标签：#API

Expedia Group has open-sourced mockql-rs, a Rust CLI that fills @mock-annotated GraphQL fields with LLM-generated data at request time. It follows Airbnb's @generateMock in April and a GraphQL Foundation RFC opened in February. All three solve the same problem with different architectures, and two use the same directive name with incompatible semantics. By Steef-Jan Wiggers

TL;DR Today, there are numerous AI models, and it's important for businesses to be able to...

A single model can be unavailable, rate-limited, or temporarily slow. If your application already uses an OpenAI-compatible API, a simple fallback can make testing more resilient without introducing another SDK. This tutorial uses Node.js and the official OpenAI JavaScript package. It tries one model first and switches to a second model only when the first request fails. 1. Install the SDK npm install openai 2. Store the API key outside your code On macOS or Linux: export JINZEAI_API_KEY = "your_api_key_here" On PowerShell: $ env : JINZEAI_API_KEY = "your_api_key_here" Never commit a real API key. Rotate it immediately if it appears in a public repository, screenshot, or support message. 3. Create an OpenAI-compatible client import OpenAI from " openai " ; const client = new OpenAI ({ baseURL : " https://jinzeai.cc/v1 " , apiKey : process . env . JINZEAI_API_KEY , }); 4. Add a small fallback function const models = [ " deepseek-chat " , " qwen-flash " ]; async function completeWithFallback ( messages ) { let lastError ; for ( const model of models ) { try { const response = await client . chat . completions . create ({ model , messages , }); return { model , text : response . choices [ 0 ]. message . content , }; } catch ( error ) { lastError = error ; console . warn ( ` ${ model } failed: ${ error . status ?? " unknown status " } ` ); } } throw lastError ; } const result = await completeWithFallback ([ { role : " user " , content : " Explain model fallback in one sentence. " , }, ]); console . log ( `Model: ${ result . model } ` ); console . log ( result . text ); 5. Decide which errors should trigger fallback The minimal example retries on every error so the control flow is easy to see. A production application should be more selective. Fallback may be reasonable for: rate limits; upstream server errors; temporary timeouts; a model that is unavailable to the current account. Do not silently retry authentication errors. An HTTP 401 usually means the key is missing,

Vercel has made the v0 API generally available, enabling developers and AI agents to programmatically generate, iterate on, preview, and deploy applications through API calls. By Daniel Dominguez

With Truth API, Trump is "selling priority access to news he himself generates."

Retries are normal in distributed systems. A caller may time out after the server commits a decision, a queue may redeliver a message, or a webhook sender may repeat an event. A decision API that treats every request as new can double-charge, duplicate actions, or record conflicting outcomes. Give each business operation a stable key The idempotency key should identify the logical business request, not a network attempt. Store it with a normalized request fingerprint, processing state, outcome, rule version, and response. If the same key arrives with a different payload, reject it rather than returning an unrelated prior result. Handle concurrent duplicates atomically Two workers can receive the same key before either writes a result. Use a unique constraint, transaction, or compare-and-set operation so only one execution owns the request. Other attempts should wait, return an in-progress response, or read the completed outcome according to the API contract. Choose retention from business risk A short cache may stop immediate duplicates but fail when a delayed queue redelivers. A permanent record may create unnecessary storage or privacy burden. Document key expiry and what happens if a key is reused after that boundary. Put side effects behind the idempotent boundary If rule evaluation triggers a message or database write, use a transactional outbox or equivalent pattern so the decision and pending event are committed together. Consumers still need their own deduplication because downstream delivery is often at least once. Return decision provenance Include the decision ID, status, rule artifact version, timestamp, and whether the response was replayed. Do not regenerate a result under a newer rule version for a duplicate key unless the caller explicitly requests a new business operation. Test the failure modes, not only the happy path Cover concurrent duplicates, payload mismatch, worker crash after commit, delayed redelivery, key expiry, and downstream retry. Obs

