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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

You've written retry logic. It probably looks something like this: async function withRetry ( fn , retries = 3 ) { for ( let i = 0 ; i < retries ; i ++ ) { try { return await fn (); } catch ( err ) { if ( i === retries - 1 ) throw err ; await new Promise ( r => setTimeout ( r , 200 * ( i + 1 ))); } } } You test it locally. You simulate a slow dependency like this: const fakeDB = async () => { await new Promise ( r => setTimeout ( r , 200 )); // simulate DB return { id : 1 , name : ' test ' }; }; Your retry logic works. Tests pass. You ship it. Then in production, your app starts dropping requests under load. The problem isn't your retry logic. It's your fake. Real dependencies don't have flat latency Here's what your Postgres instance actually looks like in production: p50: 5ms — half of all queries finish in under 5ms p95: 50ms — 95% finish under 50ms p99: 200ms — 99% finish under 200ms p99.9: 2000ms — that one unlucky query during a GC pause Your setTimeout(fn, 200) simulates the worst case, every single time. That's not how production works. And because it's not how production works, your retry logic has never actually been tested against reality. The bugs hide in the variance — not in the slow case, but in the unpredictability. What the real distribution looks like Latency in distributed systems follows a lognormal distribution . It's right-skewed: most requests are fast, a meaningful minority are slow, and a small tail is very slow. This shape comes from how real systems work: GC pauses — Java, Go, and even Node's garbage collector occasionally stops the world Cold caches — first query after a cache miss is always slower Network jitter — packet routing isn't deterministic Noisy neighbors — other workloads on the same hardware compete for resources Connection pool exhaustion — when all connections are busy, new queries wait None of these are constant. They're random, rare, and multiplicative — which is exactly what produces a lognormal shape. Why this matters fo

GraphQL: A Complete Guide for Developers in 2026 NEWS: MY GAME JUST LAUNCHED Flip Duel Card Battle - Apps on Google Play Outsmart rivals in 1v1 card duels. Joker, bluff, ranked PvP. 5 rounds. play.google.com If you have built more than a couple of APIs, you have probably felt the friction of REST at scale. You ship an endpoint, the frontend team asks for one more field, you version the route, the mobile team needs a different shape of the same data, and six months later you are maintaining /v3/users/:id/full next to /v2/users/:id/summary and nobody remembers which one the Android app actually calls. GraphQL was built to kill that exact pain. It is a query language and runtime that lets clients ask for precisely the data they need — no more, no less — from a single endpoint, against a strongly typed schema that doubles as living documentation. This guide walks through GraphQL from first principles to production concerns. It is aimed at working developers, so expect schema definitions, resolvers, real queries, the N+1 problem, federation, security, and the parts of the ecosystem that actually matter in 2026. By the end you should be able to decide whether GraphQL belongs in your stack and how to build it without shooting yourself in the foot. What GraphQL Actually Is GraphQL is a specification, not a library or a framework. It was created at Facebook in 2012 to power their mobile apps, open-sourced in 2015, and is now governed by the GraphQL Foundation under the Linux Foundation. The spec defines a query language, a type system, and an execution model — but it deliberately says nothing about which database you use, which programming language you implement it in, or how you transport requests over the wire. That last point trips people up, so let it sink in: GraphQL is transport-agnostic and storage-agnostic. Most implementations run over HTTP with JSON, but that is a convention, not a requirement. Your resolvers can pull data from PostgreSQL, a REST microservice, a gR

