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There are exactly three sensible ways to get an SVG icon into a React codebase: paste it inline as a component, import the file through a build transform like SVGR, or reference it from a sprite. Most projects need only the first. This guide walks through the inline approach with Next.js and Tailwind specifics, and points to the deeper guides where a topic deserves its own article. Option 1: an inline JSX component Take a real icon from the catalog, convert the SVG attributes to JSX casing, and you have a dependency-free component. This is Lucide's search icon, exactly as it ships in the Lucide set , wrapped for React: export function SearchIcon ( props ) { return ( < svg xmlns = "http://www.w3.org/2000/svg" viewBox = "0 0 24 24" fill = "none" stroke = "currentColor" strokeLinecap = "round" strokeLinejoin = "round" strokeWidth = { 2 } aria-hidden = "true" { ... props } > < path d = "m21 21l-4.34-4.34" /> < circle cx = "11" cy = "11" r = "8" /> </ svg > ); } The JSX gotchas are all attribute casing: stroke-width becomes strokeWidth , stroke-linecap becomes strokeLinecap , and class becomes className . Icon pages on this site do the conversion for you: every icon offers React, Vue, Svelte, and Solid snippets next to the raw SVG, so you can copy the JSX form directly. If you have a folder of SVG files instead, the free SVG to component converter batch-converts them in the browser. Prefer importing .svg files over pasting? That is the SVGR route, covered step by step in our React with Vite and SVGR guide . Next.js: server components by default An icon component like the one above has no state, no effects, and no event handlers, which makes it a perfect React Server Component. In the Next.js App Router it renders to static markup on the server and adds nothing to the client bundle: import { SearchIcon } from " @/components/icons " ; export default function DocsHeader () { return ( < label className = "flex items-center gap-2" > < SearchIcon className = "h-5 w-5 text-zinc

Despite a lawsuit from xAI, a Minnesota ban on apps that allow users to “nudify” images can move forward.

Uber has partnered with — and in some cases made direct investments in — about 30 autonomous vehicle companies over the past two years. Here's the list and the latest on the partnerships.

From premium Shark and Dyson offerings to random Amazon devices, these mini fans will make your sweaty summer a little more managable.

A discovery in a solar system 73 light-years from Earth is challenging definitions and “blurring the lines between stars, planets, and moons.”

Word review artifacts need a CI boundary too Word already has useful interactive review workflows: legal blackline comparison and Document Inspector. But a controlled handoff or CI workflow has a different question: did this package gain unresolved revisions, comments, hidden runs, external relationships, macros, custom XML, or another opaque payload change? And can we answer that without turning a build artifact into a copy of the document? DocFence 0.1.0 is a local-first CLI for that boundary. It compares .docx and .docm packages without opening Word, executing macros, evaluating fields, following links, rendering a document, or uploading source material. More than a text diff The visible body is only one stored story. DocFence inventories the body, headers, footers, footnotes, endnotes, comments, and glossary parts. It also tracks revision markup, direct hidden-text runs, field codes, content controls, Track Changes, external relationships, custom XML, and macros. A generic opaque payload signal covers mutations in parts the specialized inventories do not explain, such as styles, media, embeddings, metadata, and the package manifest. The output intentionally contains counts and fixed change categories rather than paragraphs, comments, reviewer names, URLs, relationship targets, field instructions, custom XML values, macro bytes, part paths, or fingerprints. That makes JSON, Markdown, and SARIF practical for CI artifacts while keeping source material inside the team’s environment. Policies that are small enough to review A policy is a strict, short YAML file. It distinguishes a comparison boundary from a candidate-state boundary: a team can block a newly introduced external relationship while separately requiring that the candidate contain no comments or unresolved revisions at all. version : 1 rules : no_external_relationship_changes : true no_macro_payload_changes : true no_custom_xml_changes : true require_no_unresolved_revisions : true require_no_comments : tr

Here is the whole method: get access to a real Linux machine, type commands on it for 30 to 60 minutes every day, and follow a plan that builds from navigating the filesystem up to running your own web server. Do that and you will be comfortable in four weeks and genuinely fluent in about eight. No experience required, no money required. The rest of this article is the specific plan: what to type each week, where to get a free machine you can safely break, what the three scariest errors mean, and how to tell you are actually improving. Why most people fail at Linux The pattern is nearly universal. Someone decides to learn Linux, finds a nine-hour video course, watches it at 1.5x speed, takes beautiful notes, and three weeks later cannot list the contents of a directory without checking those notes. Watching someone else type is not practice. It feels like learning because the explanation makes sense while you hear it. But command line skill is muscle memory wrapped around a mental model, and both are built one way: typing, failing, reading the error, trying again. An hour of reading about ls teaches you less than typing ls twenty times in twenty directories. Videos are fine as a preview. They are just not the workout. So flip the ratio: for every minute reading or watching, spend five with your hands on a keyboard. This article included. Read a section, then go type it. Two smaller failure modes show up almost as often. Trying to memorize everything Linux has thousands of commands. Working engineers lean hard on a core of about 25 and look up the rest without shame. The plan below teaches that core and nothing else. Fear of breaking things On a practice machine, breaking things is the goal, not the risk. A system you broke and fixed teaches more than ten flawless tutorials. Every option in the practice section makes the worst case "start over," which costs a minute. The four-week plan First, get a machine from the free options below (one minute to one afternoon, dep

