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This is a submission for Frontend Challenge - Comfort Food Edition, CSS Art . 🍩 Inspiration I write about WordPress plugins and PHP standards for a living, so when DEV dropped a "Comfort Food" theme for their Frontend Challenge, I didn't need to think twice about what to build. Not ramen, not pancakes — a donut. Specifically, the kind of donut that shows up on your desk right when a deploy breaks and somehow fixes everything. The twist I gave myself: don't just draw a static donut. Let people build one — pick a glaze, pile on toppings, then serve it — and do almost all of it in CSS, with JavaScript kept firmly in the back seat where the challenge rules ask for it to stay. That's how Donut Panic was born. 🎬 Demo Pick a glaze, load it up with sprinkles, drizzle, or powdered sugar, then hit Serve and watch it animate off the plate. 🛠️ Journey No JavaScript is driving the donut — :has() is Here's the part I'm most excited to talk about: every visual change in Donut Panic — the glaze swap, the toppings appearing, the donut lifting off the prep station and landing on the plate — is driven by plain checkbox/radio inputs and the :has() selector. Something like .kitchen:has(#serve:checked) .donut lets a parent element react to the checked state of an input buried somewhere inside it, which means the "Serve" button, the topping toggles, and the glaze picker are all just styled <label> s wired to hidden inputs. No click handlers, no state management — the checkbox is the state. JavaScript only shows up once, and it's not touching the art at all: it smooth-scrolls the stage into view on mobile after you hit Serve, because on a stacked mobile layout the donut can animate off-screen. That's the "sprinkle" of JS the challenge rules allow, used exactly the way it's meant to be — a UX nicety, not a rendering engine. Building the donut from the inside out The donut itself is layered rings, not a single flat shape: A base dough circle with a radial gradient doing double duty as both c

Cybersecurity is entering a new phase. This is because security teams are facing more and more complex problems and threats that are moving faster. To help defenders respond more effectively, OpenAI has introduced GPT-5.6-Cyber, a special model designed for advanced cybersecurity tasks. The model supports authorized security research, vulnerability discovery, and other defensive workflows. The Daybreak program is showing how specialized AI tools can improve modern cybersecurity by working together with human security experts. Quick overview GPT-5.6-Cyber is a specialized model for authorized cybersecurity work. It is available through OpenAI’s Daybreak Red access for approved defenders. OpenAI reports a 95% completion rate on its internal advanced cybersecurity evaluation. The model helped researchers uncover vulnerabilities in Chrome’s V8 JavaScript engine. Controlled access, monitoring, and human oversight remain important for safe deployment. What Is GPT-5.6-Cyber? GPT-5.6-Cyber is OpenAI’s cybersecurity-specific model, available through Daybreak Red. Built on GPT-5.6 Sol, it is trained to improve performance on specialized cybersecurity tasks such as finding zero-day vulnerabilities and developing exploit chains, while reducing refusals for certain higher-risk, dual-use cyber tasks. Daybreak has two access tiers: Daybreak Blue provides approved defenders with frontier general-purpose models such as GPT-5.6 Sol, with safeguards tailored to authorized defensive security work. Daybreak Red provides purpose-trained cybersecurity models for authorized vulnerability research, exploit validation, and security testing. This approach reflects a significant shift toward security tools designed for professional cybersecurity environments rather than unrestricted public use. The goal is clear: to help trusted defenders investigate vulnerabilities, analyze potential threats, and respond to security incidents more effectively while keeping access controlled. According to Open

