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The Prizes for the SecDim AppSec Village Wargame CtF at DEF CON 34 has been announced! Build and submit a challenge and get a chance to Win a ROG Xbox Ally if your challenge submission wins 1st Place! Challenge submissions close on 31 July. https://sessionize.com/appsec-village-wargame

𝗦𝗮𝘆 𝗵𝗲𝗹𝗹𝗼 𝘁𝗼 𝗟𝘂𝗺𝗼𝗿𝗮 — 𝗧𝗵𝗲 𝗨𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗕𝗼𝗼𝘁𝘀𝘁𝗿𝗮𝗽 𝟱 𝗔𝗱𝗺𝗶𝗻 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗨𝗜 𝗞𝗶𝘁. 💎 🔗 𝗚𝗶𝘁𝗛𝘂𝗯 𝗥𝗲𝗽𝗼: https://github.com/Chetankumar-Akarte/lumora 🔗 Demo: https://renukatechnologies.in/demo/lumora/ Don't forgot to 🤩 Star and 👉 Fork the Repo 𝗟𝘂𝗺𝗼𝗿𝗮 is a modern, responsive 𝗕𝗼𝗼𝘁𝘀𝘁𝗿𝗮𝗽 𝟱 𝗔𝗱𝗺𝗶𝗻 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗨𝗜 𝗞𝗶𝘁 designed for teams that need a polished, enterprise-ready control center without the bloat. Whether you are building for SaaS, CRM, E-commerce, or internal analytics, Lumora provides a scalable, token-driven foundation to speed up your workflow. 𝗟𝘂𝗺𝗼𝗿𝗮 is the result: a complete admin ecosystem featuring everything from KPI blocks and ApexCharts to full E-commerce management flows and authentication screens. 𝗪𝗵𝗮𝘁’𝘀 𝗶𝗻𝘀𝗶𝗱𝗲? • Full UI Kit with basic and advanced components. • Enterprise pages (Users, Roles, Permissions, Invoices). • Interactive apps like Calendar and Contacts. • Clean, token-driven styling for consistent design. 𝗧𝗲𝗰𝗵 𝗵𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: • Bootstrap 5.3 • ApexCharts & Chart.js • Vanilla JavaScript • Mobile-first design 𝗞𝗲𝘆 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: • 𝗠𝗼𝗱𝗲𝗿𝗻 𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸: Built with Bootstrap 5.3, Vanilla JS, and CSS3 using a module-first architecture. • 𝗖𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀: Includes layouts for Analytics, CRM, Project Management, HRM, and more. • 𝗙𝗲𝗮𝘁𝘂𝗿𝗲-𝗣𝗮𝗰𝗸𝗲𝗱 𝗔𝗽𝗽𝘀: Ready-to-use interfaces for Advanced Chat, Kanban boards, Email, and File Management. • 𝗗𝗮𝗿𝗸 & 𝗟𝗶𝗴𝗵𝘁 𝗠𝗼𝗱𝗲𝘀: Clean, professional visuals with seamless theme switching. • 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗙𝗿𝗶𝗲𝗻𝗱𝗹𝘆: Modular CSS, reusable partials, and organized project structure. I built this to bridge the gap between "pretty" templates and "functional" enterprise tools. Check it out, star the repo, and let me know what you think! I'd love for you to take a look at the code and perhaps even use it for your next project. Feedback and contributions are always welcome! WebDevelopment, Bootstrap5, AdminDashboard, OpenSource, UIUX, JavaScript, GitHub, Bootstrap, CodingCommunity, OpenSourceProject, FrontendDev, LumoraUI

