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Here is a step-by-step guide to configuring your Playwright/Cucumber test suite to run on LambdaTest Cloud via Azure DevOps pipelines, returning test results directly to Azure. 1. Prerequisites A GitHub repository containing your Playwright, Cucumber, and JavaScript automation code. An active Azure DevOps account with a project created. A LambdaTest account (you will need your username and access key). 2. Connect GitHub to Azure DevOps In Azure DevOps, navigate to Pipelines > New Pipeline. Select GitHub as the source and authenticate your account. Choose your repository and target branch (e.g., main). 3. Create LambdaTest Credentials Variable Group Go to Pipelines > Library in Azure DevOps. Click + Variable group and name it LambdaTest-Credentials. Add the following key-value pairs: LAMBDATEST_USERNAME = your_lambdatest_username LAMBDATEST_ACCESS_KEY = your_lambdatest_access_key (toggle "Keep this value secret") Save the group. 4. Add/Update Your azure-pipelines.yml Place this configuration file in your repository root directory: trigger : - main pool : vmImage : ' windows-latest' variables : - group : LambdaTest-Credentials - name : BASE_URL value : ' https://your-app-url.com' - name : LT_BROWSER value : ' chrome' - name : ENABLE_LAMBDATEST value : ' true' stages : - stage : Test jobs : - job : UITestsLambdaTest displayName : ' UI Tests (LambdaTest Cloud)' steps : - task : NodeTool@0 inputs : versionSpec : ' 20.x' displayName : ' Install Node.js 20.x' - script : npm ci displayName : ' Install Dependencies' - script : npm run test:ui:smoke displayName : ' Run UI Smoke Tests on LambdaTest' env : ENABLE_LAMBDATEST : ' true' LT_USERNAME : $(LAMBDATEST_USERNAME) LT_ACCESS_KEY : $(LAMBDATEST_ACCESS_KEY) LT_BROWSER : $(LT_BROWSER) BASE_URL : $(BASE_URL) - task : PublishTestResults@2 condition : always() inputs : testResultsFormat : ' JUnit' testResultsFiles : ' reports/junit-report.xml' testRunTitle : ' UI Tests - LambdaTest Cloud' 5. Update Your Test Code Ensure your tes

var is one of the ways to create a variable in JavaScript. A variable is a place to store a value, like a name or a number. var is mostly seen in old JavaScript code, written before 2015. Today most people use let and const instead, but it still helps to know var , especially when reading old code. Creating a Variable var name = " Abishek " ; var age = 22 ; console . log ( name ); console . log ( age ); Here, name stores "Abishek" and age stores 22 . We Can Change the Value var age = 22 ; age = 23 ; console . log ( age ); The output is 23 . The value inside age got updated. We Can Also Create it Again We can create the same variable a second time with var , and JavaScript does not give an error. var name = " Abishek " ; var name = " Abi " ; console . log ( name ); The output is Abi . It just overwrites the old value. It Works Across the Whole Function A block is a small part of code inside { } , like an if statement. var does not care about these small blocks, it only cares about the function. function test () { if ( true ) { var x = 10 ; } console . log ( x ); // works fine } test (); Even though x was created inside the if part, we can still use it outside the if , as long as we are inside the function. Hoisting console . log ( x ); var x = 10 ; You might expect an error here, but the output is undefined . This is because JavaScript moves the var declaration to the top before running the code. This is called hoisting. Why var Isn't Used Much Now Most people use let and const instead of var , because var can cause confusing bugs like accidental redeclaration and hoisting. let is used when the value can change, and const is used when it should not change. In Short var was the first way to create variables in JavaScript. It can be changed, redeclared, and it works across the whole function instead of one block. Once you understand var , let and const become easier to learn.