يقوم Claude الآن بإرفاق بيانات تعريف العزو (provenance metadata) المشفّرة وفق C2PA بالملفات التي ينشئها. وينطبق الأمر نفسه على نماذج الصور من OpenAI و Gemini . هذا يعني أن إشارة العزو تصل سليمة إلى نقطة التحميل لأول مرة، لكن سلسلة معالجة الصور لديك قد تحذفها قبل أن يراها أي شخص. جرّب Apidog اليوم لا يحدث ذلك بنية سيئة؛ بل يحدث افتراضيًا. فمثلًا، تنشئ sharp().resize() ملفًا جديدًا بلا بيانات تعريف ما لم تطلب الاحتفاظ بها صراحةً. وينطبق ذلك أيضًا على ImageMagick وPillow ومعظم شبكات CDN الخاصة بالصور. يدخل الملف، ويخرج JPEG أصغر، ولا تخبرك السجلات أن بيانات العزو اختفت. هذه مشكلة قابلة للاختبار. ستتعلم هنا كيف تحدد المرحلة التي تحذف بيانات C2PA، وتثبت ذلك عبر رحلة رفع وتنزيل حقيقية، وتضيف فحصًا في CI يمنع عودة المشكلة. يتولى Apidog تنسيق سيناريو الـ API، بينما يتولى c2patool التحقق من صحة البيانات على مستوى البايت. ما الذي يتم تدميره بالفعل؟ بيان C2PA هو كتلة موقعة تشفيريًا ومضمّنة داخل حاوية الملف. يسجل من وقّع الأصل وما الذي ادعاه عنه. وبما أنه موقّع، فإن تغيير البايتات دون إعادة التوقيع يكسر التوقيع بطريقة يستطيع أي مدقق اكتشافها. النقطة المهمة هنا هي حاوية الملف : عندما تعيد كتابة الحاوية، قد يختفي البيان. العملية هل يبقى البيان افتراضيًا؟ نسخ أو نقل بايت ببايت نعم sharp().resize().toBuffer() لا ImageMagick عبر convert أو magick لا Pillow عبر Image.save() لا تحويل PNG إلى WebP أو JPEG إلى AVIF لا التحسين التلقائي في CDN للصور غالبًا لا لقطة شاشة لا إعادة الحفظ من محرر صور لا رفع إلى S3 دون تحويل نعم كل عنصر في عمود لا هو إجراء شائع في تطبيقات الويب: إنشاء صور مصغرة، توليد صور متجاوبة، التفاوض على التنسيق، أو إزالة EXIF لأسباب الخصوصية. كل خطوة منطقية بمفردها، لكنها قد تنهي سلسلة العزو بصمت. انتبه أيضًا إلى أن استخدام -strip لإزالة بيانات EXIF قد يكون مقصودًا، لأن EXIF قد يحمل إحداثيات GPS أو أرقامًا تسلسلية للكاميرا. لكن إزالة جميع البيانات الوصفية للتخلص من بيانات الموقع تزيل بيان C2PA كذلك. الحل هو إزالة البيانات الحساسة بشكل انتقائي، لا حذف الكتلة كاملة. أثبت المشكلة في دقيقتين قبل تغيير خط الأنابيب، تحقق من وجود المشكلة فعلًا. تحتاج إلى ملف واحد يحتوي على بيان