The problem I kept running into I'm a chronic tab hoarder. At any given time I've got 40–80 tabs open across two windows. Chrome's built-in Memory Saver is aggressive in the wrong ways — it hibernates tabs I'm actively referencing. And the built-in task manager is a two-step detour that still doesn't tell me which tabs I should actually close. So I built Tab Memory Manager. What it does Per-tab memory estimates — A live MB count next to every open tab. Sorted by memory usage by default. There's a live total on the toolbar icon so you always know what Chrome is consuming right now. Smart suggestions — The extension flags your biggest, stalest tabs: ones that are idle the longest and consuming the most. It never suggests your active tab, pinned tabs, tabs playing audio, or domains you've whitelisted. Hibernate, don't close — This was the core design decision. Hibernating frees the memory but keeps the tab alive in your strip — it reloads when you click it. Much safer than closing, especially mid-research. Bulk cleanup — Select multiple tabs or hit Apply on the suggestions panel. See the total memory you'll reclaim before you commit. Undo list — Closed something by mistake? There's a "Recently cleaned" panel. One click to restore. Tab grouping — Groups all your open tabs by domain into color-coded Chrome tab groups, instantly. The interesting technical bit: memory estimates Chrome's stable extension API doesn't expose exact per-tab memory. The chrome.processes API that does exists only on Dev and Canary builds — not the Chrome that 99% of people use. So Tab Memory Manager uses calibrated estimates based on tab state, domain patterns, and known Chrome process overhead. These are clearly labeled "est." in the UI. If you're on Dev or Canary, you can switch on real per-tab memory in settings. The warning Chrome shows about "processes requires dev channel" is a Chrome-generated note about that optional API — the extension works completely normally without it. It's not a bug

Most AI tools give Australian small businesses American advice. An Aussie tradie running Xero does not need to hear about QuickBooks. A cafe owner with three casual staff has Fair Work Act obligations that no generic "automate your business" tool will surface. I built AppZ AU Business Advisor to fix this -- a free tool powered by Gemini 2.0 Flash that generates personalised automation blueprints with real Australian business context. This post covers the technical decisions, the prompt engineering approach, and why the AU-specific scaffold makes all the difference. The Problem with Generic AI Business Advice When you ask a general AI "how should I automate my business?", the training data skews heavily American. You get advice about QuickBooks, not Xero. About W-9 forms, not BAS lodgement. About 401k, not superannuation. For an Australian sole trader approaching the $75k GST registration threshold, this is not just unhelpful -- it is actively misleading. The compliance obligations are different. The software ecosystem is different. The pain points are different. The Prompt Scaffold Approach Instead of injecting "you are talking to an Australian business" as a keyword, I built a reasoning scaffold -- a structured context block the model uses as a knowledge foundation: AUSTRALIAN BUSINESS CONTEXT: - GST: 10%, mandatory registration at $75k annual turnover - BAS: lodged quarterly (or monthly for large businesses) to the ATO - Superannuation: 11.5% employer contribution, paid per payroll from July 2026 - ATO tools: STP Phase 2 mandatory for all employers - Dominant accounting platforms: Xero, MYOB, Reckon (not QuickBooks) - Fair Work Act: award rates, leave entitlements, payslip requirements - Key software by vertical: ServiceM8 (trades), Deputy (hospitality), Cliniko (health) This is not a keyword list -- it is a reasoning foundation. When a tradesperson mentions "invoicing problems", the model now reasons about Xero integrations, GST-inclusive invoicing, and BAS categ

This is a submission for the June Solstice Game Jam I got flagged by Sloan. If you've been on...

Microsoft recently introduced at Build 2026 Microsoft Scout, an always-on agent. Scout belongs to a new category of agents Microsoft called Autopilots: always-on agents that work autonomously on a user’s behalf with their own identity, without needing to be prompted each time. Microsoft Scout integrates with Work IQ and is based on the open-source agent framework OpenClaw. By Bruno Couriol