TL;DR — Events in Fitz LiveViews carry data three ways: a click payload ( data-flv-value-* ) tags a button with the value it should send; a form submit ( data-flv-submit ) reads the form's named inputs; and a live value ( @input / @change ) delivers a control's current value in payload["value"] . All three land in the same place — a payload map your handler reads. This post builds a live name list (add / remove / count) that runs both server-rendered and as WebAssembly. (Part 3 of the FitzLiveViews series.) Parts 1 and 2 covered the pitch and the counter. A counter only reads +1 / -1 — no data flows in . Real UIs take input: text, selections, form fields. Here's how that data reaches your handlers. The payload Every event handler has a payload in scope — a Map<Str, Str> . The three mechanisms below all fill it; your handler reads it with payload["key"] (guard with payload.has("key") ): 1. Click payload — a button that carries a value Tag any element with data-flv-value-<key>="{expr}" , and when a data-flv-click on it (or an ancestor) fires, that value rides along: <button data-flv-click= "remove" data-flv-value-item= "{it}" > × </button> event remove () { if ( payload . has ( " item " )) { let target = payload [ " item " ] names = names . filter ( fn ( it ) => it != target ) } } The delete button knows which row it is because the row's value is stamped on it. No IDs threaded through a callback, no closure capture. 2. Form submit — the whole form at once data-flv-submit="handler" on a <form> reads each named input into the payload on submit; data-flv-clear resets a field afterward: <form data-flv-submit= "add" > <input name= "item" placeholder= "Add a name" data-flv-clear /> <button type= "submit" > Add </button> </form> event add () { if ( payload . has ( " item " )) { let n = payload [ " item " ] if ( n != "" ) { names . push ( n ) } } } payload["item"] is the input's value at submit time. No preventDefault , no FormData , no fetch . 3. Live value — @input / @chang

The gaming industry has evolved from a niche entertainment sector into one of the fastest-growing technology-driven industries worldwide. India, with its large young population, growing digital economy, and increasing smartphone and internet penetration, is emerging as an important market for game development. As a result, students and technology enthusiasts are increasingly exploring a career in game development. Unlike traditional careers, game development brings together technology, creativity, storytelling, design, and problem-solving. From mobile games and PC titles to immersive AR/VR experiences, the industry offers diverse career paths for people with different skill sets. What Is Game Development? Game development is the process of designing, creating, testing, and launching video games. It involves several disciplines working together, including programming, game design, 2D/3D art, animation, sound design, storytelling, quality assurance, and project management. A game developer may work on everything from the underlying gameplay mechanics and physics to graphics, artificial intelligence, user interfaces, and multiplayer systems. Depending on their specialization, professionals can work with programming languages, game engines, animation software, or design tools. For aspiring professionals, understanding the different roles in the industry is the first step toward building a successful career in game development. Why Choose a Career in Game Development? Game development can be an exciting career option for individuals who enjoy technology and creative problem-solving. It allows professionals to turn ideas into interactive experiences while continuously learning new tools and technologies. Another advantage is the variety of career opportunities available. Someone interested in coding can become a gameplay programmer, while an artist can specialize in 3D modeling, character design, or animation. Others can explore game design, level design, sound, testing,

Nuxt 4.5 launched last month and it's really neat. One of my most favorite features is the...

A free AI visibility score is auditable only when you can inspect the prompt, engine, raw answer, date, and denominator. Treat the score as a test result, not a property of your brand. This tutorial builds a six-check control you can run by hand, store as plain data, and compare with any tool's output. The workflow takes three buyer questions, runs them in two AI surfaces, and records the six answers without trying to force agreement. It will not estimate your entire market. It will tell you whether a dashboard's headline number has enough evidence to be investigated. What does an AI visibility score measure? An AI visibility score usually summarizes brand presence across a defined set of generated answers. That definition contains the trap: the question set is part of the metric. So are the engine panel, run date, session state, retrieval mode, and rule used to count a “hit.” Remove those inputs and the number is not reproducible. Imagine a tool asks three questions in two engines. That creates six cells. If your brand appears in two cells, the simple presence result is: presence = brand_present_cells / total_cells presence = 2 / 6 presence = 0.333... = 33.3% The arithmetic is trivial. The evidence is not. A different tool can ask five different questions in three engines and produce a different score without contradicting the first run. The two tools measured different grids. Keep the unit explicit: “present in two of six generated answers on this date” is defensible. “Our AI visibility is 33” is incomplete. Which evidence fields should you require? Require five fields for every result: prompt, engine, raw answer, timestamp, and counting rule. Use a sixth field for cited sources when the surface exposes them. A source-only appearance and a prose mention can signal different problems, so do not merge them silently. Here is one real saved result from Webappski's public 14 June 2026 tracker report: { "run_date" : "2026-06-14" , "prompt" : "beste Answer Engine Optimiz

