标签：#m

Gaming keyboards have evolved over the years to add RGB LEDs, extra knobs, and buttons with screens, but one feature has remained fairly consistent: the mechanical switch. That’s slowly changing, with brands introducing adjustable optical switches that are more customizable and have a faster response time. Razer’s Huntsman V3 Pro TKL is a compact, wired […]

🦄 I shipped a linter that fails your commit if you won't admit AI touched the code, and then did the most predictable thing possible—let nine months of the data sit there untouched while I busied myself with other things. Then I actually looked at it: nine months of footers piled up in git log like a lonely change jar. Every one of them said how much of those commits were mine, but I hadn't ever sat down and actually counted the jar. So I built the thing to count it. 🪙 The Jar Nobody Counted 🫙 Nine months of commits, every single one carrying a footer that states how much of it I actually wrote, and I could not have told you the number—not roughly or even within twenty points. It was all sitting in git log , structured, and enforced on every commit by a hook I built specifically for that purpose. But it was completely inert. Dropping change in a jar isn't the same as knowing how much money is in it. rai-lint will block your commit until you write the footer, but then it's done—the pile just sits there, and I never built the thing that adds it up. anchildress1 / rai-lint Dual-language linter for Responsible AI commit footers — shared logic for Node (commitlint) and Python (gitlint). Stop playing hide-and-seek with AI in your commits. A dual-language validation framework that makes AI attribution non-negotiable. 📊 Project Stats 🗣️ Languages 📦 Packages 🤖 AI & Automation 🔧 Quality & Standards Installation • Quick Start • Required Commit Footers • Documentation What is this? 🤖 RAI Lint enforces Responsible AI (RAI) attribution in every commit. No more "who wrote this?" moments. No more mystery code. Just honest, trackable AI contributions. Read the full story: Did AI Erase Attribution? Your Git History Is Missing a Co-Author %%{init: {'theme':'dark'}}%% flowchart LR A[Developer Commits] --> B{Has AI Footer?} B -->|Yes| C[Commit Accepted ✅] B -->|No| D[Commit Rejected ❌] C --> E[Clear AI Attribution] D --> F[Add Footer & Retry] Loading Why does this exist? Because transpa

Companies without their own models — or without a layer of AI infrastructure known as AI gateways to separate their prompts from the model itself — will be in trouble, Nadella says.

The Quest Begins (The "Why") Hey friend, picture this: you’re happily writing a Node.js service, passing objects around like they’re candy at a parade. Everything works until one day you mutate a shared object in a helper function and suddenly your UI shows stale data, or worse, you get a mysterious Cannot read property 'map' of undefined that only appears in production. You spend hours tracing the flow, adding console.log s everywhere, and you start to wonder if there’s a hidden contract you missed. I’ve been there. I spent an entire afternoon debugging a race condition that only showed up when two async requests touched the same user profile. The fix felt like a band‑aid, and I kept thinking, “There has to be a better way to reason about who owns what.” That curiosity led me to Rust, and more specifically, to its ownership system—a set of rules that, at first glance, feels like a strict teacher with a red pen, but ends up being the most reliable compass I’ve ever had for writing safe, concurrent code. The Revelation (The Insight) Rust’s ownership model isn’t just another syntax quirk; it’s a philosophy that answers three simple questions for every piece of data: Who owns it? How long can it live? Who can read or change it while it’s alive? If you can answer those, the compiler guarantees you won’t have dangling pointers, use‑after‑free, or data races— without a garbage collector pausing your thread. For a JavaScript developer, that sounds like magic, but the rules are surprisingly concrete once you see them in action. Surprising Feature #1: Move Semantics (The “Give Away” Rule) In JavaScript, when you do let b = a; you’re copying a reference. Both a and b point to the same object, and mutating one affects the other unless you clone. Rust treats assignment differently for types that own resources (like String , Vec<T> , or custom structs). Assigning b = a moves the ownership; after that, a is considered uninitialized and you can’t use it again. let s1 = String .fro