This is an English translation of my original article on Qiita . An Android build failed in an Expo SDK 54 app. The project still used Expo SDK 54, but several Expo packages had been upgraded to versions intended for SDK 55. TypeScript checks passed, and the development server ran normally. I did not catch the mismatch until EAS Build reached the native build step. What the dependency list looked like The relevant part of package.json looked like this: { "dependencies" : { "expo" : "~54.0.33" , "expo-apple-authentication" : "~55.0.13" , "expo-dev-client" : "^55.0.27" , "expo-image-picker" : "^55.0.18" , "expo-linking" : "^55.0.12" , "expo-notifications" : "^55.0.19" , "expo-splash-screen" : "^55.0.18" } } The expo package was still on version 54, while several related packages were on version 55. This happened because those packages had been installed individually using their latest versions. The package version does not always match the Expo SDK number. For example, Expo SDK 54 uses expo-notifications 0.32 and expo-splash-screen 31. Looking only at major version numbers is not enough to determine SDK compatibility. Start with expo install --check Expo CLI can compare the installed packages with the versions expected by the current SDK: npx expo install --check It can also return the result as JSON: npx expo install --check --json This is more reliable than trying to infer compatibility from package.json manually. Expo CLI can fix the versions automatically: npx expo install --fix npx expo-doctor I wanted to review each change, so I used the reported versions to update package.json myself. The versions I changed These were the main corrections: - "expo-apple-authentication": "~55.0.13" + "expo-apple-authentication": "~8.0.8" - "expo-dev-client": "^55.0.27" + "expo-dev-client": "~6.0.21" - "expo-image-picker": "^55.0.18" + "expo-image-picker": "~17.0.11" - "expo-linking": "^55.0.12" + "expo-linking": "~8.0.12" - "expo-notifications": "^55.0.19" + "expo-notifications"

This article was originally published on e-shikumi-labo . Hello, I'm Shin from e-Shikumi-Labo. This is the final installment (Part 4) of "Systematized Thinking," where we use AI to build our own tools and independently maintain them. So far, we have discussed creating a prototype that automatically saves Gemini chat logs, converting them to Markdown for Obsidian integration, and elevating it to a safe, fully automated system. In this final installment, we will cover the "countermeasures for downtime due to screen specification changes," an unavoidable issue when operating tools that handle web data, and the core of the "self-reliance" humans should possess in the AI era. 1. The Web Data Extraction Compromise: "You Can't Extract What Isn't on the Screen" During development, there was a time when I thought, "I also want to record the exact date and time (timestamp) when the chat was sent." However, no matter how much I analyzed Gemini's screen structure, the exact timestamp of each utterance did not exist in the HTML. The fundamental rule of web data extraction is: "You cannot extract data that does not exist on the browser screen." As long as you are extracting data from the screen (DOM) rather than via an API, forcing the extraction of something that isn't there will require complex guesswork processes and will instead become a cause of trouble. Understanding this "technical limit," gracefully giving up on what cannot be done, and judging to maintain simplicity is also an important element of tool building. 2. Specification Changes Are Not Defects, But "Fate" As long as you deal with tools that extract data from other people's websites, the time will inevitably come when the tool suddenly stops working one day due to design changes or updates on Google's side. "It was working fine until yesterday, but suddenly it stopped saving." This is not a defect in the tool, but an unavoidable "fate" as long as you depend on someone else's platform. The important thing is not t

