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Section 1: Getting Started with Cypress 1. Installing and Setting Up Cypress Prerequisites Before installing Cypress, ensure you have Node.js installed on your machine. Cypress requires Node.js 18.x or 20.x and above. You should also have an existing React project or create a new one. Check your Node.js version by running this command in your terminal: node --version # Should output v18.x.x or higher Creating a React Project (Optional) If you don't have an existing React project, create one using Vite which is the recommended approach for new React projects: npm create vite@latest my-react-app -- --template react cd my-react-app npm install Installing Cypress Navigate to your React project directory and install Cypress as a development dependency. Cypress is a fairly large package, so the installation might take a minute or two: npm install cypress --save-dev # Or using yarn yarn add cypress --dev Opening Cypress for the First Time After installation, open Cypress for the first time. This will create the initial folder structure and configuration files: npx cypress open When Cypress opens for the first time, you'll see a welcome screen where you can choose between E2E Testing and Component Testing. Select E2E Testing to get started with end-to-end tests. Cypress will then prompt you to choose a browser. You can select Chrome, Firefox, Edge, or Electron. Choose your preferred browser and click Start E2E Testing in [Browser] . Adding NPM Scripts Add convenient scripts to your package.json for running Cypress tests: { "scripts" : { "dev" : "vite" , "build" : "vite build" , "cy:open" : "cypress open" , "cy:run" : "cypress run" , "test:e2e" : "start-server-and-test dev http://localhost:5173 cy:run" } } Tip: Use npm run cy:open for interactive development with the Cypress Test Runner. Use npm run cy:run for headless execution in CI/CD pipelines. 2. Understanding Cypress Project Structure Project Directory Overview After initializing Cypress, you'll notice several new fold

TL;DR Claude Code and the op CLI reorganized 690 credentials — four vaults, 390 items tagged, SSH agent configured — in one session. This is AI-native work: the agent operated the vault; the human set direction and approved via Touch ID. The CLI failed on 18 items with social-auth ( UNKNOWN field type) — hard failure, not graceful degradation; a real reliability blocker for team-scale use. The bug was filed from the terminal via the GitHub CLI in the same session it was found. If your password manager has a CLI, you already have everything needed to run this. I've been a 1Password user for years. Not in a conscious, intentional way — more in the way you use a good chair: it became part of how I work and I stopped thinking about it. That changed when I set up a new machine. I had to install 1Password, wire up the SSH agent, reconnect the CLI, re-authenticate everything. The process took longer than it should have because I'd never written down what I'd built. I'd only accumulated it. And somewhere in the middle of that setup, it hit me: I had 690 credentials in one flat vault — logins from jobs I'd left years ago sitting next to active API keys, personal bank accounts mixed with infrastructure credentials, demo user passwords alongside production secrets. The kind of accumulation that happens when a tool works well enough that you never stop to organize it. I'd been meaning to clean it up for a long time. I never did, because the job is exactly the kind of work that's too tedious to do manually and too important to skip: touch every item, make a judgment call, move it somewhere sensible, repeat 690 times. Then I realized: with Claude Code and the op CLI, this was now actually possible. Not assisted — the agent could do it. So I handed it the keys. What "AI-native" actually means here Quick context on timing: 1Password launched its SSH agent and CLI 2.0 in March 2022. Git commit signing via the vault came six months later. These are mature, stable features — not betas