Time zones look like a tidy grid of whole hours. They aren't. I read the standard UTC offset of all 162 cities, countries and regions on our World Clock straight from the IANA database (via Intl ) — and the real shape is lumpy, with quarter-hour outliers and a near-even split over whether clocks move at all. The quirk, in one line: Kathmandu keeps its clocks 5 hours 45 minutes ahead of UTC — the only :45 offset on the board, and one of 11 places out of 162 that don't sit on a whole hour. Nearly half the rest never move their clocks at all. The clocks that don't sit on the hour Most of the world rounds to a whole hour from UTC. A handful don't: Offset Places UTC+3:30 Tehran (Iran) UTC+4:30 Kabul (Afghanistan) UTC+5:30 India — New Delhi, Mumbai, Kolkata, Bengaluru, Hyderabad UTC+5:45 Kathmandu (Nepal) UTC+9:30 Adelaide, Darwin (Australia) Half-hour and quarter-hour offsets are a reminder that a time zone is a political decision, not an astronomical one — which is exactly why date code should read the IANA database rather than dividing longitude by 15. Nearly half never change their clocks Daylight saving feels universal if you live in North America or Europe, but it isn't. Of the 162 places tracked, 87 (54%) shift their clocks and 75 (46%) never do . The whole of East Asia, the Gulf, most of Africa, India and much of South America keep one fixed offset year-round — Tokyo, Singapore, Dubai, Nairobi and New Delhi never spring forward. Where the clocks crowd together Offsets aren't evenly populated. Four of them carry nearly half the board: Offset Places Who's there UTC−5 25 US Eastern — New York, Toronto, Miami, Boston UTC+1 21 Central Europe — Paris, Berlin, Rome, Madrid UTC−6 14 US Central — Chicago, Dallas, Mexico City UTC+2 12 Eastern Europe & Africa — Athens, Cairo, Johannesburg The full set spans 22 hours , from Honolulu at UTC−10 to New Zealand and Fiji at UTC+12. Reproduce it Every number here is printed by one dependency-free Node script that reads each place's

GitHub热门项目 | BetterGI 的脚本仓库，内含BetterGI 的JS脚本、路径追踪、战斗策略、七圣召唤策略。 | Stars: 515 | 2 stars today | 语言: JavaScript

GitHub热门项目 | یک پنل گرافیکی کاربردی برای ارائه اشتراک‌های Worker با پروکسی‌های ، Trojan و Warp به همراه زنجیره پروکسی، ارائه دهنده تنظیمات کامل DNS، IP تمیز و روتینگ پیشرفته برای کاربران تمامی پلتفرم‌ها با استفاده از هسته‌های Amnezia، Wireguard، Sing-box، Clash/Mihomo و Xray. | Stars: 3,039 | 24 stars today | 语言: JavaScript

Disclosure: I build Receiptful, the printing API used in this tutorial. The Node and Express parts apply whatever you print with. You have orders coming into your point of sale, and you want each one to print on the thermal printer at the counter. This is a complete walkthrough of a small Node service that does exactly that. By the end you will have an endpoint you can POST an order to and watch paper come out. There is nothing to install next to the printer for this tutorial to work, and no ESC/POS to write by hand. You send HTML, Receiptful prints it. Before you start You need two things from the console : A paired printer, which gives you a printer ID . If you have not done this yet, the getting started guide walks through it in a couple of minutes. An API key (the rf_live_… value), created under API keys and shown only once. On the code side you need Node 18 or newer, so that fetch is available globally with no extra dependency. We will use TypeScript, but the same code works in plain JavaScript if you drop the types. Put your credentials in the environment rather than in the source: export RECEIPTFUL_API_KEY = "rf_live_3f9c…" export RECEIPTFUL_PRINTER_ID = "42" Step 1: model the order Start with the shape of an order. Yours will have more fields, but this is enough to print a useful receipt: interface LineItem { name : string ; quantity : number ; unitPrice : number ; // in cents, to avoid float rounding } interface Order { id : number ; items : LineItem []; placedAt : Date ; } Keeping money in cents and formatting only at the edges saves you from the classic floating point rounding bugs that show up as a receipt total that is one cent off. Step 2: render the order as HTML This is the part that decides how the receipt looks. Receiptful converts the HTML you send into ESC/POS for your specific printer, so you get to lay a receipt out with tags you already know instead of byte codes. function money ( cents : number ): string { return " $ " + ( cents / 100 ). toFi