Roblox exposes game and group stats through public, unauthenticated endpoints — no login, no scraping tricks: GET https://games.roblox.com/v1/games?universeIds=<id>,<id>,... GET https://games.roblox.com/v2/groups/<groupId>/games?limit=50 I used them to pull the full public games list for a few independent creator groups that each ship multiple games in the same cheap-to-build "obby" template genre (think: dozens of studios building the same core traversal loop with a different skin). The question was simple: within one studio's own catalog, how concentrated is traffic in the single best title versus everything else they've shipped? The answer is the same shape every time: a small number of throwaway builds with near-zero traffic, and one outlier that accounts for nearly all of the studio's lifetime visits. Not "most games do okay and one does great" — more like one game is the studio, traffic-wise, and the rest are lottery tickets that didn't hit. As a sanity check against numbers that are already public knowledge (no anonymity concern), I ran the same script against Uplift Games' group (id 295182): $ python3 fetch_group_stats.py 295182 Group 295182: 371 published experiment(s) Total lifetime visits across all games: 44,412,921,224 Top title alone: 44,377,094,324 visits (99.9% of the group's total traffic) Visit-count distribution: 0-10K: 355 game(s) 10K-500K: 12 game(s) 500K-5M: 2 game(s) 5M-50M: 1 game(s) > 50M: 1 game ( s ) One title (Adopt Me) is 99.9% of that group's entire lifetime traffic across 371 shipped experiments. Same power-law concentration as the smaller, anonymized groups in the full writeup — just at a much larger scale. Why this is more than a curiosity : if you're building in a genre like this, the template itself is clearly not the moat — everyone in it ships near-identical mechanics. The variance between a 45-visit build and a 700M-visit build using the same template looks like it's mostly about timing and whatever the discovery algorithm rewar

Yulu aims to have a fleet of 200,000 bikes in the next two years and faster electric two-wheelers, aiming at new logistics use cases.

If you clip short-form video for money, you know Whop Content Rewards: hundreds of live campaigns paying $0.15–$20 per 1,000 views. The discover page lets you sort by budget. That sort is quietly costing you nights of work. Here's the number that changed how I pick campaigns: on the live board right now, 21% of active campaigns have never paid out a single cent. Big banner budget, $0 actually spent. A "$30,000 budget" campaign that has paid nobody in three weeks is not a $30,000 opportunity — it's a landing page. The problem: the board doesn't show you payout speed. You can see budget and budget left , but not how fast the money is actually moving — and that's the only number that separates a campaign that pays from a campaign that poses. The trick: the page already contains everything you need Every campaign card on Whop publishes three things: when it was funded, how much has been spent, and how many creators joined. From one snapshot — no monitoring, no state between runs — you can derive: dailyBurnUsd = budgetSpent / daysSinceFunded → is money moving? estimatedDaysLeft = budgetLeft / dailyBurnUsd → will it still be there? payoutPerCreatorUsd = budgetSpent / creators → what did the average clipper earn? budgetPace = "draining" | "healthy" | "slow" | "stalled" That last field is the shortcut. On today's board of 456 campaigns: pace meaning what to do draining <3 days of budget left skip — gone before your clip gains traction healthy 3–60 days this is where you clip slow 60–180 days fine, but budget may outlive the campaign stalled >180 days at current burn the "big budget" mirage — money posted, almost nobody paid null zero paid out so far unproven; could be brand new, could be dead Real example from today: two campaigns, both showing ~$30K budget. One burns $255/day and has paid the average creator $75 . The other burns $19/day — at that rate its budget lasts four years , which is a polite way of saying nobody is getting paid. On the default board they look ident