En una gestoría del automotor, consultar las multas de un auto era entrar a 33 sistemas distintos (Provincia, CABA, municipios), cada uno con su captcha y sus caídas. Lo automatizamos con una sola API, la de Multita , y comparto cómo quedó porque sirve a cualquiera que arme herramientas para el rubro automotor o fintech en Argentina. El problema Las infracciones de tránsito en Argentina no viven en un solo lugar. Hay sistemas provinciales (Buenos Aires, Santa Fe, Entre Ríos, Misiones, Chaco, Salta, Mendoza) y municipales (decenas). Ninguno habla con el otro. Consultar a mano son 15 a 20 minutos por vehículo. La solución: una request, todas las jurisdicciones La API de Multita recibe una patente, un DNI o un CUIT y devuelve, en JSON, las actas de cada jurisdicción con su monto y su estado. curl -X POST https://multita.com.ar/api \ -H "X-Api-Key: TU_KEY" \ -H "Content-Type: application/json" \ -d '{"tipo":"patente","valor":"AB123CD","jurisdicciones":"todas"}' { "resultados" : [ { "jurisdiccion" : "pba" , "nombre" : "Provincia de Buenos Aires" , "cantidad_actas" : 2 , "total_oficial" : 418500 }, { "jurisdiccion" : "caba" , "nombre" : "CABA" , "cantidad_actas" : 1 , "total_oficial" : 95000 } ], "resumen" : { "cantidad_actas" : 3 , "total_oficial" : 513500 } } Lo que nos ahorró Pasamos de 15-20 minutos por auto a segundos, y de cuatro ventanas abiertas a una sola llamada. Para una gestoría que cotiza decenas de carteras por día, es la diferencia entre atender 10 clientes o 30. Datos clave Cubre 33 jurisdicciones argentinas (provinciales y municipales), por patente (dominio), DNI o CUIT. Respuesta en JSON al instante; opcional, el total ya cotizado con tu pricing. Hay también una consulta web gratis para probar sin integrar nada. Si tenés una gestoría o estudio y querés esto andando sin programar, escribinos a BA Gestoría y te lo dejamos listo (y un descuento si venís de este post). Docs de la API: https://multita.com.ar/api

Here's the thing nobody warns you about when you put Supabase behind a "real" backend. My stack is React + FastAPI + Supabase Postgres. Every write goes through FastAPI. Every endpoint checks the user, the role, the ownership. I audited that backend HARD — rate limits, JWT validation, RLS, the whole thing. I was proud of it. And none of it mattered for the two holes I actually shipped. Because the Supabase anon key lives in the browser. It HAS to — that's how supabase-js talks to your project. Which means every logged-in user is holding a key that talks to Postgres directly . Not through my FastAPI. Around it. That anon key is a SECOND API. And I'd spent months hardening the first one while the second one sat there, wide open, the whole time. Hole #1 — the answers were just... readable Quiz questions live in quiz_options , one is_correct boolean per option. My backend never sends is_correct to a student before they submit. Obviously. But the browser doesn't have to ask my backend. // any logged-in student, straight from the console: const { data } = await supabase . from ( ' quiz_options ' ) . select ( ' question_id, label, is_correct ' ) // <- the answer key. all of it. The RLS policy said "authenticated users can read quiz_options ." Totally true for the rows. It just also handed back the column that decides the grade. The answer key. To anyone with a login and ten seconds of curiosity. Fix: column-level REVOKE SELECT from the client role, and let the backend be the only thing that ever reads is_correct . (PR #775.) Hole #2 — they could WRITE things they shouldn't Same class of bug, bigger blast radius. The default Postgres grants let the client role insert/update far more than I'd realized — including a path toward forging a certificate. Nobody did it. But "nobody did it yet" is not a security model! So I stopped patching table by table and flipped the whole thing: -- kill the client's entire write surface, then grant back the ONE thing it needs ALTER DEFAULT PRI