My motorcycle has a Bluetooth instrument cluster. It pairs with the manufacturer's phone app and shows turn-by-turn navigation right on the dash, which sounds great until you actually use it. The nav is routed through a maps provider I don't love, the app is clunky, and there's no way to extend any of it. I kept thinking: it's just my bike talking to my phone over Bluetooth. How locked down can it really be? So one weekend I decided to find out, and a few weeks later I had Google Maps navigation running on the cluster through an app I wrote myself. Here's how that went. There are no docs Of course there aren't. It's a proprietary protocol, and the only reference that exists is the manufacturer's own app, in compiled form. So step one was just watching. I started with a GATT walk on the live bike, which is the Bluetooth equivalent of knocking on every door to see what's there. The cluster exposes one vendor service with two characteristics: one the phone writes to, one the bike sends notifications back on. That's the entire conversation surface. Then I captured the actual bytes going across. Android can log every Bluetooth packet through its HCI snoop log, so I paired the phone with the bike, rode around, and pulled the capture. Now I had real traffic, and absolutely no idea what any of it meant. Reading the app to read the protocol You can stare at hex forever and still guess wrong. The faster path was the app itself. I pulled the APK, ran it through JADX to decompile it, and got something close to readable source. Most of the class names weren't even obfuscated, which was a gift. From there it was cross-referencing: take a message I saw on the wire, find the code that builds it, and work out what each byte is. Frida helped a lot here. It lets you hook a running app and watch functions get called with their real arguments, so I could catch the exact moment the app turned "next turn is a left in 200m" into bytes and shipped them to the bike. Slowly the shape came out

One of the challenges I faced while building my LaTeX Writer project was implementing version control and project forking in a storage-efficient way. A typical LaTeX project contains multiple files. Even a simple project usually has a "main.tex" file, bibliography files, images, style files, and other supporting documents. If I stored a complete copy of every file for every version or fork, storage requirements would grow rapidly. Imagine a project with four files and ten versions. Storing the entire project for every version would mean storing the same files repeatedly, even when only one line changed. Forking would create an even bigger problem because every fork would require another complete copy of the project. Instead of accepting this inefficiency, I started researching how large platforms solve the same problem. GitHub was the obvious inspiration. Learning from GitHub GitHub does not store a complete copy of a repository every time a change is made. Instead, it stores content separately and uses references to connect files, commits, and repositories. This idea became the foundation for my own implementation. Project Structure Whenever a new project is created, a default file called "main.tex" is generated automatically. The project itself does not directly contain file contents. Instead, it stores metadata such as: Project ID Owner ID Root Folder ID File References Each file also has its own metadata record containing: File ID File Name Blob ID Project ID Owner ID Folder ID The actual content is not stored inside the file metadata. Instead, the content lives inside a separate entity called a Blob. Loading a Project When the editor loads a project, it reconstructs the directory structure using metadata. The process works like this: Retrieve the project's Root Folder ID. Find all folders belonging to that folder hierarchy. Find all files belonging to each folder. Build the directory tree for the frontend. Because files and folders are stored independently, the

Epic Games just dropped something that could reshape how game studios handle code and assets. They've open-sourced Lore — a centralized version control system built from the ground up to solve one painful problem: managing enormous binary files alongside source code. This isn't another Git wrapper. It's a completely new VCS, written in Rust, MIT-licensed, and battle-tested behind Fortnite's UEFN (Unreal Editor for Fortnite) toolkit. Why Does the World Need Another VCS? Git is brilliant for text-based code. But game development isn't just text. It's 4K textures, uncompressed audio, rigged 3D models, animation sequences, and massive world maps. These files can be hundreds of megabytes each. Git wasn't designed for this. Git LFS (Large File Storage) helps, but it's a patch on top of a fundamentally text-oriented system. Perforce Helix Core has been the industry standard for game studios for decades — but it's proprietary, expensive, and closed-source. Epic Games looked at this landscape and said: we can do better. What Is Lore? Lore is a centralized, content-addressed version control system optimized for: Large binary assets — textures, meshes, audio, video Massive teams — hundreds of developers working simultaneously Hybrid projects — code + binaries in the same repository Sparse checkouts — developers only download what they need Think of it as Perforce's philosophy (centralized, binary-friendly) combined with Git's content-addressed storage model, wrapped in Rust's performance guarantees. How It Works Under the Hood Lore's architecture is built around a few key technical decisions: Content-Addressed Storage Every piece of data is stored and referenced by its content hash. This means: Automatic deduplication — identical content is stored once Integrity verification — any tampering changes the hash Efficient caching — content can be cached anywhere in the pipeline Merkle Trees & Immutable Revision Chain Revision hashes are cryptographically derived from parent hashes