Day 48 of building GoDavaii, and the toughest problem isn't the sheer volume of allopathic medicines or the complexity of their interactions. It's the invisible logic of 'Desi Ilaaj' - the home remedies and traditional practices deeply ingrained in Indian families for generations. When everyone knows the comfort and efficacy of 'haldi-doodh' (turmeric milk) for a cold, how does an AI health platform authentically verify and integrate that knowledge without replacing professional medical advice? This isn't just a cultural nod; it's a fundamental challenge for any health AI truly built for India. Global competitors like Epocrates or drugs.com, while excellent within their scope, are entirely English-centric and focused on Western allopathic data. They have no framework for the millions of people who search for health guidance in Hindi, Tamil, or Marathi, and whose first instinct for a cough might be a herbal concoction, not an over-the-counter syrup. The Unspoken Truth About India's Health Landscape For a vast majority of Indian families, health decisions often involve a blend of modern medicine and traditional wisdom. From specific herbs to dietary adjustments passed down through generations, these practices are effective for many minor ailments. Yet, in the digital health space, they're largely ignored. Why? Because the data is fragmented, often anecdotal, and doesn't fit neatly into structured pharmacological databases. It's a goldmine of practical health knowledge, but also a minefield for safety if not handled with care. My realization as Pururva Agarwal, 27-year-old founder of GoDavaii, was simple but profound: if we truly want to serve families coming online in their mother tongue, our AI needs to understand and interact with this context. This means going far beyond just translating English medical terms into 22+ Indian languages. It means building a knowledge graph that can intelligently cross-reference traditional remedies with known active compounds, potent

If you're building or integrating Automated Test Equipment for aerospace or defence electronics, the technical requirements go well beyond "does the test pass." You need documentation that satisfies MIL-STD, AS9100, and DO-178C auditors — and an architecture that scales from prototype to production. Here's how modern Universal ATE systems are structured for defence-grade compliance. The Core Architecture A defence-grade ATE system has four functional layers: ┌─────────────────────────────────────────┐ │ Test Executive (LabVIEW) │ ← Orchestrates all test sequences ├────────────────┬────────────────────────┤ │ Instrument │ DUT Interface │ ← Hardware layer │ Control │ (ICT/JTAG/Func) │ ├────────────────┴────────────────────────┤ │ Data Management Layer │ ← Logging, traceability, reports ├─────────────────────────────────────────┤ │ Calibration & Verification │ ← Ensures measurement accuracy └─────────────────────────────────────────┘ Test Executive Design in LabVIEW The test executive controls the sequence, manages results, and handles failures. Key design principles: Test Sequence: 1. DUT identification (serial number scan or manual entry) 2. Pre-test self-check (verify instrument calibration status) 3. ICT phase — passive component verification 4. JTAG boundary scan — IEEE 1149.1 interconnect verification 5. Power-on functional test — operational verification 6. RF/signal analysis — if applicable to DUT type 7. Report generation — automatic, timestamped 8. Pass/fail disposition record JTAG Integration via IEEE 1149.1 For high-density boards where bed-of-nails is not viable, JTAG boundary scan is implemented via a JTAG controller (e.g., XJTAG, Corelis, or ASSET InterTech) integrated into the LabVIEW environment: LabVIEW → JTAG Controller API → Scan Chain → DUT ICs The boundary scan description files (BSDL) for each IC define the test vectors. Your test executive loads BSDL files, generates scan chain topology, and runs interconnect tests automatically. Data Traceabili

Six months ago, I sat down with a YouTube playlist, a blank notebook, and one goal: learn Python. What I did not expect was how hard it would be, not the Python itself, but figuring out how to actually learn it. I started with a YouTube playlist. Simple enough. Except nobody tells you what to do after you watch a video. Do you rewatch it? Take notes? Jump straight to code? I had no system. I'd watch a concept, feel like I understood it, open VS Code, and stare at a blank file. That's when I realized I had fallen into passive learning. And passive learning in the age of AI is a particularly dangerous trap, because it's so easy to confuse activity with progress. I could watch a video, feel good. I could ask Claude to explain a concept, feel good. I could even ask AI to write code, read it, nod along, and feel like I'd learned something. I hadn't. I'd just consumed. There's a difference. The real moment of honesty came when I was stuck on a coding problem. My instinct, everyone's instinct now is to open ChatGPT or Claude immediately. And I knew, sitting there with the cursor blinking, that if I did that every single time I got stuck, I was building nothing. My brain would never develop the muscle of working through problems. I would be someone who can prompt AI to code, not someone who can think in code. And in a world where AI can already write decent code, the person who can't think independently isn't valuable. They're replaceable. So I had to build a system that forced me to actually learn. After a lot of trial and failure, I landed on a 5-phase checklist that I wrote out by hand and kept next to my laptop. Phase 1: is what I call First Contact — watch one focused video, then write a summary purely from memory, then discuss it with an LLM not to get answers but to pressure-test what I thought I understood. Phase 2: is Deep Understanding — read a written source, write proper notes, map the concept visually, and list every edge case and exception I can find. Phase 3:

In the previous article, we explored how to build our first CRUD API using FastAPI. While our API worked correctly, there was one major problem. We were storing data inside Python lists, which exist only in memory. If you've ever wondered how applications like Instagram, LinkedIn, or ChatGPT remember information even after a server restart, the answer is simple: databases. In this article, we'll solve the problem of in-memory storage by connecting our FastAPI application to SQLite using SQLAlchemy. If you haven't read the previous post, check it out: FastAPI for AI Engineers - Part 2: Building Your First CRUD API Ananya S Ananya S Ananya S Follow Jun 1 FastAPI for AI Engineers - Part 2: Building Your First CRUD API # ai # backend # fastapi # python 7 reactions Comments Add Comment 4 min read By the end of this article, you'll understand: Why in-memory storage is a problem What SQLite is What SQLAlchemy is How ORM works How to create database tables using Python classes How to perform CRUD operations using a real database The Problem with In-Memory Storage Previously, our application stored students inside a Python list. students = [ { " id " : 1 , " name " : " Ananya " , " department " : " CSE " , " cgpa " : 8.9 } ] This worked for learning CRUD operations. However, consider what happens when the server restarts: FastAPI Server Stops ↓ Python Memory Cleared ↓ All Student Data Lost This is unacceptable in real-world applications. We need a place where data can survive application restarts. This is where databases come in. What is SQLite? SQLite is a lightweight relational database. Unlike MySQL or PostgreSQL, SQLite doesn't require a separate database server. Instead, everything is stored inside a single file. students.db Advantages of SQLite: No installation required Lightweight Easy to learn Perfect for local development Great for small projects For this article, we'll use SQLite. What is SQLAlchemy? Before SQLAlchemy, developers often wrote raw SQL queries. Exampl

Ideogram 4.0 launched this week and within 48 hours it was available on seven platforms. That is unusual. Most model launches trickle onto one or two platforms over weeks. Ideogram went wide immediately, which suggests the open weights strategy is working as intended. Here is what you will pay depending on where you use it. fal.ai The cheapest API access. Turbo mode at three cents per megapixel. That is roughly three cents per 1K image. Balanced at six cents. Quality at ten cents. Pay-per-use, no minimums. If you are generating through an API, this is your starting point. Krea Included in all paid plans. Basic is $5.25 per month billed annually with 5,000 compute units. Pro is $21 per month with 20,000 CUs. The CU cost for Ideogram 4.0 specifically is not published yet, but Krea includes 150 plus models in their CU pool, so you are not paying extra for access. If you already use Krea for other models, Ideogram 4.0 is effectively free to try. ComfyUI Free if you have the GPU. The model is open weights at 9.3 billion parameters. Native ComfyUI support means you can download the weights and run it locally. No per-generation cost. No API calls. Just your electricity bill and GPU time. For volume generation or iteration, this is the cheapest path by far. Leonardo Announced as a day zero launch partner but the pricing page still lists Ideogram 3.0. Plans range from $12 to $60 per month with token allowances from 8,500 to 60,000. Third party models on Leonardo always consume tokens, no relaxed generation. Until they publish the 4.0 token cost, you are guessing. Assume it will be similar to their other premium models. Replicate The Ideogram 3.0 listing is live but 4.0 is not there yet. Replicate prices by hardware time rather than per-image, which can be cheaper or more expensive depending on your batch size and the GPU allocated. Worth checking when it lands. FLORA Available in FLORA. Pricing unclear. FLORA is primarily a creative platform, not an API provider, so you are