The runtime boundary behind RocketRide's crash isolation, task lifecycle, and Cloud operations. By Krish Garg and Mithilesh Gaurihar At 9 a.m., with ten thousand users mid-session, a node in an AI pipeline dereferences a bad pointer. The process running it is gone before Python can raise a useful exception. That is an unpleasant failure, but it is not the question we care about most. The question is what happens next. Does that crash take unrelated pipelines with it? Does the server need a restart? Does the on-call engineer walk into a system-wide incident, or into one failed task and a useful record of why it failed? In RocketRide, a failed task is meant to be contained and recorded. The server sees the child process exit, updates the task's state and exit code, releases the task's ports and connections, and sends status updates to subscribed monitors. The run stops. Its history does not vanish. Other task processes are not sharing its memory, interpreter, or worker threads. That behavior comes from a decision we made early: every pipeline run gets its own isolated process. It is not the cheapest or fastest possible architecture. Starting a process has a cost, and keeping one around has a cost too. We accepted those costs because the alternative makes failures much harder to reason about once Python code, native libraries, model runtimes, and user-defined nodes are all running in the same service. One Process, One Blast Radius An AI pipeline does not fail like a typical request handler. A normal exception is one thing. A segfault in a C extension, a crash in a media decoder, or a broken native inference library is another. Once a process has corrupted memory, application-level error handling is no longer a reliable line of defense. So each RocketRide task starts as a fresh child process with its own embedded Python interpreter. It loads one pipeline, initializes that pipeline's nodes, and owns the work for that run. The parent runtime keeps the task registry, alloc

Climate change is upping the odds of major wildfires, creating a need for new tools to combat them.

Fusion power startup Thea Energy snagged a $20 million award from ARPA-E to scale production of its high-temperature superconducting magnets.

The deal means content will be integrated into the YouTube experience, allowing viewers to discover and watch Peacock content without leaving the platform.

The outgoing executive director of the Office of Homeland Security Statistics is one of very few federal officials to speak out against the Trump administration’s immigration crackdown.

A second gremlin is rolling through Spotify this evening.

Postmortem after several rounds of performance work on a Laravel site, trying to push concurrent user capacity up. Ended up putting Varnish in front of Nginx. submitted by /u/noweh95 [link] [留言]

An extended Xbox outage that began Sunday evening hasn't just caused issues for people trying to play digital games - it blocked people from playing their disc-based games, too. Xbox's status page initially reported the outage on Sunday at about 11PM ET, and it has also prevented people from logging in, launching apps, or finding […]

Nanoleaf’s Blocks Combo XL Smarter Kit is a fun back-to-school buy that can add pops of customizable light and storage to your wall. It combines colorful smart lighting panels with a low-profile shelf and pegboard, and right now you can buy the kit for half off at $99.99 from Nanoleaf, which marks a new low […]

This is a submission for DEV's Summer Bug Smash: Clear the Lineup powered by Sentry . Project Overview Staxa is a multi-tenant deployment platform I am building solo under Stackforge Labs. The backend is a single Go binary ( staxad ) using the chi router, with about 60 API endpoints, running on K3s on a Hetzner CAX21 ARM64 server that costs around $11/month. Each tenant gets an isolated Kubernetes namespace with their own app container, a PostgreSQL 16 or MySQL 8 database, a subdomain with automatic SSL, and resource quotas. Container builds run through Buildah, and the frontend is Next.js (App Router) with shadcn/ui and Clerk for auth. Bug Fix or Performance Improvement The symptom: POST /api/v1/tenants/{id}/deployments/{depId}/rollback accepted a deployment ID in the URL path and then completely ignored it. Whatever version you asked for, you got the most recent successful deployment instead. The route was wired up correctly in internal/api/router.go:149 : r . Post ( "/tenants/{id}/deployments/{depId}/rollback" , srv . handleRollbackDeployment ) But handleRollbackDeployment never called chi.URLParam(r, "depId") . It read {id} for the tenant and stopped there. How I found it: I was auditing my published API docs against the actual handlers, endpoint by endpoint. When I got to the rollback entry I went to write down what {depId} did, went to the handler to confirm, and found nothing reading it. The docs described an ID that the code never looked at. The worst part is that it returned 202 Accepted and then performed a real, successful rollback. Just not the one you asked for. There was no error to notice, no failed request in any log. The frontend had been passing the deployment ID into the URL since it was written ( src/lib/api.ts ), so the UI always believed the parameter was honored. Root cause: the handler created a rollback deployment row with no reference to any target, and the worker independently decided what to restore. In internal/worker/pipeline.go , runRo