Nếu bạn vừa nhận một chiếc laptop từ danh sách ReviewLaptop để vừa học kiến trúc vừa làm dev, việc tối ưu hóa máy là cực kỳ quan trọng. Các phần mềm như Revit hay AutoCAD vốn đã ngốn tài nguyên rất lớn, vì vậy bạn cần một môi trường lập trình 'nhẹ' và ổn định. Tối ưu hóa WSL2 và Docker cho máy Workstation Với các dòng máy như Dell Precision hay Lenovo LOQ, việc chạy WSL2 với cấu hình mặc định có thể chiếm dụng quá nhiều RAM, làm ảnh hưởng đến các ứng dụng kiến trúc đang mở. Bạn nên giới hạn tài nguyên cho WSL2 bằng file .wslconfig . Truy cập vào đường dẫn %USERPROFILE%_\.wslconfig (hoặc tạo mới) và cấu hình như sau: [wsl2] # Giới hạn RAM để dành cho Revit/AutoCAD memory = 8GB # Giới hạn số lượng nhân CPU processors = 4 # Tự động giải phóng bộ nhớ khi không sử dụng autoMemoryReclaim = true # Thiết lập swap nếu cần swap = 4GB Nếu bạn có GPU rời (như trên dòng Lenovo LOQ hay Alienware), hãy đảm bảo đã cài đặt NVIDIA Container Toolkit để thực hiện Docker GPU passthrough . Điều này giúp bạn chạy các container xử lý AI hoặc render mà không làm treo hệ điều hành chính. Quản lý nhiệt độ và giới hạn phần cứng Một giới hạn thực tế quan trọng khi dùng laptop workstation là VRAM ceiling . Các dòng máy tầm trung thường có VRAM hạn chế, nếu bạn chạy Docker image nặng hoặc render cùng lúc, máy sẽ bị giật lag do tràn bộ nhớ đồ họa. Lời khuyên về Thermal Mode: Khi Code/Làm việc nhẹ: Hãy để ở chế độ Balanced hoặc Quiet . Việc này giúp giảm tiếng ồn của quạt (fan noise) và kéo dài tuổi thọ linh kiện. Khi Build Project/Render: Chuyển sang chế độ Performance hoặc Turbo . Lúc này, ưu tiên là đẩy hết nhiệt lượng ra ngoài để duy trì xung nhịp CPU cao nhất có thể. Việc cân bằng giữa sức mạnh phần cứng cho đồ án kiến trúc và sự ổn định cho môi trường dev sẽ giúp bạn làm việc hiệu quả hơn rất nhiều.

Revisar código es una de las actividades más subestimadas del desarrollo de software. La mayoría de los equipos la tratan como un trámite, algo que hay que aprobar antes de mergear. El resultado es que los PRs se aprueban con un “LGTM” después de dos minutos de scroll, y los problemas reales pasan de largo. Una revisión bien hecha no es leer línea por línea buscando typos. Es entender qué intenta hacer ese código, si lo hace de la manera correcta, y si introduce riesgos que no existían antes. Eso requiere un proceso, no un instinto. Este artículo cubre cómo estructurar ese proceso: qué revisar, en qué orden, qué preguntas hacer, y cómo detectar problemas de seguridad sin ser un experto en ciberseguridad. Empieza por el contexto, no por el código El error más común en code review es abrir el diff y empezar a leer desde la primera línea modificada. Antes de ver una sola línea, necesitas entender qué problema resuelve este cambio. Lee la descripción del PR. Si no hay descripción, o si dice “fixes bug”, ya encontraste el primer problema. Un PR sin contexto obliga al reviewer a reconstruir el razonamiento del autor desde cero, y eso aumenta la probabilidad de aprobar algo que no debería aprobarse. Lo que un buen PR debe explicar: Qué cambia no cómo, sino qué problema resuelve Por qué este approach si hay alternativas que se descartaron, decirlo Cómo probarlo, pasos para verificar que funciona Qué no cubre, scope explícito evita confusiones Si tienes esa información antes de ver el diff, tu revisión va a ser significativamente más efectiva. Qué revisar y en qué orden No toda línea de código merece el mismo nivel de atención. Un buen reviewer distribuye su energía de forma inteligente. Primero: arquitectura y flujo de datos. ¿El cambio tiene sentido a nivel de diseño? ¿Agrega una dependencia innecesaria? ¿Rompe alguna abstracción existente? Esto es lo más difícil de cambiar después. Segundo: lógica de negocio. ¿El código hace lo que dice que hace? ¿Los edge cases están cub