We shipped an MCP server for WhatsApp link generation — no API key required If you've ever needed an AI agent to validate a WhatsApp number, build a wa.me link, or generate a QR code on the fly, you've probably hand-rolled it: scrape a regex off Stack Overflow, write your own phone-format validator, maybe hit some undocumented endpoint. We got tired of watching that happen and shipped a small, open MCP server that does it directly. What it is WhatsUsernames.link already runs a free public REST API for validating WhatsApp usernames/phone numbers and generating wa.me links + QR codes. We just exposed the same logic over the Model Context Protocol , so Claude, and any other MCP client, can call it as native tools instead of you writing a fetch wrapper. Endpoint: https://whatsusernames.link/api/mcp No API key. No account. No signup form. Same open, IP-rate-limited model as the REST API (60 req/min for JSON tools, 20 req/min for QR generation, sliding window via Upstash Redis). The five tools Tool What it does validate_username Checks WhatsApp username ( @username ) format validate_phone Checks phone number format (8–15 digits, international) username_link Builds a wa.me link (+ short link) from a username, optional prefilled text phone_link Builds a wa.me link from a phone number, optional prefilled text qr_code Renders a QR code (PNG or SVG) for a wa.me link, custom size/colors Every tool wraps the exact same services/ and validate-* functions the REST API uses — there's no separate business logic to drift out of sync. If the REST endpoint says a number is valid, the MCP tool agrees, because it's the same code path. Connect it Drop this into your MCP client config: { "mcpServers" : { "whatsusernames" : { "url" : "https://whatsusernames.link/api/mcp" } } } That's it. No stdio process to spawn, no local install — it's a stateless Streamable HTTP transport running on Vercel, same domain as the site. Why build this Two reasons. Practical: every agent that needs to hand a u

Sometimes your application can reach an external service from one server, but not from another. I ran into this problem while working on one of my projects. I needed my server in Iran to communicate with Telegram, but the connection wasn't reliable from inside Iran. Instead of moving the whole application, I built a small intermediate service: Signed Webhook Receiver It is a lightweight FastAPI service that receives requests signed with an RSA private key and verifies them using the corresponding public key before processing them. Your Server | | RSA Signed Request v Webhook Receiver | | HTTP Request v External Service The receiver can be useful for: Secure server-to-server communication Webhooks and internal APIs Acting as a controlled proxy/gateway Connecting servers across different network environments Payment integrations where a provider requires requests from an Iranian IP For example, if your main application is hosted outside Iran but a payment gateway only accepts requests from Iranian IP addresses, an Iranian server can act as the intermediate gateway: Foreign Server | | Signed Request v Iranian Gateway Server | v Payment Gateway The important part is that this isn't an open proxy. Requests can be authenticated and the gateway can be restricted to specific operations and destinations. The project is built with Python, FastAPI, Cryptography, Docker, and Traefik and is open source. View the project on GitHub I also wrote more technical notes and development articles on my website: Building a Secure Webhook Receiver for Server-to-Server Communication | CyberHuginn

REST works great while your API describes resources. But as soon as the domain becomes verb-shaped - recalculateInvoice , mergeAccounts , assignTask - you end up bending verbs into nouns and arguing about which HTTP method cancels an order. JSON-RPC 2.0 cuts through all of that: every call is just method + params , one endpoint, a spec that fits on two pages, and batching out of the box. In this article we will build a working JSON-RPC 2.0 API on Symfony: a task tracker with DTO validation, batch requests and generated OpenAPI documentation. There is surprisingly little code to write: methods are declared with attributes, validation is derived from property types, and Swagger is generated by a console command. Everything below lives as a ready-to-run project on GitHub: symfony-jsonrpc-api-demo - clone it and poke it with curl while you read. We will use the otezvikentiy/json-rpc-api bundle (PHP 8.2-8.5, Symfony 6.4/7/8; this article uses PHP 8.4 and Symfony 7.4). Full disclosure: I am the author of the bundle. It has been running in production for three years - internal fintech tooling, an HRM system - nothing glamorous load-wise, but the correctness, logging and audit requirements were real, and they shaped most of what you will see below. Installation composer create-project symfony/skeleton: "7.4.*" tasks-api cd tasks-api composer require otezvikentiy/json-rpc-api If Flex has contrib recipes enabled, the bundle registers itself. If not, it is two lines by hand: // config/bundles.php return [ // ... OV\JsonRPCAPIBundle\OVJsonRPCAPIBundle :: class => [ 'all' => true ], ]; Wire up the route and a minimal config: # config/routes/ov_json_rpc_api.yaml ov_json_rpc_api : resource : ' @OVJsonRPCAPIBundle/config/routes/routes.yaml' # config/packages/ov_json_rpc_api.yaml ov_json_rpc_api : access_control_allow_origin_list : - ' http://localhost:8000' The bundle registers a single route, /api/v{version} - every request goes through it. Note the CORS list format: these are ful