I run a consumer product company by myself. Physical inventory, two storefronts, multiple marketplaces, subscription billing, bookkeeping, tax filings, government paperwork, content in two languages, and a codebase that ships to production most weeks. Headcount: one. Not "one plus a virtual assistant." One. Three years ago this was not possible. It is also not possible today by keeping a chat window open and asking it questions. The thing that changed is not that models got smarter in the abstract. The thing that changed is that agents can now operate software the way an employee does : click through admin dashboards, fill in government forms, read email, write and deploy code, remember what happened last Tuesday, and run on a schedule without being asked. Once that is true, most of what a small company's staff does becomes a workflow you can write down, hand to an agent, and audit weekly instead of doing daily. Everything I run sits on five primitives. Tool names will churn every six months. These won't. 1. A browser-operating agent An agent that drives a real browser session with my real logins: seller dashboards, banking portals, government sites, ad platforms, email. This is the highest-leverage primitive, and it is the one most people skip. The reason is uncomfortable: roughly 90% of small-business operations live behind a login wall with no usable API. Your marketplace seller console. Your payment provider's merchant dashboard. Your country's tax portal. The grant program that still ships application forms as attachments. If your automation strategy requires an official API for everything, you will automate the 10% that already had one, and you will still be doing the other 90% by hand at 11pm. The browser agent is my hands. It logs in, navigates, reads what's on screen, fills forms, downloads documents, and reports what it found. 2. A coding agent An agent that reads my repositories, writes changes, opens a review pass, and deploys. I treat it exactly like a

Microsoft bolstered its AI cybersecurity offerings this week with the launch of its first AI security model and a new security platform.

Here's what the rumor mill tells us Google might be preparing for its next Pixel launch.

Model Context Protocol servers have quickly become the connective tissue between AI agents and the outside world — file systems, databases, APIs, internal tools, you name it. That convenience comes with a catch: the tools, prompts, and schemas an MCP server exposes are a new kind of attack surface, and most traditional scanners simply don't look there. MCPRadar is an open-source project built specifically to close that gap. Why this matters A recent academic study examining nearly 1,900 MCP servers found meaningful security issues in a surprising share of them — general vulnerabilities in roughly 7% and MCP-specific tool poisoning in another 5%. Tool poisoning, prompt injection hidden in tool descriptions, and quietly over-permissioned configurations are easy to miss because they don't look like a "normal" vulnerability — there's no CVE, no obvious buffer overflow, just a tool description that quietly tells an agent to do something it shouldn't. MCPRadar's whole premise is that this class of risk deserves the same rigor as any other part of your CI pipeline. What it actually scans MCPRadar isn't a single-purpose linter — it looks at an MCP server from several angles: Protocol inspection — enumerates tools, prompts, resources, and templates the server exposes, and checks server instructions for suspicious content. Source analysis — walks Python and JavaScript/TypeScript code looking for SSRF, unsafe deserialization, command/SQL injection, Trojan Source tricks, and mismatches between a tool's description and what its code actually does. Configuration review — flags poisoned MCP or agent configuration files, risky hooks, and overly broad permission grants. Supply chain checks — fetches packages without running install scripts, cross-references dependencies against OSV, and can emit a CycloneDX SBOM with hashes and provenance. Change monitoring — stores snapshots in SQLite and diffs them over time, classifying changes as cosmetic, behavioral, or security-relevant so sil