There was a phase when Google was the only goal I could see. I pushed myself through endless DSA...

Here's what you need to do to get those pesky "write with Gemini" pop-ups to go away.

Have you ever tried to track your calories by manually searching for "half-eaten avocado toast" in a database? It’s a nightmare. While basic AI Computer Vision can identify an "apple," traditional models often fail at the granular level—distinguishing between 100g and 250g of pasta or identifying hidden toppings in a complex salad. In this tutorial, we are building a high-precision food nutrition AI engine. By combining the Segment Anything Model (SAM) for pixel-perfect object isolation and GPT-4o Vision for multi-modal reasoning and volume estimation, we can transform a simple smartphone photo into a detailed nutritional report. If you’re looking to dive deeper into production-grade AI patterns, I highly recommend checking out the advanced engineering guides at WellAlly Blog , which served as a major inspiration for this architecture. 🏗️ The Architecture: A Hybrid Vision Pipeline To achieve high accuracy, we don't just throw an image at an LLM. We use a "Segment-then-Analyze" pipeline. This ensures the LLM focuses on specific regions of interest (ROIs) rather than getting distracted by the background. graph TD A[User Uploads Food Image] --> B[Pre-processing with OpenCV] B --> C[SAM: Segment Anything Model] C --> D{Multi-Object Masking} D -->|Mask 1: Protein| E[GPT-4o Vision Reasoning] D -->|Mask 2: Carbs| E D -->|Mask 3: Veggies| E E --> F[Nutrient Mapping & Volume Estimation] F --> G[FastAPI Response: JSON Schema] G --> H[Final Dashboard] 🛠️ Prerequisites Before we start, ensure you have your environment ready: Python 3.10+ GPT-4o API Key (OpenAI) SAM Weights ( sam_vit_h_4b8939.pth ) Tech Stack : FastAPI , OpenCV , PyTorch , segment-anything 🚀 Step-by-Step Implementation 1. Object Segmentation with SAM First, we use Meta’s SAM to generate masks. This allows us to "cut out" each individual food item. import numpy as np import cv2 from segment_anything import sam_model_registry , SamPredictor # Initialize SAM sam_checkpoint = " sam_vit_h_4b8939.pth " model_type = "

📚 Limn Engine — Complete API Reference Quick Navigation Class Purpose Level Display Canvas, game loop, input, camera, scenes 🟢 L1 Component Every visible game object 🟢 L1 Camera Viewport control (follow, shake, zoom) 🟡 L2 move Movement, physics, particles, helpers 🟢 L1 state Read-only query helpers 🟢 L1 TileMap Grid-based levels 🟡 L2 Tctxt Rich text with backgrounds 🟢 L1 Sound Single audio file 🟢 L1 SoundManager Multiple sounds, volume control 🔴 L4 ParticleSystem Emit, burst, continuous emitters 🟠 L3 Sprite Spritesheet animation 🟡 L2 Display The heart of every Limn Engine game. Creates the canvas, runs the game loop, captures input, manages the camera, and controls scenes. Constructor const display = new Display (); Properties Property Type Description .canvas HTMLCanvasElement The game canvas .context CanvasRenderingContext2D 2D drawing context .keys Array Boolean array indexed by keyCode .scene Number Current active scene (default 0) .camera Camera Attached camera instance .deltaTime Number Time since last frame (seconds) .fps Number Current frames per second .frameNo Number Total frames elapsed .x / .y Number false Methods Method Parameters Description .start(w, h, node) width, height, parentNode Initialise canvas and start game loop .perform() — Activate dual-canvas pipeline (call before .start() ) .add(comp, scene) Component, scene number Register a Component for rendering .stop() — Pause the game loop .scale(w, h) width, height Resize canvas after start .backgroundColor(color) CSS color Set background colour .lgradient(dir, c1, c2) direction, color, color Linear gradient background .rgradient(c1, c2) color, color Radial gradient background .fullScreen() — Enter fullscreen .exitScreen() — Exit fullscreen .tileMap() — Build TileMap from display.map and display.tile Usage const display = new Display (); display . perform (); display . start ( 800 , 600 ); display . backgroundColor ( " #0a0a2a " ); const player = new Component ( 40 , 40 , " blue " , 100 , 100 ); d