🚀 Shipping a major update to my portfolio After weeks of designing, developing, and refining, I'm excited to share the latest version of my personal portfolio. Rather than building another static portfolio, I wanted to treat it like a real product—focusing on performance, interaction design, accessibility, analytics, and user experience. 🌐 Live: https://www.alanbabychan.online What I built 🟩 Matrix Theme A cyberpunk-inspired dark mode featuring animated binary effects, glowing UI elements, and an immersive developer experience. ⬜ Paper Theme A clean, modern light mode designed with readability, visual hierarchy, and clarity in mind. 🎵 Interactive Audio System •Background music •UI sound effects •Dedicated settings panel •Adjustable volume controls •Built using the Web Audio API 🖱️ Interactive Cursor A custom mouse-follow glow and subtle cursor interactions that enhance the browsing experience without becoming distracting. ✨ Micro-interactions Hover states, smooth page transitions, animated UI components, and responsive visual feedback to make every interaction feel intentional. 📖 UX & Accessibility Built around clear typography, intuitive navigation, responsive layouts, and accessibility-focused design to provide a consistent experience across devices. 📊 Performance & Analytics Built with Next.js and optimized for speed, SEO, and scalability. Implemented a complete Google Analytics 4 setup including: •SPA page tracking •Google Consent Mode v2 •Custom event tracking •User interaction analytics Tech Stack: Next.js • React • Tailwind CSS • Framer Motion • Web Audio API • Google Analytics 4 • Microsoft Clarity Coming Soon... 👀 I'm currently building a personal AI assistant that will allow visitors to interact with my portfolio, ask questions about my projects, experience, and skills, and explore everything conversationally. What I learned This project pushed me to dive deeper into: •Theme architecture •Frontend performance optimization •Animation systems •Custom UI inte

Receiving a webhook is easy. Building a webhook pipeline that survives duplicate deliveries, delayed events, missing messages, invalid signatures, and seller authorization changes is the real engineering work. This guide explains how to build a production-ready pipeline for TikTok Shop Customer Service messages—from HTTPS ingress to history reconciliation. First, Understand the Scope TikTok Shop Customer Service API is designed for conversations between buyers and sellers in a TikTok Shop. It is not an API for reading ordinary TikTok direct messages. Before implementation, confirm that: Your application has access to the required Customer Service API scopes. The seller has authorized your application. The target shop is correctly mapped to your internal workspace or tenant. Your webhook endpoint is publicly accessible over HTTPS. Customer Service API access is inactive by default and requires approval. See the official Customer Service API overview and app features documentation . If these prerequisites are missing, changing webhook code will not solve the problem. Recommended Architecture A reliable implementation separates webhook acknowledgement from business processing: TikTok Shop | v HTTPS webhook ingress | +-- Verify signature using raw request body | +-- Insert event into a durable inbox | +-- Return HTTP 200 within 3 seconds | v Message queue | v Normalize, deduplicate, and route | v Customer service workspace ^ | History reconciliation worker The webhook request should not wait for: CRM updates AI-generated replies Media downloads Ticket creation Search indexing External notifications Persist the event, acknowledge it, and process it asynchronously. Subscribe to the New Message Event For incoming customer service messages, subscribe to NEW_MESSAGE , identified as event type 14 . You can configure the subscription in TikTok Shop Partner Center or through the webhook configuration API. The official event reference is available in the New Message webhook docu