A game wallet often starts as a single balance field. That is fine for a prototype, but payment retries and unreliable networks quickly make the number hard to trust. A player can tap “buy,” lose the connection, and try again. A store callback can arrive more than once. Two devices can spend the same account at nearly the same time. The fix is to treat the balance as a cached view of a ledger. Record every change Instead of silently changing a balance, record events such as: a verified payment granting virtual currency; an item purchase spending currency; a refund creating a compensating entry; an administrative adjustment with an explicit reason. The current balance remains useful for fast reads, but the ledger explains where it came from. Make payment delivery idempotent A payment callback is a message that may be retried. I use an idempotency key derived from the provider and transaction ID, then enforce uniqueness in the database. The delivery flow is straightforward: Verify the external transaction. Record the payment. Grant currency with the unique key. Mark delivery complete. Return the original result for later retries. This prevents a temporary network problem from becoming a double grant. Protect spending too Client-side balance checks are useful for interface feedback, but they cannot protect an account. The server should lock the wallet row, check the available amount, write the ledger entry, and update the cached balance in one transaction. The same request key should return the original purchase result instead of charging twice. Keep payment, wallet, and item orders separate A real-money payment, a virtual-currency movement, and item delivery are connected but different events: payment order → wallet grant → item order → wallet spend That separation makes refunds and reconciliation much easier. When something goes wrong, support can identify which step is missing instead of guessing from one mutable number. Tests that expose the real failures Before bu

I spent a while building browser-based hardware diagnostics and came away with a much clearer sense of where the web platform is genuinely capable and where it quietly lies to you. Notes below, with live demos for each API so you can poke at them yourself. Refresh rate: requestAnimationFrame is the only signal you get There's no screen.refreshRate . The only approach is timing requestAnimationFrame callbacks and inferring the rate from the median frame delta: const deltas = []; let last = performance . now (); function tick ( now ) { deltas . push ( now - last ); last = now ; if ( deltas . length < 180 ) requestAnimationFrame ( tick ); else { const sorted = deltas . slice (). sort (( a , b ) => a - b ); console . log ( Math . round ( 1000 / sorted [ sorted . length >> 1 ])); } } requestAnimationFrame ( tick ); Two gotchas that cost me time. Use the median , not the mean — a single dropped frame wrecks an average. And browsers throttle rAF in background tabs, so the measurement is meaningless unless the tab is visible; gate it on document.visibilityState . ( live version ) Screen dimensions: four different answers, all "correct" screen.width , window.innerWidth , window.devicePixelRatio and screen.availWidth measure genuinely different things, and the one people usually want — actual native panel resolution — is screen.width * devicePixelRatio . Except that's still CSS-pixel derived, so on a scaled display it can disagree with what the panel physically is. The browser simply does not expose true hardware resolution. ( demo ) Keyboard: event.code vs event.key , and the keys you never receive event.key is layout-dependent, event.code is physical position — for a hardware tester you want code . The real limitation is that some keys never reach JS at all: PrintScreen often doesn't fire keydown , Meta combinations get swallowed by the OS, and Fn isn't a browser-visible key on most laptops. N-key rollover testing works surprisingly well though, since you just track the siz

This is a submission for Frontend Challenge - Comfort Food Edition, Perfect Landing What...

Three days ago I knew exactly what a DeepSeek call cost me. I had wired DeepSeek V4 Pro 0813 into a Spring Boot app with Spring AI, and the math was simple: $0.435 per million input tokens, $0.87 per million output tokens, and a cache-hit rate so aggressive that long agent sessions stayed embarrassingly cheap ( I wrote up the integration ). Then the pricing update landed, and tokens suddenly have rush hour. DeepSeek's official announcement introduces peak and off-peak billing: off-peak rates are 50% lower than peak, and the new prices take effect today, August 16, 2026 at 16:00 UTC (10 PM in Dhaka). The headline reads like a discount. The fine print is a price increase, and the difference matters a lot if you run batch workloads or agentic tools. Full disclosure up front: the new billing starts today, so I have not run a real bill through it yet. What I have done is read the price table carefully, watched the Hacker News thread do the math for two days, and built a scheduling pattern in Spring Boot that shifts heavy work into the off-peak window. That pattern is what I want to show you, because the interesting part is not the announcement. It is what the numbers actually mean. What actually changed The pricing page now splits every price into peak and off-peak tiers. Peak hours are 01:00 to 04:00 UTC and 06:00 to 10:00 UTC. Every other hour is off-peak, which is 17 out of 24 hours. Here are the new per-1M-token rates, straight from the page: DeepSeek V4 Flash, off-peak: $0.22 input (cache miss), $0.66 output, $0.007 cache hit. DeepSeek V4 Flash, peak: $0.44 input, $1.32 output, $0.014 cache hit. DeepSeek V4 Pro, off-peak: $0.66 input, $1.98 output, $0.022 cache hit. DeepSeek V4 Pro, peak: $1.32 input, $3.96 output, $0.044 cache hit. The off-peak discount is real: every off-peak number is exactly half of its peak counterpart, which matches the announcement's "50% lower" claim. But compare those off-peak numbers to what DeepSeek charged before this change, and the pic