Why a live payment is not a release test The riskiest way to test a SaaS checkout is to make a real payment to yourself. It feels reassuring: the live checkout opened, the card worked, the webhook fired and the refund came back. But that proof mixes engineering QA with revenue evidence. Three different proofs A cleaner billing release process separates three questions: Does billing behave correctly? Test payment, refund, webhook and subscription edge cases in a Stripe sandbox. Is production configured correctly? Verify the live price, currency, checkout destination, webhook configuration and deployed revision without moving money. Did a customer pay? Treat a genuine live transaction as customer activity and revenue evidence, not as an engineering fixture. Stripe documents sandboxes as isolated testing environments and separates sandbox credentials from live credentials. The practical lesson is broader than Stripe: operational proof and commercial proof should not share the same transaction. A useful boundary Use this sequence: Sandbox QA → read-only production verification → genuine customer payment . It keeps release evidence, reconciliation and revenue numbers easier to interpret. We recently tightened the same boundary in VendorOS. That does not prove live customer revenue; it is a workflow lesson about keeping evidence categories separate. If your release process still requires a live self-payment, ask which part of the verification can become read-only. Sources: Stripe Sandboxes Stripe API keys Stripe testing VendorOS release boundary

I Built a Cinematic Developer Portfolio Instead of a Traditional One — Here’s What I Learned Most developer portfolios follow the same structure: About. Skills. Projects. Contact. There is nothing wrong with that. But when I started rebuilding mine, I wanted it to feel less like a collection of sections and more like an experience . So what started as a simple portfolio redesign slowly turned into a cinematic, interactive developer portfolio built around my journey as a Software Engineer and AI Developer . 🌐 Live Portfolio: https://pavan-sai-portfolio-xi.vercel.app The Idea I wanted the visitor to feel like they were entering a story rather than opening another resume website. The final experience includes: A cinematic opening sequence A custom soundtrack Full-screen scene-based navigation Animated video backgrounds Project showcases and case studies A personal journey timeline Responsive mobile layouts A cinematic closing scene Smooth transitions between sections The overall visual direction is built around: Black + Champagne Gold + Cinematic Lighting The Portfolio Flow The experience follows a sequence: Intro → Pavan Sai → Hero → About → Skills → Projects → Journey → Contact → Closing Credits Instead of normal vertical scrolling, the main portfolio behaves more like a sequence of scenes. Visitors move through the experience using navigation controls. Project case studies are separate and can scroll normally because they contain more detailed technical information. The Projects Some of the projects featured in the portfolio include: DevPilot AI An AI-powered DevOps incident recovery platform focused on detecting failures, diagnosing issues and helping recover production systems. ROCmPorter Agent An AI-assisted CUDA-to-AMD ROCm migration tool that analyzes repositories, identifies CUDA dependencies and helps generate migration changes. HunarHub A local skilled-worker discovery and service marketplace platform. I also showcase other engineering, AI and full-stack pro

WordPress sitelerinde hız problemi yaşandığında genellikle ilk refleks bir cache eklentisi kurmak oluyor. Bazen gerçekten fark yaratıyor, bazen de PageSpeed puanı biraz yükselmesine rağmen site hâlâ yavaş hissettiriyor. Bunun nedeni WordPress performansının tek bir ayardan oluşmaması. Hosting, tema, eklentiler, görseller, JavaScript dosyaları ve veritabanı aynı anda sayfanın yüklenme süresini etkileyebiliyor. Bu nedenle bir siteyi hızlandırmaya başlamadan önce asıl problemin nerede olduğunu bulmak gerekiyor. WordPress projelerinde sık karşılaştığım 7 performans hatasını aşağıda topladım. Gereğinden büyük görseller kullanmak En sık karşılaştığım problemlerden biri bu. Sayfada 700 piksel genişliğinde görüntülenecek bir görselin 4000-5000 piksel olarak yüklenmesi oldukça yaygın. Özellikle yüksek çözünürlüklü stok fotoğraflar doğrudan WordPress'e yüklendiğinde tek bir görsel birkaç megabayta ulaşabiliyor. Bu da özellikle mobil bağlantılarda ciddi yük oluşturuyor. Görselleri yüklemeden önce kullanılacağı alana uygun boyuta getirmek, sıkıştırmak ve mümkün olduğunda WebP veya AVIF gibi modern formatları tercih etmek önemli. Ancak yalnızca dosya formatını değiştirmek yeterli değil. 4000 piksel genişliğindeki bir görseli WebP'ye çevirmek, görselin gereğinden büyük olduğu gerçeğini değiştirmiyor. Her problemi cache eklentisiyle çözmeye çalışmak Cache WordPress performansında önemli bir yere sahip. Fakat cache eklentisi kurmak her hız problemini ortadan kaldırmaz. Sunucu geç cevap veriyorsa, çok fazla JavaScript çalışıyorsa veya veritabanında ağır sorgular varsa cache yalnızca problemin bir bölümünü gizleyebilir. Ayrıca aynı anda birden fazla optimizasyon eklentisi kullanmak da başka sorunlara yol açabiliyor. Örneğin bir eklentide JavaScript erteleme, başka bir eklentide tekrar JavaScript optimizasyonu ve hosting panelinde üçüncü bir optimizasyon sistemi açıldığında hangi ayarın ne yaptığını takip etmek zorlaşıyor. Ben mümkün olduğunca tek bir ana cache sistemi üzerinden ilerl