AI Does Not Cancel Reality I watched the conversation between Mo Gawdat and Marina Mogilko about the future of AI. The conversation is strong. It contains important ideas, but it also contains many claims that sound large in scale, although on closer inspection they rely on very broad generalizations. AI is indeed changing the labor market, education, startups, content, hiring, and ways of thinking. But it does not cancel money, connections, trust, the human vector, creativity, necessity, morality, or people’s ability to adapt. Video on YouTube AI in hiring: automation amplifies chaos Many people have entered the job market. Companies receive huge volumes of resumes. HR departments cannot handle the volume. It is natural that part of the selection process is moving to AI. But there is a serious problem here. Candidates are also starting to play against AI. Resumes are adjusted to vacancies. Cover letters are assembled around keywords. Profiles become optimized for the filter, not for real work. In such a system, the best specialist does not necessarily pass. Often, the person who understood the selection mechanism better passes. The result: the picture becomes cleaner, while the quality of the decision becomes lower. The company gets not the strongest candidate, but the candidate who matched the algorithm best. This leads to lower hiring quality, lower productivity, and slower development. “I built a startup in six weeks”: a product is not a startup The conversation includes the idea that an AI startup would once have taken years and hundreds of engineers, and now it can be built in weeks. Technically, this is true. Prototypes are now built faster. Small teams have powerful tools. One person can now do more than a group could do before. But two different things are mixed here. Building a product faster has become real. Building a startup faster has become real only when resources are present. A startup is not only code. A startup is money, connections, trust, reputa

Created a Google Chrome extension that instantly turns any highlighted text on a webpage into a Google Calendar event

Cloudflare recently described how a slowdown in its billing pipeline was traced to contention inside the query planning stage of ClickHouse. The team profiled the bottleneck and patched ClickHouse to replace an exclusive lock with a shared lock, drop the per-query copy of the parts list, and improve part filtering. By Renato Losio

Search results look simple from the outside. You type a keyword into Google, Bing, or another search engine, and you get a page of links, snippets, ads, maps, news, images, videos, and sometimes AI-generated answers. But if you have ever tried to collect search results at scale, you know it gets messy quickly. A result page is not just a list of links. It changes by country, language, device, location, query intent, and search engine. The same keyword can show different rankings in New York, London, Singapore, or Berlin. A page may include organic results, paid ads, local packs, shopping results, People Also Ask, news results, images, videos, or other SERP features. For humans, that is just a search page. For SEO teams, AI teams, data teams, and developers, it is a data source. That is where a SERP API becomes useful. What is a SERP API? SERP stands for Search Engine Results Page . A SERP API is an API that lets you collect search engine results in a structured format, usually JSON and sometimes HTML. Instead of manually searching a keyword or building a scraper to parse search result pages, you send a request to a SERP API with parameters such as: keyword search engine country language location device type output format The API then returns structured search data. A simplified response might look like this: { "query" : "best project management software" , "organic_results" : [ { "position" : 1 , "title" : "Best Project Management Software Tools" , "link" : "https://example.com" , "snippet" : "Compare features, pricing, and reviews..." } ] } This is much easier to work with than raw HTML. You can store it in a database, send it to a dashboard, compare rankings over time, feed it into an AI workflow, or generate automated reports. Why not just scrape search results yourself? You can build your own scraper. For a small test, that may be enough. You can send a request, parse the HTML, extract titles and links, and save the data. The problem starts when the workflow bec