Access tokens, refresh token rotation, and theft detection: the parts most Node.js tutorials leave out. A friend messaged me about his side project a few months ago: "Someone else is logged into my account. I changed my password. They're still in." He had followed the tutorials to the letter. Sign a JWT on login, send it to the frontend, keep it in localStorage , attach it to every request. Done. What none of those tutorials mentioned is that this setup has no way to un -log anyone in. A JWT is a signed piece of paper. Once you hand it over, it stays valid until it expires, and his expired in 30 days. Changing the password accomplished nothing, because the token had already been signed and nothing about it depended on the password. There was no list of active sessions to delete from. There was nothing to revoke. His only remaining move was rotating the signing secret, which logged out every user on the platform at once. That was his entire kill switch: burn it all down. This is the walkthrough I wish someone had handed me the first time I built auth. Token design, storage, refresh rotation, theft detection, the Express code, the Axios interceptor on the frontend, and the specific mistakes that turn a working login into an incident. It's long. Auth is one of those areas where the missing ten percent is the part that gets you. What the standard tutorial leaves out Nearly every "JWT authentication in Node.js" post ends in the same place: sign a token, put it in localStorage , send a Bearer header. That gets you a demo. Four things stand between that and production. localStorage is readable by any JavaScript on the page. That includes the analytics snippet you added last week, the npm package that got compromised upstream, and any XSS hole in your own code. One call to localStorage.getItem('token') and an attacker holds a working credential they can replay from their own machine. You can't detect it and you can't stop it. There is no revocation. The appeal of JWTs is st

I wanted a simple WhatsApp AI bot without paying every month for cloud hosting or an AI API. So I built one that runs on a Windows PC I already have running 24/7. The result: WhatsApp integration with Node.js Optional local AI using Ollama No VPS or cloud server required No paid AI API required Runs on Windows 10/11 Can restart automatically after a reboot «The "$0/month" refers to additional software, hosting, and AI API costs. It assumes you already have the PC, internet connection, and electricity.» The basic architecture The setup is intentionally simple: WhatsApp → Node.js bot → Local AI → WhatsApp reply The Node.js application handles incoming WhatsApp messages and decides how to respond. For AI responses, the bot can send the user's message to a locally running Ollama model and return the generated answer back to WhatsApp. That gives us: WhatsApp → Node.js → Ollama on localhost → Node.js → WhatsApp No cloud AI API is required. What you need For the basic setup: Windows 10 or Windows 11 Node.js LTS A WhatsApp account Ollama if you want local AI A computer that can stay powered on You don't need Kubernetes. You don't need AWS. You don't need Docker. And you don't need to rent a VPS. Connecting WhatsApp For this project I used "whatsapp-web.js". The first time the application starts, it displays a QR code. You scan the QR code with WhatsApp, similar to connecting WhatsApp Web. After authentication, the application can listen for incoming messages and send replies. A simplified example looks like this: const { Client, LocalAuth } = require('whatsapp-web.js'); const client = new Client({ authStrategy: new LocalAuth() }); client.on('qr', (qr) => { console.log('Scan the QR code to connect WhatsApp'); }); client.on('ready', () => { console.log('WhatsApp bot is ready'); }); client.on('message', async (message) => { if (message.body.toLowerCase() === 'hello') { await message.reply('Hello from the bot!'); } }); client.initialize(); "LocalAuth" stores the authenticated W

