标签：#X

A lot of Linux incident response starts with a login question, not a malware sample. Someone sees a spike of failed SSH attempts. A root login appears in the wrong time window. A service account logs in from an address nobody recognizes. A helpdesk ticket says "the server looks weird" and the only concrete clue is a username or IP address. At that point, the useful question is not "is this host compromised?" It is more boring and more important: Did anyone actually authenticate? Which account was involved? Was it password, key, sudo, su, or a scheduled task? Was the same IP seen in web logs, current sockets, process context, or command history? Did persistence, services, packages, or recent files change near the same time? Can another responder review exactly what evidence was collected? That last point matters. If you let an AI assistant freely run shell commands during the first pass, you can get speed, but you also create a new risk: the model may over-collect, mutate the host, or produce a confident answer that nobody can audit later. For a login anomaly, I prefer a read-only evidence loop. A practical first pass Start with the narrow clue if you have one. If the alert names a user: oi login --user root -s 7d If the alert names an IP address: oi login --ip 203.0.113.44 -s 7d If the alert is vague, start wider: oi login -s 7d oi scan -s 7d The goal of the first pass is not to prove every detail. The goal is to build a timeline that a human responder can challenge. For a suspicious SSH login, I want the initial report to answer five things. 1. Authentication pattern Look for the difference between noise and access. A server can receive thousands of failed SSH attempts from the internet. That is useful background, but it is not the same as a successful session. The first split should be: failed attempts only successful login after many failures accepted key from an unusual source login by an account that normally should not be interactive root login where root SSH

The Cloud Native Computing Foundation has highlighted a new AI-assisted migration approach that enabled engineers to migrate 60 ingress-nginx resources to Higress in roughly 30 minutes, demonstrating how artificial intelligence is increasingly being applied to modernize Kubernetes networking and gateway infrastructure. By Craig Risi

Sex workers appear on the livestreams of famous manosphere influencers to boost their followings—but often end up being degraded.

TL;DR -DevRelCon NYC returns to Industry City in Brooklyn on July 22–23, 2026. It's the flagship...

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built I built and revived Mandi Central , a complete business operations and billing system for mandi-style trading businesses. Live Project: https://mandicentral.in Mandi Central helps manage daily mandi operations like purchases, farmer purchase entries, sales entries, payments, bank and cash ledgers, accounting heads, reports, invoices, outstanding balances, and financial statements from one structured platform. This project was not started as a simple demo. It was a real business-focused software project, but it had many unfinished parts, broken flows, missing reports, pending PDF generation, incomplete mobile API planning, and several UI and accounting issues. For the GitHub Finish-Up-A-Thon Challenge, I brought it back, fixed the pending issues, completed the core business flow, and made the project live on mandicentral.in . Demo Live Website: https://mandicentral.in GitHub Repository: https://github.com/hiijoshi/bill Screenshots and demo cover the completed modules: Business Operations Hub Sales List Bank reconciliation Cash ledger Bank ledger Sales entry Purchase entry Farmer purchase entry Bulk PDF download Accounting reports Party ledger redirection Outstanding reports Mobile API-ready backend The Comeback Story Mandi Central started with a strong idea: create one platform where mandi businesses can manage their complete daily workflow. But before this challenge, the project was stuck with many incomplete and pending items. There were more than 40 pending issues across PDF generation, sales invoices, bank ledger, cash ledger, accounting reports, outstanding reports, purchase calculations, sales calculations, mobile APIs, AWS deployment, domain setup, and UI fixes. Some of the major problems before completion were: PDF generation was not working properly. Bulk sales bill download was missing. Mobile application APIs were pending. Farmer Purchase Entry API was pending. Purchase Entry API w