Modern applications depend on secrets. Every application requires: Database Passwords API Keys SSH Keys TLS Certificates Cloud Credentials OAuth Tokens Service Account Keys The biggest question is: Where should we store them securely? Unfortunately many organizations still store secrets in: Git Repository Docker Image Application Config Files Environment Variables Shared Documents Excel Sheets This creates a massive security risk. This is why Secret Management platforms like HashiCorp Vault became critical in modern cloud-native environments. 🔗 Resources ** Support the Journey on GitHub: If you're following along, consider starring and forking the repo:** https://github.com/17J/30-Days-Cloud-DevSecOps-Journey What is a Secret? A secret is any sensitive piece of information used to authenticate or authorize access. Examples: Database Password AWS Access Key JWT Signing Key API Token TLS Certificate Private Key OAuth Secret If a secret gets exposed: Attacker ↓ Application Access ↓ Database Access ↓ Infrastructure Compromise What is Secrets Management? Secrets Management is the process of: Store Protect Rotate Control Audit sensitive credentials securely. A modern secrets management platform provides: Centralized storage Encryption Access control Secret rotation Audit logs Dynamic credentials Why Secrets Management Matters Imagine this scenario: database : username : admin password : Password123 committed into GitHub. Result: Developer Pushes Code ↓ GitHub Repository ↓ Credential Leak ↓ Database Breach This happens more often than people realize. The Problem with Traditional Secret Storage Many teams use: .env Files Kubernetes Secrets Configuration Files Hardcoded Passwords Problems: Difficult rotation No audit trail Poor access control Risk of accidental exposure Compliance failures What is HashiCorp Vault? HashiCorp Vault is a centralized secrets management platform designed to securely store, access, and manage secrets. Think of Vault as: Central Secret Bank for you

Table of Contents The Integration Problem That Broke Industry 4.0 MCP: The Vertical Connection Layer How MCP Connects to Servers, Tools, and Databases MCP in Real World Industrial Automation ACP: The Horizontal Communication Layer How ACP Works Under the Hood ACP in Real World Industrial Coordination The Six Precise Differences How They Work Together: The Complete Stack Decision Framework for Industrial AI Architects 1. The Integration Problem That Broke Industry 4.0 Industry 4.0 promised connected factories, intelligent automation, and seamless data flow between machines, systems, and humans. The technology arrived. The connectivity did not. The reason is a number called N times M. An enterprise manufacturing facility might have 12 AI agents across quality, maintenance, and planning — and 28 data sources including ERP, MES, SCADA, IoT sensors, databases, CAD repositories, and supplier APIs. Without a standard protocol: 12 agents multiplied by 28 data sources equals 336 custom integrations. Each integration is bespoke code. Each breaks when either side updates. Each requires maintenance. Each represents a point of failure and a security surface that must be independently managed. IBM VP Armand Ruiz stated this precisely: "Without a common standard, every integration is costly duct tape." MCP and ACP together replace 336 pieces of duct tape with two standard protocols — one governing how agents connect to systems, one governing how agents connect to each other. The smart manufacturing market is projected to reach 374 billion dollars by 2025 at 11.8 percent CAGR. Over 50 percent of companies in industrial automation are expected to adopt MCP-based connectivity. The integration problem is not theoretical. The solution is being deployed at scale right now. 2. MCP: The Vertical Connection Layer MCP connects agents to tools and data — the vertical integration layer. It handles the connection between an AI agent and everything it needs to interact with in the external worl

TL;DR — The DIFP protocol was designed to be data-compact and geo-aware from day one. We recently discovered it maps almost perfectly onto the Nostr event format. Here's how, and why it matters for decentralized food infrastructure. Background: What Is DIFP? DIFP (Djowda Interconnected Food Protocol) is an open protocol designed to sync food product data across distributed nodes — compactly, efficiently, and with geo-location awareness built in by default. One of its core design decisions is the PAD system (Preloaded Asset Distribution): Apps ship with a preloaded asset pack — item metadata, compressed images, category structure — all bundled at install time. Only price and availability need to travel over the wire during sync. This means the data footprint per product is tiny. Very tiny. Enter Nostr Nostr is a simple, open protocol for decentralized communication. One of its key specs: events support up to 64 KB of content . When we started exploring Nostr as a potential transport layer, we ran the numbers — and the fit was surprisingly clean. The Math: Products Per Event Baseline encoding A product represented with three fields: { "id" : 500 , "available" : true , "price" : 30000 } At this level of verbosity, a single 64 KB Nostr event can hold approximately: ~1,500 – 2,000 products Already useful. But we can do better. Optimized encoding Two key optimizations: 1. Drop the availability key — If a product entry exists in the JSON, it's available. If it's absent, it's not. No boolean needed. 2. Drop the field names — Instead of {"id": 500, "price": 30000} , just store: 500,30000 Field mapping is handled at the app level, not the protocol level. The device knows position 0 is the product ID, position 1 is the price (in smallest currency unit, e.g. cents). Result ~6,000 – 7,000 products per single Nostr event Possibly more, depending on the price distribution and ID ranges in a given catalog. Geo-Discovery: MinMax99 Cells DIFP uses a geo-cell system called MinMax99 to