Start from the mismatch, because every bug in this integration comes out of it. Email hands you a MIME tree, an SMTP envelope, and a Message-ID chain that defines the conversation. Slack hands you a channel, a message of at most 50 blocks, and a ts that defines the conversation. The whole job is mapping one onto the other without dropping information — the reply chain, the authentication verdicts, the attachments — on the floor. Here's the whole inbound half, as a Cloudflare Worker. It runs as pasted with one KV namespace bound as SEEN and one dependency ( npm install mailkite ): // worker.js — inbound email → Slack. wrangler secret put SLACK_BOT_TOKEN / MAILKITE_WEBHOOK_SECRET import { MailKite } from " mailkite " ; const clamp = ( s , n ) => ( s . length > n ? s . slice ( 0 , n - 1 ) + " … " : s ); function blocksFor ( email ) { const subject = email . subject || " (no subject) " ; const trusted = email . auth . dmarc === " pass " ; const sender = trusted && email . from . name ? ` ${ email . from . name } < ${ email . from . address } >` : email . from . address ; return [ { type : " header " , text : { type : " plain_text " , text : clamp ( `📧 ${ subject } ` , 150 ) } }, { type : " section " , fields : [ { type : " mrkdwn " , text : `*From:*\n ${ clamp ( sender , 2000 )}${ trusted ? "" : " ⚠️ " } ` }, { type : " mrkdwn " , text : `*To:*\n ${ email . to [ 0 ]. address } ` }, ] }, { type : " section " , text : { type : " mrkdwn " , text : clamp ( email . text || " _no text part_ " , 3000 ) } }, { type : " context " , elements : [ { type : " mrkdwn " , text : `spf \` ${ email . auth . spf ?? " unknown " } \` · dkim \` ${ email . auth . dkim ?? " unknown " } \` · dmarc \` ${ email . auth . dmarc ?? " unknown " } \` ` }, ] }, ]; } export default { async fetch ( req , env ) { const raw = await req . text (); const sig = req . headers . get ( " x-mailkite-signature " ); // HMAC recompute, constant-time compare, ±5-minute replay window: one call if ( ! MailKite . verify

This is a submission for Frontend Challenge - Comfort Food Edition, Perfect Landing What I Built For the Perfect Landing prompt, I built Masala Dosa House , a warm and modern landing page inspired by one of my favorite comfort foods — South Indian Masala Dosa . 🇮🇳 The idea was to create a fictional restaurant website that feels like stepping into a familiar neighborhood dosa spot. The landing page focuses on: 🍽️ Hero section featuring Masala Dosa 🥞 Signature dishes 🥥 Chutneys and sambar 🌿 Traditional South Indian food experience ❤️ A warm, welcoming visual design 📱 Responsive layout for desktop and mobile ✨ Smooth interactions and animations 🎨 Food-inspired colors, typography, and visual elements 📍 Restaurant-style call-to-action sections Rather than creating a generic restaurant landing page, I wanted the entire experience to communicate the feeling behind comfort food — warmth, familiarity, and home . Demo 🍽️ Live Project: Masala Dosa House — A Taste of Home View the Masala Dosa House project on CodePen Journey I started by thinking about what makes a food website feel different from a regular landing page. For me, comfort food isn't only about the food itself. It's about the experience around it — the aroma, the warmth, the familiar presentation, and the feeling of sitting down for a meal that you already know you'll enjoy. That became the design direction for Masala Dosa House . I used a warm visual palette inspired by dosa, banana leaves, spices, chutneys, and traditional South Indian dining. The layout was designed to keep the food as the main focus while making the page easy to navigate. Building the experience I structured the landing page around a simple restaurant journey: Discover → Explore → Choose → Visit The hero section introduces the restaurant and immediately establishes the comfort-food theme. The menu section highlights signature dishes, while supporting sections provide more context about the restaurant and its food. I also focused on making the

This is a submission for Frontend Challenge - Comfort Food Edition, Perfect Landing What I Built Karachi Ki Raatein ("Karachi's Nights") — a single-page love letter to the street food that keeps my city awake after dark. Instead of a restaurant or a recipe box, I built it around a real pattern from home: Karachi basically runs on an unofficial food schedule. Maghrib means chai and something fried. Bun kabab happens standing up, mid-errand. Nihari is what you sit down for after Isha. Seekh kabab shows up wherever there's smoke. And halwa puri at 3am is for the people who never went to sleep in the first place. The whole page is built around that rhythm instead of a menu. A few things I'm happy with on the frontend side: A signboard hero with a flickering neon-style headline and hand-drawn CSS/SVG steam rising from a cup — no stock photography anywhere on the page, everything is drawn. A canvas-based particle steam system that replaces the static SVG once JS is available — real particles with drift, turbulence and upward acceleration, and they physically scatter when you move your cursor through them, like waving your hand through actual steam. A live "Night Clock" that reads your real local time ( Date , your timezone, nothing hardcoded) and marks whichever stall is "in season" right now with a pulsing "you are here" badge — so the page behaves differently depending on when you actually open it. A theme built for the medium : dark ink background, ember/turmeric accent colors, a hand-lettered chalk font for the "voice" of the thela-wala mixed with a bold display face for the signage, instead of the usual cream-and-terracotta food-site look. Respects prefers-reduced-motion everywhere (falls back to a static SVG steam loop and skips the canvas sim), keyboard-focusable throughout, fully responsive. Demo Journey I wanted to avoid the obvious comfort-food landing page — cream background, terracotta accents, a hero photo of a steaming bowl. It's a solid look but I see it ev