It's 3 AM. A production service is misbehaving, you're on-call, and you'd love an agent that can pull the service's health and tee up a restart for you. The catch is obvious: an agent with raw access to a DevOps API is a liability. One bad call could scale you into a huge bill or delete an incident record you needed. So the real question isn't "can the agent reach the API." It's "which calls should it be allowed to make at all, and how should the dangerous ones be treated differently from the safe ones." That decision is what Skillgate handles, and it's the part we actually build and run in this post. Scope, up front This post is about the classification and curation layer: turning an OpenAPI spec into a governed set of skills. Skillgate decides which endpoints become tools, marks which are read-only, flags which writes should require approval, and denies the destructive ones outright. Wiring an approval flag to a live human-approval pause, and making that pause survive a crash, is the job of the Agent OS runtime, not Skillgate. We link to it at the end. The demo here does not implement that runtime, and this post does not pretend it does. The problem Skillgate solves Point an LLM at a DevOps API and you have three bad options: Expose nothing. The agent is useless. Expose everything. Now the model can call DELETE and scale on a whim. Hand-whitelist every route. It works until the API changes, then it rots. Skillgate replaces all three with opt-in curation plus automatic risk classification. You choose a small surface, and every endpoint on it gets a class based on its method and shape. From REST endpoint to agent skill Skillgate's input is an ordinary REST API described by an OpenAPI spec. Nothing about the API is agent-aware. It's the same deploy, scaling, and incident routes your platform already exposes. Each endpoint is described in the standard OpenAPI shape: a method, a path, some parameters, a description, and tags. A representative operation from the DevOps

Most teams don't consciously choose their messaging infrastructure. They inherit it. Someone used Service Bus on the last project, it worked fine, and now it's the default answer for every async communication problem that comes up. Two years later, you're bending it into shapes it was never designed for, and the operational pain gets blamed on "distributed systems being hard" rather than on the actual culprit: a tool being asked to do a job it doesn't fit. The problem isn't that Service Bus, Event Grid, or Kafka are bad. It's that they solve genuinely different problems, and conflating them doesn't just create technical debt — it creates architectural liability that compounds over time. The Real Difference Is the Communication Contract, Not the Feature List When you put these three tools side by side in a comparison table, you'll find overlapping columns. All three move messages between systems. All three have some delivery guarantee story. That's where the surface-level comparison breaks down and people make bad decisions. The more useful question is: what contract does your system need to uphold with the data it moves? Service Bus is fundamentally about reliable, ordered processing with strong delivery guarantees. It's designed for the case where every message matters individually, where you need competing consumers pulling from a queue, where poison message handling and dead-lettering are first-class concerns. If you're coordinating business process steps or handling financial transactions where exactly-once semantics matter, this is the right shape of tool. Event Grid is about reactive routing. Something happened in your infrastructure or your application, and you want other things to respond to it. It's push-based, fan-out-friendly, and optimized for low-latency notification rather than high-volume throughput. It's not trying to be a buffer. If you're triggering downstream workflows in response to blob uploads, resource state changes, or custom application even

Here's a bug your test suite probably wouldn't catch. A backend developer refactors the user model. The id field — an integer since forever — starts coming back as a string: "42" instead of 42 . Every value is still "correct". Your assertion pm.expect(user.id).to.eql(42) fails, sure — but only on the one endpoint you asserted id on, not the other nine that return users. Meanwhile three client apps that did user.id + 1 are now computing "421" . That's structural drift , and it's what actually breaks API consumers: renamed fields, changed types, properties that quietly vanish. Field-by-field value assertions catch it patchily and by accident. Schema validation catches it systematically — and in Postman it costs about ten lines, because the ajv JSON-schema validator is built into the script sandbox. The ten lines In Scripts → Post-response on any request that returns a user: const userSchema = { type : " object " , required : [ " id " , " name " , " email " ], properties : { id : { type : " integer " }, name : { type : " string " }, email : { type : " string " , pattern : " @ " } } }; pm . test ( " Response matches the user schema " , () => { pm . expect ( pm . response . json ()). to . be . jsonSchema ( userSchema ); }); That single test now fails if id becomes a string, if email disappears, if name becomes an object — every structural mutation, whether or not you thought to assert on that field's value. For an endpoint returning an array of users: const userListSchema = { type : " array " , minItems : 1 , items : userSchema // reuse the object schema }; pm . test ( " List matches schema " , () => { pm . expect ( pm . response . json ()). to . be . jsonSchema ( userListSchema ); }); Share one schema across every endpoint The real power move: your API returns users from /users , /users/:id , /login , /teams/:id/members … and they should all be the same shape . Store the schema once as a collection variable (JSON, stringified), and every request validates against the sa