This article was originally published on e-shikumi-labo . Hello, I'm Shin from e-Shikumi-Labo. This is Part 3 of "Systematized Thinking," where we use AI to build our own tools and independently maintain them. Last time, I talked about creating a system to automatically output Markdown (.md) files to Google Drive simultaneously with appending to a spreadsheet. With list management in a spreadsheet and a comfortable viewing environment in Obsidian established, it was getting very close to completion as a tool. However, as I continued to use it practically, new challenges emerged on the operational front. This time, I will share the risks I faced while transitioning from a "manual button" to "full automation," and the process of evolving into safe code. 1. I Want to Eliminate the "Hassle of Pressing a Button" During the prototype stage, the system was designed so that logs were saved by pressing a button placed on the screen. However, as long as a human operates it manually, there are inevitably limitations. If you are concentrating on the conversation, you might forget to press the save button and close the screen. If the conversation gets long, you might miss past utterances that are no longer displayed on the screen. "If I have the screen open and am conversing, I want it to automatically save in the background without bothering human hands." Thinking this, I asked the AI to write the code for full automation. 2. The Code the AI Produced: "Patrolling the Screen Every 1.5 Seconds" When I consulted the AI, it immediately presented code for full automation. The mechanism was, "Start a timer every 1.5 seconds, check the entire screen in the background, and send any new utterances." When I actually tried it, the logs accumulated automatically as soon as I conversed without pressing the button, and at first glance, it looked like exceptionally well-done full automation. However, I felt something was slightly off regarding this "monitoring on a 1.5-second cycle." 3. The B

The impact will kick up a plume of debris so high, it’ll likely be visible through some telescopes. Astronomers will be watching.

Also has big plans beyond the TM-B. The startup mostly refers to itself as a "vehicle" company, and has plans to make four-wheel pedal-assist cargo vehicles for Amazon.

How to Verify a SHA-256 Checksum on Windows, macOS, and Linux You download an ISO, installer, archive, or release binary. The publisher provides a long value such as: 9f86d081884c7d659a2feaa0c55ad015 a3bf4f1b2b0b822cd15d6c15b0f00a08 That value is a checksum, usually generated with SHA-256. Verifying it answers one practical question: Does the file you downloaded have exactly the same contents as the file the publisher hashed? A checksum mismatch can indicate a damaged download, an incomplete transfer, the wrong file version, or modified contents. Before verifying anything Get the expected checksum from a source you trust. Ideally, use the software publisher’s official website, release page, package repository, or signed checksum file. A matching checksum confirms that your file matches the data represented by the expected hash. It does not prove that the original publisher or website was trustworthy. If an attacker can replace both the download and the displayed checksum, they can make the two values match. For stronger authenticity verification, use a signed release when the publisher provides one. Verify SHA-256 on Windows Open PowerShell in the folder containing the downloaded file. Run: Get-FileHash ".\filename.iso" -Algorithm SHA256 Example: Get-FileHash ".\ubuntu.iso" -Algorithm SHA256 PowerShell returns something similar to: Algorithm : SHA256 Hash : 4A1F... Path : C:\Users\You\Downloads\ubuntu.iso Compare the value beside Hash with the checksum published by the download provider. Uppercase and lowercase letters do not matter in hexadecimal hashes. The characters themselves must otherwise match exactly. Compare automatically in PowerShell Instead of comparing two 64-character values manually, store the expected checksum and let PowerShell compare them: $expected = "PASTE_EXPECTED_SHA256_HERE" $actual = ( Get-FileHash ".\filename.iso" -Algorithm SHA256 ) . Hash if ( $actual -eq $expected ) { Write-Host "Checksum matches" } else { Write-Host "Checksum does not

The new funding brings Index's total available investing capital to $3.5 billion.

The Situation Our team's CI/CD pipeline on Azure DevOps was taking 15 minutes to complete on every push to develop. You'd merge a PR, grab a coffee, come back — and it was still running. A 15-minute feedback loop breaks flow state — by the time the pipeline finishes, you've already switched context twice and forgotten what you were checking. I spent an afternoon digging into the Azure DevOps logs. Here's what I found. The Numbers (Before) Artifact content (uncompressed): 1,218 MB (1.2 GB) Artifact downloaded (compressed): 614 MB Download time: 3-4 min Pipeline breakdown: Build stage: ~5 min (Docker build + artifact) Download artifact: ~3 min (614 MB over the wire) Configure App Service: 2m54s (5 Azure API calls) Deploy (AzureWebApp@1): ~1 min Validate: 2m07s (sleep 30 + 3×30s probes) ───────────────────────────────── Total: ~15 min Root Cause #1: Ignoring output: 'standalone' next.config.js had this: const nextConfig = { output : ' standalone ' , // ← was there the whole time ... }; output: 'standalone' tells Next.js to produce .next/standalone/ — a self-contained directory with only what's needed at runtime. Trimmed node_modules . Auto-generated server.js . No source files. No dev dependencies. But the pipeline was ignoring it: # Old pipeline — copies everything from Docker docker cp deployImage:/app/node_modules . # 600 MB 😱 docker cp deployImage:/app/src . docker cp deployImage:/app/.next . docker cp deployImage:/app/server.js . # ... more files /bin/zip -r deploy.zip .env .next public node_modules package.json \ next.config.js jsconfig.json postcss.config.mjs decs.d.ts src server.js # Then published the ENTIRE working directory as the artifact - task : PublishPipelineArtifact@0 inputs : targetPath : ' $(System.DefaultWorkingDirectory)' # 1.2 GB of loose files + zip Azure DevOps compressed this to 614 MB for transfer. The deploy stage downloaded 614 MB to use a 24 MB zip buried inside it. The fix: # New pipeline — standalone only docker cp deployImage:/app/.next/