GitHub热门项目 | Modular SenseNova skills for building AI-powered office assistants and productivity workflows | Stars: 4,916 | 1 star today | 语言: JavaScript

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🌀 MOKSHA Devlog — August 15, 2026 Overview Today's session focused on implementing and refining the Shareera Gatee (body-motion) mechanic as a companion to Samaya Gatee (time-flow). Major work included UI/UX polish, physics integration, and karmic carry-over mechanics for praarabdha (accumulated karma from past lives). Commits & Changes 1. UI: Added HUD Element for Shareera Gati Commit: 8874bcc | 06:33 UTC Scope: HTML/JS refactoring of HUD elements Changes: Added new shareera-gatee HUD indicator (cyan, #67e8f9 ) Renamed ui-gatee → samaya-gatee for clarity Updated engine state tracking: _oldStats and _uiScales now include both samayaGatee and shareeraGatee _uiGlows state expanded for dual-gatee animations Files Modified: index.html — HUD markup src/engine.js — State initialization src/main.js — UI element references src/state.js — Animation loop updates Status: ✅ Foundational UI structure ready 2. UI:UX: Implemented Shareera Gatee Commit: 387e488 | 09:30 UTC Scope: Physics integration + dynamic speed modulation Changes: Karma-speed coupling: Punya/Paapa/Praarabdha now reduce player movement speed Base speed modifier: _sMod = 0.7^ashuvhaKarma × 0.8^shuvhaKarma × 0.7^praarabdha Body-motion indicators: 🐌 = slowed (< 100%) 🚶 = normal (100%) 🏃 = accelerated (> 100%) Samaya Gatee now represents relative time flow: Inverted modifier: karmaSpeedMul = (1/0.7)^ashuvhaKarma × (1/0.8)^shuvhaKarma Time accelerates under karma-debt, slows under merit Dynamic emojis: 🧊 (slow) / ⌛ (normal) / ⚡ (fast) Praarabdha snapshot on death: Speed multiplier carries forward to next rebirth Stored in _praarabdhaSpeedMul for persistent karma-weight Game Feel: Karma now directly affects both movement speed and time progression , creating dual gameplay feedback Files Modified: src/engine.js — Physics + HUD animation src/karma.js ��� Rebirth speed carry-over index.html — Icon symbols Status: ✅ Core mechanic implemented 3. praarabdha: No Reset of Samaya Gatee on Punarjanma Commit: 4305106 | 10:25 UTC

Yesterday morning my feed exploded with a model release again. But this one was different from the usual frontier drop. Qwen 3.8 27B hit the top of Hacker News and stayed there: at the time I checked, the thread had passed 1,194 points with 713 comments in under a day. That is the kind of heat normally reserved for a $5-per-million-token API announcement. The twist is that this is a dense 27-billion-parameter open model, Apache 2.0 licensed, that people are running on laptops. Simon Willison ran it on an M5 Max MacBook Pro through LM Studio with a 17GB GGUF file and spent 21 minutes watching it think about an SVG ( his comment ). I build production AI systems with Spring Boot and Spring AI, so my first question was not "how smart is it?" It was: can I call this thing from the code I already have, without a second SDK or a cloud account? The answer is yes, and the setup is smaller than the model's license file. Here is what shipped, what the community actually found when they ran it, and the exact Spring Boot wiring for a local Qwen 3.8 27B. What actually shipped Qwen 3.8 is the latest generation of Alibaba's open model family, and 27B is its compact dense member. The model card lists the headline details: A dense 27B vision-language model. A causal language model with a vision encoder, built on the Qwen3.5 architecture. It takes text, images, and video input. 262,144 tokens of native context. The card says it can be extended toward 1 million tokens with RoPE scaling (YaRN), though the card warns static YaRN can hurt performance on shorter inputs. FP8 quantization from the lab. The FP8 repo uses fine-grained fp8 with a block size of 128 and claims "performance metrics are nearly identical to those of the original model." Thinking on by default. Qwen3.8 operates in thinking mode by default, with three reasoning effort levels: xhigh , medium , and low . It also keeps reasoning context from earlier messages ( preserve_thinking ) for multi-step agent work. Multi-token pr