Every log has a story خب، روز سوم الان که دارم این لاگ رو می‌نویسم، روز تقریبا تموم شده و امروز از اون روزهایی بود که حس خوبی داشت اگر بخوام بهش نگاه کنم، می‌تونم بگم یکی از روزهای خوب ۱۴۰۵ بود البته امیدوارم بهترینش نباشه چون هنوز کلی برنامه داریم، کلی چیز هست که باید ساخته بشه و کلی روز بهتر قراره بیاد یک پیام غیرمنتظره دیروز ساعت 17:54 یک پیام دریافت کردم راستش آن لحظه خیلی آماده جواب دادن نبودم من معمولا پیام کسی را بی‌جواب نمی‌گذارم، حتی اگر ناراحت باشم یا فاصله‌ای ایجاد شده باشد اما بعضی وقت‌ها آدم نیاز داره سکوت کنه چند دقیقه، چند ساعت یا حتی بیشتر نه برای نادیده گرفتن، فقط برای اینکه بتونه با ذهن آرام‌تر و بی کینه تر جواب بده تایم نهار تصمیم گرفتم بخونم و جواب بدم هر چی نباشه اون کسی بود که موقع اعتراضات حالم رو پرسید حتی وقتی راجع به اون روز جمعه نکبت بار شنید با خطرات اون روزا برای عیادت من اومد ای کاش اون روزا بجای ساچمه تیر میخوردم ای کاش بعد دیدن اون کشتار من هم زنده نمی موندم شاید غریبه باشیم شاید دلخور باشم ولی خب هرگز خوبی ادم ها رو فراموش نمیکنم پیام طولانی بود خیلی طولانی و این دقیقا چیزی است که همیشه دوست داشتم آدم‌هایی که من را می‌شناسند می‌دانند که خودم هم معمولا جواب‌های کوتاه نمی‌دهم به نظرم نوشتن زیاد همیشه به معنی زیاد حرف زدن نیست گاهی یعنی برای توضیح دادن، برای فهمیده شدن و برای احترام گذاشتن وقت گذاشتی و این پیام هم همین حس را داشت نه فقط دفاع از خودت بلکه تلاش برای فهمیدن و توضیح دادن مرور خاطرات بعد از خواندن پیام، چند سال گذشته دوباره مرور شد بعضی آدم‌ها و بعضی روزها، حتی بعد از گذشت زمان، یک گوشه از ذهن باقی می‌مونن سال‌هایی که گذشت از ۱۳۹۹ تا ۱۴۰۲ سال‌هایی پر از تغییر، تجربه و اتفاق‌های مختلف پونه فرزانگان اختیاریه خب خیلی گذشته احساس پیری میکنم سال ۱۴۰۳ ارتباط کمتر شد و هر کسی مسیر خودش رو دنبال کرد تو کنکورت و من هم درگیر درد و دل با این باینری ها بودم اردیبهشت ۴۰۴ اتفاق‌هایی افتاد که شاید بهتر باشه فقط به عنوان تجربه به اون نگاه کنیم نه چیزی که هر روز دوباره مرور شه گاهی گذشته رو نمیشه تغییر داد و خب هممون اشتباه میکنیم بعد دوباره رسیدیم به یک نقطه جدید از ۴فروردین تا ۲۰ اردیبهشت امسال خب بازم شاید همون طوری که امروز گفتی هردو یه

Introduction Hello from Japan! 🇯🇵 I am a professional truck driver teaching myself Python and web development while working toward a career transition into web engineering. This article records what I learned after approximately 122 hours of programming study , starting on May 12, 2026. Recently, I added a ripple animation effect to the answer buttons in my self-developed application: 🚛 DPT — Driver Personality Test https://qiita.com/tosane932/items/220d0f7d36bd79b2aa81 At first, I thought it would be a small visual improvement. However, while implementing it, I realized that the blinking light on my toilet control panel and a JavaScript flag named isProcessing were performing exactly the same role. This article explains that connection. It Started as Protection Against Repeated Clicks In DPT, clicking an answer button moves the user to the next question. In the original version, the next question appeared immediately after the button was clicked. However, this created a problem. If a user repeatedly clicked the button, the application continued advancing through the questions at the same speed. In an extreme case, someone could finish all 50 questions in only a few seconds. That would reduce the reliability of the personality test and could also create invalid answer records. To prevent this, I introduced a processing-state flag . let isProcessing = false ; testContainer . addEventListener ( " click " , ( event ) => { if ( isProcessing ) return ; const button = event . target . closest ( " .option-btn " ); if ( ! button ) return ; isProcessing = true ; createRipple ({ currentTarget : button , clientX : event . clientX , clientY : event . clientY }); const qIdx = Number ( button . dataset . qIndex ); const oIdx = Number ( button . dataset . oIndex ); setTimeout (() => { if ( oIdx === - 1 ) { handleAnswer ( qIdx , - 1 , " No answer " , 0 ); } else { const option = shuffledQuestions [ qIdx ]. shuffledOptions [ oIdx ]; handleAnswer ( qIdx , oIdx , option . text , optio