Why good companies go bad and how great companies stay great Discussion | Link

GitHub热门项目 | The sandbox agent framework. | Stars: 4,519 | 126 stars today | 语言: TypeScript

This story was shared by a fellow developer on DEV who asked to remain anonymous. If you've got a story to tell — come find me. Your name won't appear anywhere. Based on real microservice security design patterns. About an engineer whose PR got blocked by an AI security system — he thought he was fixing a vulnerability. Turns out, someone had a vested interest in that vulnerability staying open. 1. $1,400,000 All-hands meeting. CTO James stood at the front, a number on the screen: $1,400,000 "This is what we're spending on security this year." He pointed at the number. "The biggest piece — right here." He clicked the remote. VoidSentinel's architecture topology appeared on screen. "VoidSentinel — an AI security platform. Integrated into our CI/CD pipeline. Starting today, every PR involving internal service-to-service calls — it reviews them automatically." The CEO didn't show up today. James didn't mention it. He looked straight at Mark — VP of Security. Mark took the mic. "VoidSentinel has been running in our pre-production environment for three weeks. It's caught 47 high-risk patterns. Zero false positives." He paused. " — Of course, some people might feel uncomfortable when their PR gets blocked. But this isn't personal. This is the security standard. " He wasn't looking at me. But I knew who he was talking about. 2. High Risk. Denied. The story started three weeks earlier. We had a payment service and a user service that talked to each other internally. They shared an old API key — one key across thirty-plus services, unchanged for five years. It wasn't that nobody knew. It just never made it to the top of the backlog. On Day 1, I opened a PR: add independent service-to-service auth between the payment and user services. Not much code — a new token exchange module, three call sites modified. Five minutes later, VoidSentinel's automated comment hit: "High-risk alert: Unauthorized internal access pattern change detected. This PR has been automatically rejected. C

I've been building a static Astro site on Cloudflare Pages over the last few weeks. Sharing the 3 deployment bugs that cost me the most time, in case they save anyone else the same loop. Setup Astro 5 + Cloudflare Pages + Tailwind 4. Content lives in a few JSON files; each page is a dynamic route mapped over the data. Free-tier hosting, no backend. Standard static-first stack. Bug 1: Trailing-slash 307 chain I started with trailingSlash: 'never' in Astro config. Build output went to dist/foo/index.html . Result: Astro emitted canonical tags as /foo (no slash), but Cloudflare Pages served /foo/ (auto-adding the slash via 307). Google Search Console flagged pages as "Redirect error" because the canonical URL pointed at a redirect chain instead of a real 200. I first tried build.format: 'file' to get flat dist/foo.html output, hoping that would bypass the trailing slash. That made it worse — Cloudflare still 307-stripped, but now to a non-existent .html file → 404. Fix: stop fighting the platform. ​ js // astro.config.mjs export default defineConfig({ trailingSlash: 'always', // ... }); ​ trailingSlash: 'always' plus default directory build aligns the canonical URL with what Pages actually serves. The redirect errors resolved on next re-crawl. Bug 2: _redirects rejected at deploy I tried to do a www → apex 301 in public/_redirects : https://www.example.com/* https://example.com/:splat 301! Cloudflare rejected the deploy with three validation errors: ​ Line 13: Only relative URLs are allowed. Line 22: Duplicate rule for path /foo. Line 23: Duplicate rule for path /bar. ​ Pages tightened _redirects validation — absolute-URL sources aren't accepted anymore. The duplicate errors were because Astro's own redirects config in astro.config.mjs generates HTML meta-refresh files that Pages parses as implicit redirect rules — conflicting with my explicit ones. Fix: delete _redirects entirely. Use a Cloudflare Redirect Rule from the dashboard for cross-host 301s (Wildcard pattern,