Last week I was doing a routine check on a CyberPanel server and noticed something that didn't add...

moin, ich möchte euch mein aktuelles Projekt vorstellen: Die Wiederbelebung eines Alienware m17x (Baujahr 2008) als dedizierte DJ-Workstation unter Linux. Ziel war es, alte Hardware nachhaltig zu nutzen und eine stabile Umgebung für Mixxx zu schaffen. Die Hardware: Notebook: Alienware m17x (Core 2 Duo, 4GB RAM, SSD) OS: KDE Neon mit Low-Latency-Kernel (6.8.0) Software: Mixxx 2.4 Audio-Interface: Günstiges USB-Audio-Device für den Master-Ausgang Das Problem: Mixxx verweigerte unter ALSA den Dienst mit der Fehlermeldung: Error opening "USB Audio Device (hw:1,0)" - Invalid sample rate Die Analyse über /proc/asound/card1/stream0 zeigte die Ursache: Das USB-Gerät unterstützt ausschließlich 46875 Hz – eine für Audio-Interfaces sehr unübliche Rate, die weder 44100 Hz noch 48000 Hz entspricht. Der direkte Zugriff über hw:CARD=Device,DEV=0 schlug fehl. Die Lösung: Die Rettung war die Aktivierung der ALSA-Plug-Erweiterung über PipeWire/ALSA, die eine automatische Sample-Rate-Konvertierung erlaubt. Starten Sie Mixxx nicht direkt, sondern setzen Sie zuvor die Umgebungsvariable: export PA_ALSA_PLUGHW=1 mixxx Damit Mixxx auch dauerhaft korrekt startet (z.B. über das KDE-Menü), habe ich den Starter wie folgt angepasst: bash -c "export PA_ALSA_PLUGHW=1; mixxx" Ergebnis: ✅ Master-Ausgabe über das USB-Device funktioniert stabil. ✅ Kopfhörer-Vorhören (C-Media USB Headphone Set) läuft parallel. ✅ Das System läuft trotz des Alters der Hardware (2008) flüssig und mit geringer Latenz. Die vollständige Dokumentation inklusive Fotos des Umbaus, der genauen Kernel-Einstellungen und der Konfiguration findet ihr in meinem Open-Source-Repository: 👉 [ https://github.com/qrishii/DJ-Installationen ] Ich hoffe, diese Lösung hilft anderen, die ähnliche Probleme mit exotischen USB-Audio-Raten unter Linux haben! das Leben ist lustig

Imagine you have a friend who only understands the 26 English letters (A-Z), numbers (0-9), and a dash (-). Now imagine another friend wants to write their name as "José" or "你好" or "🍕". The first friend doesn't understand those special letters or emojis. So, you use a secret translator that changes them into something the first friend can understand. That's what Punycode does. A simple example Suppose someone wants a website with the domain: münchen.com The internet's basic domain name system can't directly understand the ü. So Punycode converts it into something like: xn--mnchen-3ya.com Both mean the same website. What you see: münchen.com What computers use: xn--mnchen-3ya.com The xn-- at the beginning tells computers: "Hey! This is Punycode. Decode it back into the real name." Think of it like nicknames Imagine your teacher can't pronounce your name, Tolúwàní. She writes it in a way she can pronounce, but everyone knows it's still you. Punycode does the same thing for website names. Why is it needed? Without Punycode, websites could only use simple English letters like: google.com ✅ example.com ✅ With Punycode, people can have websites in their own languages, such as: café.com mañana.com 中国.com Ελλάδα.gr Behind the scenes, those are converted into Punycode so the internet can route them correctly. One thing to be careful about Sometimes bad people create fake websites that look almost identical to real ones by using letters from other alphabets. For example: apple.com (real) аpple.com (the first "a" is actually a Cyrillic letter, not the English "a") Although they look almost the same, they're different domains. Browsers often show the Punycode version (starting with xn--) instead, helping users spot suspicious domains. In one sentence Punycode is a translator that lets the internet use website names with letters from any language while still speaking the simple alphabet that computers expect.

Filmmaker sues Netflix over stolen screener of unreleased Nicolas Cage movie.

The leading U.S. automakers are mentioning EVs on their investor calls at pre-pandemic rates, according to new data from TechCrunch and Hudson Labs.