The build-vs-buy question gets answered wrong in both directions. Scrappy teams build things they should have bought, wasting six months reinventing Stripe. Enterprise teams buy things they should have built, ending up with a duct-taped stack of ten SaaS products that cost more than a full-stack engineer. The real answer depends on five questions most decision frameworks don't ask. This guide is a practical walkthrough for anyone trying to figure out the right call for their own business. The Myth That Distorts Every Build-vs-Buy Conversation "Buying is cheaper." This is the default assumption, and it's wrong often enough to be dangerous. Buying looks cheaper because the cost is monthly instead of upfront -- a psychological trick, not an economic one. Run the numbers on any SaaS tool over 5 years and you'll usually find the cost lands within 2x of building custom. Sometimes below. The real cost difference is not price; it's time, flexibility, and ownership. When you buy: You spend less today, more in year 3 You get speed now, rigidity later You trade money for control You own none of the code When you build: You spend more today, less per year You trade speed now for flexibility later You trade money for control You own the code and can change anything Both are rational trades. The question is which one matches the stage and strategy of your business. When Buying Wins Start with the easy case. Buy off-the-shelf when: 1. The problem is generic and solved. Email hosting, payment processing, accounting, HR payroll, customer support tickets, video conferencing, file storage. These are solved problems. Building your own is nearly always the wrong call. 2. The space has mature competitive options. If there are 5 reputable companies competing on price and features, you benefit from that competition. Building custom takes you out of it. 3. Your process is standard. If you do exactly what every other company in your vertical does, a tool built for every company in your verti

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Filament's export action is great. It's quick to set up, supports queued exports, includes column mapping, handles notifications, and keeps a history of generated files through the Export model. For most use cases, it's exactly what you need. But I recently ran into a limitation that the native export couldn't solve. When XLSX isn't really Excel I was exporting financial data or measurements from a Filament table. The export worked. The file downloaded. Excel opened it without any issue. The problem was that every amount was exported as text instead of a real numeric value. For an accountant, that creates several problems immediately: Excel formulas such as =SUM() don't work correctly Selecting a range of cells doesn't display totals in Excel's status bar Conditional formatting based on numeric values becomes unreliable Additional manual cleanup is required before the file can be used Technically the export contained the data. Practically, it wasn't usable. The root cause is simple: Filament's export system is designed around CSV-style exports. That's perfect for many scenarios, but it doesn't expose the full spreadsheet capabilities offered by PhpSpreadsheet and Laravel Excel . On top of that, I also had a second, completely different requirement: a yearly report with one worksheet per month, merged headers, borders, conditional formatting, and custom layouts. Not a table dump but a report. Why not just use Laravel Excel directly? Laravel Excel already solves all of these problems. It's built on PhpSpreadsheet and provides complete control over cell types, number formats, formulas, styling, and multiple worksheets. The obvious solution would have been to abandon Filament's export action entirely and build custom exports from scratch. But that means losing everything Filament already provides: Export modal and options form Column mapping UI Queue handling Progress notifications Download links Export model history I didn't want to rebuild all of that. I simply wanted

Your backend returned 200. The mobile app showed an error. The user tapped "Pay" three times. Three pending charges hit their account. One order was placed. Their balance was short. And your incident log showed zero failures. Every engineer on the team did their job. Nobody solved the problem. This is the most common way engineering teams fail, not through incompetence, but through excellent execution of the wrong unit of work. And until you recognise the difference between completing a task and solving a business problem , you will keep shipping systems that work perfectly and experiences that don't. The Ticket-Thinker vs. The System-Owner Most engineers early in their careers think in tickets. Ticket assigned → code written → tests pass → PR merged → ticket closed. Done. This is fine when you're learning. It's a liability when you're trying to grow. The engineer who closes tickets is useful. The engineer who asks "what problem does this ticket actually solve, and am I solving it in the right place?" that engineer is dangerous in the best way. Here's the distinction in practice. The backend engineer builds a payment endpoint. It processes charges correctly, returns the right status codes, has proper error handling. 100% test coverage. Ticket closed. The mobile engineer builds the payment screen. It calls the endpoint, handles the response, shows confirmation or error. Smooth UI. Ticket closed. The problem nobody owned: what happens when the network drops after the backend processes the charge but before the mobile app receives the confirmation? The backend: charge processed. No error. The mobile: timeout. Shows "Payment failed." User retries. The user: charged twice. Both engineers solved their assigned problem correctly. The business problem — charge the user once and confirm it reliably — went unsolved. Because that problem lived in the space between their tickets, and nobody was watching that space. Real Scenario 1: The Payment That Worked and Failed at the Same