When a team hits a ceiling with their coding agent, the first instinct is to reach for a better model. The reasoning feels obvious: the model is the part that produces the code, the code is the part that is wrong, therefore a smarter model will produce more correct code. Wait for the next release. Switch providers. Bump the tier. This is sometimes right. It is much more often wrong, and the cost of being wrong about it is that you spend months waiting for a model upgrade to solve a problem the model was never the cause of. The harness is the cause of the problem more often than the model. Most teams discover this only after they have exhausted the model-upgrade reflex and finally turn to look at everything else. What a model upgrade actually buys you Newer, stronger models do tangibly improve some things. They handle longer contexts more reliably. They make fewer simple reasoning errors on complex tasks. They follow nuanced instructions more closely. On a fixed prompt, with a fixed task, a better model produces a better answer. What model upgrades do not change: The fact that the agent has no idea your team prefers functional components over class components, because the convention is not in any file the agent reads. The fact that your tests do not actually fail when the code is wrong, so the agent can ship broken code that passes CI. The fact that your codebase has three different ways of handling errors and the agent picks one at random on each PR. The fact that the rule the senior engineer keeps repeating in reviews is not encoded anywhere the next session can see. None of these are fixed by a smarter model. They are fixed by a better harness. A smarter model loaded into the same broken harness will produce slightly more sophisticated versions of the same problems. The diagnostic The diagnostic is one question: when the agent fails, does it fail because it lacked information, or because it lacked capability? A capability failure looks like: the task required reas

There are strategies to improve healthcare, but US isn't trying them.

A new hacking campaign is trying to trick Signal users to give up their secret recovery key, which can be used to access online backups containing past messages.

The same agent, same prompts, did markedly different work on two codebases I work in. One has a test suite that runs in eight seconds. The other takes twelve minutes. The eight-second project gets a careful, iterative collaborator. The twelve-minute project gets a confident guesser. I noticed it first as a vibe. The agent in the slow codebase would write five files at once, then announce the task complete without having run anything end to end. The agent in the fast codebase would write one function, run the tests, react to the failure, fix it, run them again. Same model. Same configuration. The only difference was how expensive it was to learn whether the previous step was right. That is the whole post in one sentence. An agent's effective intelligence is bounded by how fast it can verify its hypotheses. Cut the verification cost and you raise the agent's apparent IQ. Raise it and you lower the agent's apparent IQ. The model in the middle is unchanged. Why this binds harder for agents than for humans A human engineer can hold a hypothesis in their head. "I think this works. I will check it later." The cost of holding the hypothesis is roughly free; the human has institutional memory, intuition, a sense of what the code does that does not require running the code to confirm. They can defer verification without losing fidelity. An agent cannot. It has no intuition about your codebase. The only ground truth it has access to is what the tests say, what the type checker says, what the build says. When those signals are cheap, the agent uses them constantly. When they are expensive, the agent stops using them and starts speculating. Speculation by an agent looks plausible. It produces code that compiles, follows the patterns it has seen in your repository, names things sensibly. The problem is that plausible is not the same as correct. The agent that speculates is shipping a guess; the agent that iterates is shipping a tested answer. From the diff alone, they can be hard

Every Linux PHP developer knows the dance. You need to switch from PHP 8.1 to 8.3. You run your sudo commands, update your global symlinks, and then realize your local development server in the other window just crashed because it was running on the old version. Why should managing PHP versions be a system-wide struggle? The Solution: Per-Shell Version Isolation phpvm brings the seamless developer experience of tools like pyenv , rbenv , or nvm to the PHP ecosystem on Linux. Instead of changing /usr/bin/php globally, it uses a lightweight shim directory prepended to your PATH . When you call php , the shim inspects your environment variables and forwards the execution to the correct binary. It supports three layers of resolution, falling back gracefully: Shell pin : Pinned manually via phpvm shell <version> Project default : Resolved from .php-version or composer.json requirements when you cd into a directory Global default : The system fallback managed by update-alternatives Effortless Provisioning No need to look up repository installation guides. The built-in installer automatically detects your distribution (Ubuntu or Debian) and configures the appropriate upstream repositories (Ond?ej Sur�'s PPA or deb.sury.org ) to fetch the exact CLI and FPM packages you need. Polish in v2.5.1 Our latest release focuses on making the environment rock-solid: Tray App Auto-Start : Spawns the GTK desktop tray app immediately after installation by resolving the graphical session environment from active processes. PATH Priority : Actively prevents IDEs, login shells, or snap profiles from overriding the shim's position in PATH . Clean Cleanup : Ensures all background processes are terminated during uninstallation. Getting Started You can install or upgrade using the interactive script: curl -fsSL https://raw.githubusercontent.com/rijverse/phpvm/main/install.sh | sudo bash If you are already running v2.5.0, simply run: phpvm --self-update Check out the project website, or find the