When building and validating US stock quantitative strategies, I used to focus solely on core market data metrics. Like most individual quantitative developers, I prioritized the integrity of price candlesticks and trading volume data, assuming that complete K-line datasets would guarantee reliable backtesting outcomes that align with real-market performance. This assumption held true for small-scale tests and short-cycle verification, until I encountered persistent inconsistencies between historical backtest reports and live trading results. After thorough troubleshooting of strategy logic, parameter settings, and sliding point simulation, I finally pinpointed the root cause — inconsistent and inaccurate timestamp processing from market data APIs, a trivial-looking but critical engineering detail that most developers overlook. Most engineering teams devote massive effort to verifying the accuracy of US stock API quote data, yet ignore standardized processing for time fields. In quantitative trading systems, timestamp offset and timezone disorder are far more impactful than superficial chart display errors. They directly distort candlestick combinations, disrupt technical indicator calculations, and ultimately mislead the entry and exit signal judgments of trading strategies. Core Requirement: Time-series consistency for valid backtesting Market data is essentially a continuous time-series stream, where price and volume merely represent transaction outcomes at specific timestamps. The time dimension acts as the fundamental anchor that defines the exact position of every single trade in the market timeline. Unlike A-share market data that adopts a unified time standard, US stock data providers deliver multiple incompatible time formats across different APIs, including pure UTC time, US Eastern trading time, and original exchange timestamp fields. Without unified parsing and conversion logic in your program, timestamp misalignment and data dislocation are inevitable.

We’ve all been there: hunched over a keyboard at 3 AM, neck craned forward like a turtle, debugging a race condition. "Tech neck" isn't just a meme; it’s a productivity killer. As developers, our spine is our most underrated hardware. In this tutorial, we are going to build a Real-Time Spine Posture Monitor . We will leverage real-time human pose estimation and MediaPipe Python libraries to track your posture via your webcam. By the end of this guide, you'll have a system that detects when you're slouching and sends a system notification to keep your ergonomics in check. This project is perfect for those looking into OpenCV computer vision and developer ergonomics solutions. The Architecture 🏗️ The logic is straightforward: we capture video frames, process them through a pre-trained neural network to find body landmarks, and apply some basic geometry to determine if your posture is healthy. graph TD A[Webcam Feed] --> B[OpenCV Frame Processing] B --> C[MediaPipe Pose Landmark Detection] C --> D{Extract Shoulder & Ear Coordinates} D --> E[Calculate Neck Inclination Angle] E --> F{Angle > Threshold?} F -- Yes --> G[Trigger System Notification] F -- No --> H[Continue Monitoring] G --> B H --> B Prerequisites 🛠️ Before we dive into the code, ensure you have the following installed: Python 3.9+ MediaPipe : Google’s framework for cross-platform ML. OpenCV : For video stream handling. PyObjC : (For macOS) to trigger native system alerts. pip install mediapipe opencv-python pyobjc Step 1: Initialize the Pose Engine MediaPipe makes pose estimation incredibly easy. We’ll use the Pose solution, which provides 33 3D landmarks for the human body. import cv2 import mediapipe as mp import math # Initialize MediaPipe Pose mp_pose = mp . solutions . pose pose = mp_pose . Pose ( static_image_mode = False , model_complexity = 1 , enable_segmentation = False , min_detection_confidence = 0.5 ) mp_drawing = mp . solutions . drawing_utils Step 2: Calculating the "Slouch" Angle 📐 To detect