Most "compress your image" websites upload your photo to a server. You don't need one. The browser's own canvas can re-encode an image at any quality — I built a drag-and-drop compressor in about 30 lines , and your photo never leaves your machine. 🗜️ Try it (drop a photo): https://dev48v.infy.uk/solve/day9-image-compressor.html 1. Catch the dropped file — locally drop . addEventListener ( " drop " , e => { e . preventDefault (); const file = e . dataTransfer . files [ 0 ]; // stays in the tab, 0 bytes uploaded loadImage ( file ); }); For sensitive images (IDs, screenshots), "never uploaded" is a real feature, not just a nicety. 2. Decode it into an <img> A dropped file is just bytes. Load it via a local blob: URL: const img = new Image (); img . src = URL . createObjectURL ( file ); await img . decode (); 3. Draw it onto a canvas Now the browser holds the raw pixels, detached from the original file format: canvas . width = img . naturalWidth ; canvas . height = img . naturalHeight ; canvas . getContext ( " 2d " ). drawImage ( img , 0 , 0 ); 4. Re-encode at a quality (this IS the compression) canvas . toBlob ( blob => { preview . src = URL . createObjectURL ( blob ); showSize ( blob . size ); }, " image/jpeg " , 0.7 ); // 0.7 = 70% quality JPEG and WebP are lossy — they discard detail the eye barely notices. That third argument is the entire compression dial; a small quality drop often halves the file size. 5. Hand the result back as a download link . href = URL . createObjectURL ( blob ); link . download = " compressed.jpg " ; // the browser saves it, no server The takeaway FileReader → Image → Canvas → toBlob is a surprisingly powerful local image pipeline. The same four steps do resizing, format conversion, cropping, watermarking — all client-side, all private. A whole category of "image tools" needs no backend at all. Open it and drop a photo.

Turbo Start Aisle is our agentic e-commerce starter on Shopify and Sanity. Instead of filters...

If you build with AI, three stories this week rhyme into one theme: the hype is colliding with the bill. Here's the builder's read on each — and what I'd actually do about it. 1. Most of a new TikTok feed is now AI slop A Kapwing study reported by Tubefilter hand-checked 10,742 videos across 20 categories and found that 59% of what a brand-new TikTok account sees is AI-generated . Kids content was the worst — 57% slop, with the #CartoonKids tag hitting 97% — and TikTok serves roughly 3x more slop than YouTube. Why builders should care: generation is now free and infinite, so volume is worthless as a moat. The scarce thing is taste and verification. If your product or content can be faked by a feed of bots, it will be. Polish, point of view, and "a human clearly did this" are the new differentiators. 2. Databricks grew 80% — but agents are eating its margins Per CNBC , Databricks' annualized revenue jumped about 80% to ~$6.9B, and its AI products now bring in $1.7B (up from $1.4B). The catch: the CEO says gross margin "will go lower" as customers run more agents. Why builders should care: this is the quiet tax of agentic software. An agent that loops, retries, and calls tools burns far more tokens than a single API call. If you're shipping agents, budget for inference at scale , not the sticker price on the pricing page. Profitability now lives in prompt efficiency, caching, and knowing when not to call the model. 3. 60% of US consumers are turned off by "AI" branding A WordPress VIP survey of 2,000 people, covered by TechCrunch , found that 60% reject "AI" in brand messaging , while 86% still want to check the original sources behind a claim. Why builders should care: "Now with AI!" is starting to read like a warning label. Sell the outcome, not the technology — "2x faster," "fewer errors," "your data stays private" — and cite where your results come from. Trust is becoming a feature you ship, not a slogan you bolt on. The takeaway Feeds are flooded, agents are cost