JetBrains has described how it began centralising AI usage after development-related spending increased roughly tenfold in six months. Rather than restricting engineers to a small set of approved tools, the company built a shared access and accounting layer intended to preserve tool choice while giving teams greater visibility and control over consumption. By Matt Foster

This is a submission for DEV's Summer Bug Smash: Smash Stories powered by Sentry. Introduction: The...

This is a submission for Frontend Challenge - Comfort Food Edition, CSS Art . Inspiration In an Indian tech office, no product release, critical bug fix, or late-night deployment is complete without a 5-minute pantry chai break. A steaming clay Kulhad of Ginger Adrak Chai paired with crisp Garma-Garam Samosas is the ultimate comfort food that powers developers through endless coding sprints. This authentic workplace culture and rich street-food nostalgia inspired me to build a pure-CSS interactive art and corporate pantry scene. Demo Live Interactive Demo : Corporate Chai & Samosa Experience CodePen Embed : codepen.io GitHub Repository : Sayista-Yazdani/corporate-chai What I Built Adrak Chai & Samosa is an interactive web experience featuring: Pure CSS Hero Artwork : Handcrafted clay-textured Kulhad Chai cup with a shimmering tea surface, malai rim, and layered rising steam animations. Golden-brown samosas with crisp crimped edges, served on a traditional plate alongside mint and tamarind chutneys. Interactive Corporate Pantry Corner : Fully detailed tea kitchen equipped with a glowing gas stove, boiling tea saucepan with foam, spice jars ( Adrak , Elaichi , Chai Patti ), and stacked clay cups. 4 Distinct Characters : Rohan (Frontend Dev), Amit (Tech Lead), Priya (Product Manager), and Kaka (Pantry Specialist). Web Speech API & Audio Narration : Real voice speech synthesis with gender-matched voice profiles for each character. Character Gaze & Mouth Choreography : Characters automatically look toward whoever is currently speaking with dynamic gaze shifting, listening poses, and animated mouth movements. Journey & Tech Stack Technical Implementation CSS Artwork : Built entirely with CSS gradient meshes, polygon shapes, keyframe animations, and layered pseudo-elements ( ::before / ::after ). Audio Engine : Powered by native window.speechSynthesis with dynamic voice selection and text cleaning. State Management : Reactive data-speaker HTML attributes driving multi-char

I used to think financial apps were designed mostly for people who already understood finance. You open an app, see dozens of charts, numbers, and settings, and sometimes your first thought is: “Okay… where do I even start?” AI is slowly changing that experience. The most useful AI applications in fintech are not always about making predictions. In many cases, they are about removing friction: organizing information, automating repetitive tasks, and helping users understand complicated processes. We can already see this trend in different areas. Companies like Stripe are using technology to simplify online payments, while platforms like Plaid help connect financial data between different services. The same idea is appearing in other parts of finance too. Some digital financial platforms are exploring automation tools that help users create more structured workflows instead of manually managing every step. For example, platforms like BYDFi have introduced automated tools that allow users to use predefined strategies rather than constantly monitoring every market movement. Of course, automation does not replace human decisions. A smarter tool does not mean a person can ignore research or risk management. For developers, the interesting challenge is not building the most complicated system. It is building something that makes complicated things easier for real users. Maybe the future of fintech is not about adding more buttons. Maybe it is about helping people need fewer buttons.