Pay Tel secured the publicly exposed data after security researchers discovered the leak containing callers' sensitive ID documents and inmate communications.

The launch of the Ojai minivan robotaxi comes after years of development and testing, but arrives amid a challenging time for Waymo.

Elon Musk is publicly reframing xAI’s massive Anthropic compute deal as short-term and cancellable, despite SpaceX’s own S-1 filing describing payments through May 2029.

When Mozilla shut down Pocket in July 2025, I lost my favorite tool. Worse, none of the English alternatives (Instapaper, Readwise, Matter, Raindrop) had Japanese UI, and their article extraction was mediocre on Japanese pages. So I built one. It's called Readbox — Japanese-first, English-too, read-later as a PWA. Here's what I learned shipping it. The stack Next.js 15 App Router + TypeScript strict (no any ) Supabase (Postgres + Auth + RLS) Stripe (JPY + USD prices, locale-routed) Tailwind CSS Service Worker for PWA install + offline read Three things that bit me 1. Article extraction on Vercel serverless First attempt: Mozilla Readability + jsdom. Doesn't bundle on Vercel because of ESM compatibility issues and the 50MB serverless function size limit. I tried 6 approaches — Webpack externals, dynamic imports, edge runtime — none worked cleanly. Ended up using Jina Reader , which returns clean Markdown/HTML from any URL. Trade-off: third-party dependency, rate limits at scale. But it works today, and it's free. 2. Storing article body on-device I didn't want to host millions of articles' worth of HTML on Supabase (cost + privacy). Solution: extracted HTML lives in the browser's IndexedDB only (via Dexie); only metadata (URL, title, tags, read status) syncs to the server. Trade-off: cross-device sync of body content doesn't work seamlessly. Acceptable for a "read it later" workflow where you usually read on the device you saved on. 3. i18n routing — the silent sitemap killer For Japanese + English from one codebase: app/[locale]/ segment with /en prefix for English (default Japanese has no prefix, to preserve old URLs). Middleware detects cookie / Accept-Language and redirects accordingly. The gotcha (cost me a launch-day hour): middleware matcher excludes _next , api , image extensions — but if you forget .xml/.txt/.webmanifest , sitemap.xml and robots.txt get rewritten to /ja/sitemap.xml (which doesn't exist as a route → 404). Fix: export const config = { matcher

A new crop of AI labs are focused on recursive self-improvement — but the goal is proving elusive.

General Compute is betting SambaNova will be the next breakout chipmaker.

It is one thing to say AI will change the world. It is another to expect the class of 2026 to applaud it. In fact, when former Google CEO Eric Schmidt told University of Arizona graduates that their task is to help shape AI, he was met with a resounding chorus of boos. “I can…

Microsoft announced Azure Linux 4.0 and Azure Container Linux at Open Source Summit. Azure Linux 4.0 is a Fedora-based general-purpose server distribution for Azure VMs, the first time Microsoft has offered a supported Linux beyond container hosting. Azure Container Linux is an immutable container-optimized host built on Flatcar. By Steef-Jan Wiggers

Meet the wet bulb globe temperature, the ominous measure that shows when it's too hot to go outside.

Cloudflare recently added support for Claude Managed Agents, allowing developers to run and manage Claude agents within Cloudflare. Developers can connect agents to private systems, choose their runtime environment, and monitor agent activity using Cloudflare services. By Renato Losio