I Built a Vanilla JS Web App that Scored 100/100 Under Lighthouse’s New "Agentic Browsing" Audit. Here’s What It Means. If you have run a performance audit on PageSpeed Insights or Lighthouse recently, you might have noticed a fascinating new line item quietly slipping into the metadata report: Agentic Browsing . When I audited my free tool suite, Paktheta , I managed to hit the ultimate developer milestone— a perfect 100/100 across Performance, Accessibility, Best Practices, and SEO. But seeing that perfect score alongside the label "Agentic Browsing" got me thinking. What exactly is an AI-driven agent experiencing when it hits our sites, and why is this the new gold standard for web performance? Let's dive into what Agentic Browsing actually means for the future of optimization. What on Earth is "Agentic Browsing"? Historically, speed tests like Lighthouse were passive. A headless browser opened your URL, waited for the page to load, recorded metrics like First Contentful Paint (FCP) and Largest Contentful Paint (LCP), and closed the tab. It was a linear, predictable, and frankly synthetic snapshot. Agentic Browsing changes the paradigm entirely. Instead of a basic static script, modern auditing platforms use autonomous, intelligent browser agents. Guided by modern AI-driven browser control (using updated instances like HeadlessChromium), these agents don't just stare at your page—they explore it like a real human would. An agentic audit runner will: Identify interactive buttons and click them to test responsiveness. Scan form elements to see if they accept paste commands cleanly. Intelligently look for broken layout shifts (CLS) by dynamically scrolling and triggering micro-animations. Interact with JavaScript components to see if they block the main execution thread. In short: It simulates real, unpredictable human behavior at lightning speed. If your site relies on bloated frameworks that look fast initially but lock up the second a user tries to interact, an a

TL;DR — UUID v7 trie 13× plus vite que v4 en simulation B-tree (1M entrées), expose une empreinte mémoire identique, mais révèle son timestamp d'émission. UUID v4 reste le choix "zéro réflexion" pour les identifiants isolés. Le reste de cet article vous donnera les données pour décider. Introduction Les UUIDs sont omniprésents dans les systèmes modernes : clés primaires de bases de données, identifiants de sessions, tokens de traçabilité. Pourtant, le choix de la version impacte directement les performances en écriture et en lecture, la fragmentation des index, et — dans certains contextes — la confidentialité des données. UUID v4 (RFC 4122, 2005) est aujourd'hui la version par défaut de presque tous les ORM et frameworks. UUID v7 (RFC 9562, 2024) est son successeur moderne, conçu pour corriger son principal défaut : le désordre lexicographique. Dans cet article, nous allons mettre les deux en face avec des benchmarks réels sur des volumes de 100 000 à 10 millions d'UUIDs , analyser leur structure bit par bit, et vous donner une grille de décision claire. Structure interne : ce que contiennent ces 128 bits UUID v4 xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx Bits Contenu 0–47 Aléatoire 48–51 Version (0100 = 4) 52–63 Aléatoire 64–65 Variant (10) 66–127 Aléatoire 122 bits d'entropie pure. Aucune information temporelle. Chaque UUID est statistiquement indépendant des autres. UUID v7 019ed5c8-2a2f-7974-91f2-6ba1f313dcfa └──────────────┘ 48 bits = timestamp Unix en millisecondes Bits Contenu 0–47 Timestamp Unix (ms) 48–51 Version (0111 = 7) 52–63 Aléatoire (sub-ms ou compteur) 64–65 Variant (10) 66–127 Aléatoire 74 bits d'entropie + 48 bits de temps. Naturellement monotone : deux UUIDs générés dans la même milliseconde sont toujours distincts et ordonnés de façon cohérente. Lecture du timestamp (Python) : import uuid6 u = uuid6 . uuid7 () b = u . bytes ts_ms = int . from_bytes ( b [: 6 ], ' big ' ) # → 1781703125553 (ms depuis epoch Unix) Depuis la sortie de nos benchmarks : [00

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...