A few years ago, a technical interview mostly tested one thing: can you write correct code, from memory, under pressure. That bar has quietly shifted — and a lot of developers preparing for interviews right now haven't fully clocked it. AI coding assistants are part of daily work at most companies now, from big IT services firms to small product teams. Interview panels have adjusted to that reality faster than most prep guides have. What's actually different now Interviewers care less about whether you can produce a function from scratch, and more about whether you understand what code is doing and why. It's increasingly common to be handed a piece of AI-generated code and asked to find the bug, justify a design decision, or optimize it — instead of writing something from zero on a whiteboard. Some companies go further and let you use AI tools during the technical round, then evaluate how well you direct the tool, verify its output, and catch its mistakes. The skill being tested has moved from "can you write code" to "can you reason clearly with code as your material." Three things I keep seeing candidates get wrong Treating a finished course or degree as the finish line. Completing a syllabus tells an employer you were exposed to concepts. It doesn't tell them you can apply those concepts to a messy, real-world problem — which is exactly what open-ended interview scenarios are designed to expose. Leaning on AI tools without understanding the output. Using an AI assistant while practicing at home is fine. The problem shows up when that habit surfaces in a live interview as an inability to explain your own solution. If you can't walk through why a piece of code works, a couple of follow-up questions will make that obvious fast. Underrating communication and debugging skills. As AI tools take on more initial code-writing, the human value shifts toward reviewing, debugging, and explaining decisions to teammates. Candidates who only practiced writing code — and never pr

Spent the week redesigning my portfolio’s blog layout and nuking thousands of stale notes in my Obsidian vault. Between the UI polish and some deep dives into libp2p DHT de-flaking, I pushed 36 commits and managed to delete almost 16,000 lines of clutter. TL;DR I’ve always believed that your digital space needs a good pruning every now and then to stay healthy. This week was the embodiment of that philosophy. I pushed 36 commits across four primary projects, resulting in over 23,000 additions and nearly 16,000 deletions. Most of that churn came from a massive redesign of my portfolio's blog and a long-overdue "fresh start" for my Obsidian vault. On the open-source side, I spent some quality time in the weeds of py-libp2p , chasing down flaky DHT tests and proposing better subnet diversity limits. What I Built Portfolio Redesign: The Typography Pivot My main focus this week was my portfolio. I’ve been feeling like the blog layout was getting a bit cluttered, so I opened and merged PR #15, which was all about "typographic hierarchy instead of decoration." I’m moving away from unnecessary borders and boxes and letting the type do the heavy lifting. I spent a lot of time in components/blog and app/blog refining the layout. I implemented borderless filter pills and full-width rows to give the content more room to breathe. One of the bigger technical shifts was moving the blog list to be fully server-rendered. It feels snappier, and it allowed me to implement more "honest" dates and better hover states on the rows. I also added a real focus ring for accessibility (because we’ve all been frustrated by keyboard navigation that feels like a guessing game). By the time I was done, I’d touched over 200 files in that repo alone. The Obsidian Purge I also took a metaphorical chainsaw to my obsidian-vault . I nuked nearly 10,000 lines of stale content. I removed entire directories for "Projects," "Rust," and "Backend" notes that were just gathering digital dust. It’s easy to let

If you love the simplicity of Docker Swarm (native Compose files, lightweight single binary) but miss the advanced capabilities of Kubernetes (targeted label placement, SRE-grade observability, built-in DNS service discovery, and zero-trust ingress), meet Gubernator (gbnt) . We are excited to release Gubernator v2.13.0 , introducing three massive feature suites natively integrated into a single binary and a modern Material Design 3 Flutter Web Dashboard: Google SRE Multi-Burn-Rate SLO Engine & Interactive Suite CoreDNS 4-Tab Management Suite & Interactive Dig Playground Caddy Ingress & Zero-Trust Reverse Proxy Suite Fun Fact: The entirety of Gubernator's codebase, multi-node deployment pipelines, and SRE features were designed, built, and pair-programmed using **Google Antigravity (AGY) , Google DeepMind's agentic AI coding assistant! Let's dive into what's new and how you can level up your self-hosted or production container clusters! 1. Google SRE Multi-Burn-Rate SLO Engine & Web Suite Defining Service Level Objectives (SLOs) and tracking Error Budgets is the gold standard of Site Reliability Engineering. Until now, implementing SLOs meant running heavy Kubernetes CRDs (via tools like Sloth or Pyrra) or using costly SaaS platforms. Gubernator v2.13.0 brings Google SRE Workbook (Chapter 5) compliant multi-burn-rate alerting straight to simple docker-compose.yml services: version : " 3.8" services : payment-api : image : hashicorp/http-echo:latest labels : gbnt.slo.enable : " true" gbnt.slo.target : " 99.9" gbnt.slo.window : " 30d" gbnt.slo.template : " caddy-http" gbnt.slo.journey : " Checkout Flow" What makes Gubernator's SLO Suite unique? Google Multi-Burn-Rate Alerting : Automatically generates standard 4-window Prometheus recording and alert rules ( Critical Page 1h/6h & Warning Ticket 3d/14d ). Dynamic "No-Code" Management : Click "+ Configure / Add SLO" in the Web UI or call POST /v1/slo/edit to create, edit, or disable SLOs on the fly without editing Compose

