, including one weakness that is already being actively exploited and two others that were publicly detailed prior to today.

2026-08-12 原文 →
AI 资讯

I Built This to Fix One Task. It Turned Into Something You Can Run.

There are two ways to work with an AI agent and I had tried both. Write the thing yourself and hand over only the tedious parts. Or hand over the whole task and audit whatever comes back at the end. The first is slow. The second is fast right up until it is wrong, and by then the wrong thing is finished. I expected this series to be about forcing a third option into existence. Nine parts of making an agent follow a workflow it would rather skip. That is not what happened. I never had to enforce it once. The queue that started this had a payload contract nobody had verified, and each phase after that cost me something before it gave anything back. A plan that would not move until the risk register named the provider contract the brief had only guessed at. A build that missed nothing except what my own brief left out. A review that stopped handing back a feeling and started handing back a verdict on every requirement I had already called done. A matrix instead of a trusted green run. A rollback with a name on it before anything got called shipped. And a retrospective that would not let a lesson through until it had checked itself against the trail. Eight parts of that. What I did not expect was which part turned out to be automatic. The Fight I Expected Never Started By the time I finish writing a requirement, I already know roughly what it is going to cost. Most engineers do. You can feel the difference between a one-line fix and something that is going to touch four files and a migration before you have written a single line of it. What I assumed was that the agent could not feel that, and that policing the gap would be my job forever. Reminding it to run the chain. Catching it when it decided a spike was small enough to skip. It has not needed the reminder. Small bugs do not trigger a brief and a plan, and they should not. A standard requirement, a spike, anything long or cross-cutting, runs the full cycle in order. The classification lands where I would have put i

2026-08-12 原文 →
AI 资讯

Processes vs Threads

📺 Prefer to watch? 90-second YouTube Short · 💬 Telegram Originally published on software-engineer-blog.com . You run code concurrently all the time. But "concurrent" hides a critical choice: are you spawning separate processes or threads inside the same process? That choice decides whether one crash takes down your entire system or stays contained, and whether you're copying data between isolated worlds or racing to read the same memory. Mental model: A process is its own house; threads are roommates sharing one. Processes: Isolation at the Cost of Weight When you start a process, the operating system hands it its own private address space. That address space is walled off. Your process can't touch another process's memory—the OS enforces it at the CPU level. If your process crashes, it corrupts only its own memory. The kernel cleans it up. Every other process keeps running untouched. This is why browsers put each tab in its own process. One tab runs malicious JavaScript, spins into an infinite loop, or has a memory leak—that tab's process dies. The rest of your browser lives. You close the dead tab and open a new one. Your other tabs don't even hiccup. But isolation isn't free. Each process carries: Its own copy of the heap, stack, and memory pages Its own file descriptor table, open sockets, and kernel resources OS overhead to track and protect it Spawning a process is expensive—milliseconds on modern hardware, but measurably heavier than a thread. And if two processes need to share data, they can't just read the same memory. One process must copy data into a pipe or socket, send it across, and the other process must copy it out and into its own memory. That's overhead on every exchange. Threads: Speed and Sharing, With a Trap Threads live inside a single process and share that process's entire memory. The kernel doesn't wall them off from each other. When you spawn a thread, you're not duplicating the heap, the file descriptors, or the kernel state—you're just cr

2026-08-11 原文 →
AI 资讯

The automation post pipeline

I am testing my first automated end to end social media post automation system. which is created using the free tools. But it is very efficient and productive. i can use this thing in future posting on various platforms to tell people about my learning's and update about me. Tools : Make.com = I use this tool to mainly automate my system it include flow how things works and system is linked. Hashnode = I use this as a central blog and article publishing tool other tools is connected with it so content links is properly distributed. Google Ai Studio = I use this to integrate the ai in between this whole process which just do small job to add the engaging hook and the tags for the reach Buffer = I use to connect X (twitter) with this Because Make.com remove the platform X (twitter) to His integration. After the policy change of the platform. Dev.to = I use this to improve SEO of my post over the google search engine. Challenges : I cannot integrate the github actions with the hashnode becuase this feature is become paid on hashnode. May be in future i can do this thing using self written yml file, i am guessing Not sure will this 100 % work or not. Twitter integration as i described early that twitter integration is not present in the make.com so i use the another tool Buffer. The limits calculation, Their was a limits on each tools for their specific use case so i have to intentionally calculate them properly. Even the free tear of the twitter which is X is few hundreds words that's why i have to limit the text of the post, which is hook only, The threads creation i don't think it will be their in this tools which i am using, i will definitely find it if their. Solutions : Simply use other Way if this way is closed, use different tool for twitter May be in future i create yml file for the github actions but for now i am directly writing on hashnode. The dev.to does not provide feature of direct posting it save your cycle into draft so you have to manually click on pu

2026-08-11 原文 →