(Without Fighting upload_max_filesize ) You deploy your app. A user picks a 400 MB video. They hit upload. The progress bar freezes. Then — nothing. You check the logs. POST Content-Length exceeded post_max_size . Again. We've all been there. The fix is usually "raise PHP limits" or "use S3." Both work — until you're on shared hosting, a legacy VPS, or a client who won't touch php.ini . That's the problem Pinion solves. What is Pinion? Pinion is an open-source resumable chunked upload protocol for PHP. Instead of one giant multipart/form-data request, the browser sends the file in small parts (default: 5 MB). The server stores each part, then assembles the final file on disk. Three steps. That's the whole contract: init → upload parts → complete Package Registry Role pinoox/pinion Packagist PHP server engine @pinooxhq/pinion-client npm Browser client Protocol id: pinion · version: 2 Why not just use S3? Object storage is great. But sometimes you need files on your server : A CMS media library on local disk A Laravel app without cloud budget Shared hosting with no S3 SDK An admin panel behind a simple PHP API Pinion isn't a CDN or a storage service. It's a protocol — a stable HTTP contract that works in plain PHP, Laravel, or Pinoox. How it works (30-second version) sequenceDiagram participant Browser participant API participant Disk Browser->>API: POST /init (filename, size, fingerprint) API-->>Browser: upload_id, chunk_size, missing_indexes loop Each part Browser->>API: POST /upload (chunk + SHA-256 hash) API->>Disk: store part end Browser->>API: POST /complete API->>Disk: assemble file API-->>Browser: done ✓ Resume is built in. The client sends a fingerprint ( name:size:lastModified:type ). If the connection drops, the same file picks up where it left off — only missing parts are re-uploaded. Integrity too. Each part gets a SHA-256 chunk_hash . The server can reject corrupted chunks before they pollute your disk. Server side: 10 lines of PHP composer require pinoo

I promised myself that starting this week I'd switch to lighter topics. But on Monday, my JSNation...