Most "AI gift finders" are a search box with a chatbot glued on. I wanted to build something different — a quiz-driven gift recommender that ranks real Amazon products by who the recipient actually is , not just keywords. I call it GiftHive . In this post I'll walk through the architecture, the conversion tricks I learned shipping it, and the bits I'm proudest of. The Problem Picking gifts is emotionally expensive. You scroll Amazon for an hour, second-guess every option, and end up buying a gift card. Existing tools don't help because they optimize for keyword match , not recipient fit . GiftHive flips the input: instead of "show me gifts under $50", you answer a 30-second quiz about the person (relationship, interests, occasion, budget) and get a ranked shortlist with explanations of why each gift fits. Stack Next.js (App Router) — SSR for fast first paint, RSC for product data Tailwind CSS — design system + dark mode via CSS variables Cloudflare Pages — edge-deployed, free tier covers the traffic Amazon Associates — affiliate revenue model The Funnel The whole site is a 3-step conversion funnel: Landing page — exit-intent modal + social proof toasts prime the visitor Quiz — 30-second, one-question-per-screen flow, no login Results — ranked products with countdown bar and "X people found gifts this week" social proof Every step has a single primary CTA. The exit-intent modal is route-aware — it only fires on / and stays silent on /quiz and /results so it never interrupts the funnel mid-flow. That bug cost me ~15% of quiz completions before I caught it. Personalization Logic Each quiz answer maps to a vector of attributes (interests, style, budget, relationship). Products in the catalog have matching tags. Ranking is a weighted score: score = tag_overlap * w1 + budget_match * w2 + occasion_match * w3 No ML model needed — a few hundred products and clean tagging is enough to feel personal. Amazon Affiliate Integration Every product link runs through getAmazonUrl() w

I was watching The Mandalorian the other day when it struck me that I don't know Pedro Pascal, which...

Say "AI" in a room full of developers and a pecking order forms on its own. Python sits at the top,...

The concrete problem Running two or three coding-agent sessions is easy. Knowing when their work is safe to combine is not. One session changes an API while another writes regression tests against the old shape. A third investigates a production failure and quietly edits the same configuration file. Git worktrees prevent immediate filesystem collisions, but they do not explain task dependencies, transfer assumptions, or warn that two agents are solving incompatible versions of the problem. The developer becomes a human message bus: checking terminals, copying commit IDs, repeating context, and deciding which session should wait. The more capable each agent becomes, the less useful a wall of terminal panes is as a coordination interface. The current signal Claude Code now supports messaging between sessions on the same machine. Its documentation describes session discovery, plain-text messages, and a local messaging socket. Agent view separately exposes background-session state, worktrees, pull-request status, and a JSON listing suitable for scripts. Hooks can observe tool input and block a tool call before execution. That does not prove demand for a new product. It does create a concrete implementation moment: the primitives for handoffs and visibility exist, while dependency ownership and conflict negotiation remain a workflow problem. In RayTally's bounded Hacker News snapshot at August 9, 00:33 UTC, the cross-session messaging discussion had 50 points and 26 comments and ranked 18th. Those numbers describe that historical observation only; they are not user counts, market validation, or a prediction of lasting interest. A product direction: a control desk for handoffs The useful product is not another chat window. It is a small local control desk that makes each session declare four things: its goal, worktree, files it expects to touch, and the result another session is waiting for. When the API session finishes, the testing session should receive a compact hando