O Event Loop é o mecanismo responsável por decidir quando callbacks e continuidades de operações assíncronas devem ser executados. Ele não executa operações de I/O diretamente, mas organiza a ordem em que elas retornam para o JavaScript. Essa arquitetura permite que o Node.js mantenha uma única thread de execução para JavaScript, enquanto delega operações de rede, disco e sistema operacional para componentes especializados do runtime e do próprio sistema operacional. Início Quando iniciamos um processo Node.js, o runtime carrega o arquivo de entrada da aplicação e executa todo o código síncrono disponível na Call Stack. Somente após essa etapa o Event Loop passa a assumir o controle do fluxo da aplicação, verificando continuamente quais callbacks estão prontos para execução. │ timers │ └─────────────┬─────────────┘ │ v ┌───────────────────────────┐ ┌─>│ pending callbacks │ │ └─────────────┬─────────────┘ │ ┌─────────────┴─────────────┐ │ │ idle, prepare │ │ └─────────────┬─────────────┘ ┌───────────────┐ │ ┌─────────────┴─────────────┐ │ incoming: │ │ │ poll │<─────┤ connections, │ │ └─────────────┬─────────────┘ │ data, etc. │ │ ┌─────────────┴─────────────┐ └───────────────┘ │ │ check │ │ └─────────────┬─────────────┘ │ ┌─────────────┴─────────────┐ │ │ close callbacks │ │ └─────────────┬─────────────┘ │ ┌─────────────┴─────────────┐ └──┤ timers │ └───────────────────────────┘ Trecho retirado da documentação principal. Sobre o Event Loop Durante muito tempo tratei o Event Loop como um dos conceitos mais complexos do Node.js. Depois de estudar a documentação oficial com mais calma, percebi que a dificuldade não está no Event Loop em si, mas na quantidade de conceitos diferentes que normalmente são apresentados ao mesmo tempo: libuv, Call Stack, Promises, Microtasks, Sistema Operacional e I/O. Quando isolamos o papel do Event Loop, ele se torna surpreendentemente simples. Definindo os passos e apresentando o iceberg 🧊 O Event Loop não executa trabalho. Ele agenda tr

Most dashboard icon problems do not come from bad icons. They come from good icons used with the wrong meaning. You search for users , pick a clean SVG icon, place it in the sidebar, and move on. Then later you need another icon for: Customers Team members Account owners Permissions Audiences Invited users Admins Suddenly, the same “user” metaphor has to carry too many meanings. That is where SaaS dashboards often start to feel noisy. Not because the icons are ugly. Not because the SVGs are technically wrong. Not because the design system is broken. Because the icon choices were made by keyword instead of meaning. Keyword search is only the first step Most developers choose icons like this: Need an icon for billing? Search billing . Need an icon for users? Search users . Need an icon for analytics? Search chart . Need an icon for settings? Search settings . That works for finding candidates. But it does not solve the real UI problem. A keyword tells you what the icon is related to. It does not tell you what the icon means in your product. For example, search for settings . You might find: A gear Sliders A wrench Control knobs A preferences panel A tune icon They all match the keyword. But they do not say the same thing. A gear usually means global settings. Sliders suggest adjustable preferences or filters. A wrench feels technical or maintenance-oriented. Control knobs suggest fine tuning. A panel icon may suggest a configuration screen. The same keyword can point to different mental models. And in a dashboard, mental models matter more than decorative accuracy. SaaS dashboards are meaning-dense interfaces A marketing website can sometimes get away with decorative icons. A SaaS dashboard cannot. Dashboards are dense. They contain navigation, actions, status indicators, tables, filters, empty states, permissions, billing screens, integrations, reports, and settings. Users do not look at each icon in isolation. They scan. They compare. They move quickly. They expect

Texto com base no livro "Manual de DevOps" WIP significa "Work In Progress". É uma métrica essencial que representa a quantidade de trabalho iniciado, mas ainda não concluído. Na prática, ela ajuda a entender quantos tickets, tarefas, histórias ou demandas estão sendo executados simultaneamente pelo time. WIP Alto (Ruim) Time com 5 pessoas 20 histórias abertas Todos pegam várias tarefas ao mesmo tempo Dezenas de branches simultâneas Dezenas de PRs simultâneos WIP Baixo (Bom) Time com 5 pessoas Apenas 5 histórias abertas Cada pessoa trabalhando em uma tarefa por vez O time termina as tarefas antes de começar outras Menor Lead Time Essa métrica é essencial para uma boa estratégia de DevOps, além de ser um baita indicador para a saúde do projeto ou da companhia. Quanto maior o WIP: Maior troca de contexto Mais conflitos de merge Mais difícil rastrear e validar as entregas Maior "latência" no tempo de aprovação dos PRs Se seu time está começando muitas frentes e terminando poucas demandas, você está com um WIP alto, e isso afeta diretamente a qualidade das entregas e a qualidade de vida das pessoas. Sei que WIP aparece bastante nos princípios Lean, porém ainda não li o suficiente sobre o tema para me aprofundar nele.