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How SCPN Phase Orchestrator v0.8.0 turns Kuramoto dynamics into a domain-agnostic control compiler, why we verify math across five languages, and the honest truth about building a Boeing 747 when most people need a bicycle. The $5.2 Billion Blackout That Started This On August 14, 2003, a cascading failure in the US Northeast power grid left 55 million people without electricity. The final report cited something deceptively simple: synchrony loss . A generation unit in Ohio drifted out of phase. The protective relays, designed to prevent damage, tripped in sequence. One desynchronized oscillator triggered a cascade that propagated across 265 power plants in nine minutes. The grid had controllers. It had models. What it lacked was a shared, reviewable language for coherence — a way to ask, in real time: "Is this synchrony valuable or dangerous? And if I touch this knob, can I prove what will happen before the electrons move?" That question is why I built SCPN Phase Orchestrator . It is not a Kuramoto simulator. It is a coherence control compiler — a system that takes any cyclic process (power waves, cloud retries, neural spikes, traffic signals) and compiles it into a unified phase space where synchrony can be observed, classified, and modified with bounded, auditable, replayable actions. Version 0.8.0 just shipped. It includes something I have not seen in any other open-source oscillator library: cross-language mathematical parity verification and Lean proof obligations for safety-critical control chains. This post is the honest story of why we built it, how it works, and where we might have gone too far. The Fragmentation Problem If you work on synchrony in 2026, you live in silos. Power engineers use PSS/E or PowerFactory with swing-equation models. Cloud operators use Airflow, Kestra, or Temporal for workflow orchestration — none of which understand phase dynamics. Neuroscientists use FieldTrip or MNE-Python for EEG phase analysis, but the tools stop at visualiza

One thing I didn’t expect while trying to improve my workflow: The actual tasks aren’t what takes most of the time. It’s all the context switching around them. Things like: - jumping between tools just to complete one small step - copying data from one place to another - stopping what you’re doing to handle something repetitive - switching back and figuring out where you left off Individually it’s nothing. But over a day it adds up to constant interruptions. And it’s weirdly more draining than the work itself. I started paying attention to that instead of just the tasks, and reducing those switches made a bigger difference than trying to “optimize” the work itself. Curious if others notice the same thing or if it’s just me submitted by /u/huncho-mohammed [link] [留言]

In Chapter 2 we wrote our first tests and hit two bugs. Before we add more, we need the one skill everything else rests on: finding elements reliably . Get this right and your tests survive redesigns; get it wrong and they break every sprint. Code for this chapter is tagged ch-03 in the repo: https://github.com/aktibaba/playwright-qa-course — see src/tests/ui/locators.spec.ts . Locate the way a user perceives The brittle instinct is to grab elements by their structure — CSS classes, nth-child , XPath. All of that changes the moment a developer touches the markup. Playwright's recommended locators instead target what a user (and a screen reader) perceives: the role, the label, the visible text. Use them in this order of preference: getByRole — the role + accessible name (covers the vast majority of cases) getByLabel — form fields by their <label> getByPlaceholder — inputs without a label getByText — non-interactive content getByTestId — a deliberate data-testid , only when nothing semantic fits Here's the top of the priority list, live against Inkwell's home page: import { test , expect } from " @playwright/test " ; test ( " prefer role-based locators over CSS " , async ({ page }) => { await page . goto ( " / " ); await expect ( page . getByRole ( " button " , { name : " Global Feed " })). toBeVisible (); await expect ( page . getByRole ( " link " , { name : " Sign up " })). toBeVisible (); await expect ( page . getByRole ( " heading " , { name : " inkwell " })). toBeVisible (); }); getByRole("button", { name: "Global Feed" }) asserts two things at once — that an element with the button role exists and that its accessible name is "Global Feed". If a dev swaps the <div class="feed-btn"> for a real <button> , this locator keeps working; a CSS selector wouldn't. Strict mode is your friend Playwright locators are strict : if a locator matches more than one element, the action throws instead of silently picking the first. That catches ambiguous tests before they pick the

Make your screen recordings easy to follow Discussion | Link

Tech Companies Regret Firing Engineers for AI: The Quiet Rehiring Nobody's Talking About [2026] Klarna's CEO Sebastian Siemiatkowski stood on stage in 2024 and bragged that AI had replaced 700 customer service employees. The stock market loved it. LinkedIn influencers celebrated. And then, quietly, in 2025, Klarna started hiring humans again. That single reversal tells you everything about why tech companies regret firing engineers for AI. I've watched this pattern unfold across the industry, and a viral YouTube video by Pooja Dutt documenting these failures is now pulling over 10,000 views per day. The audience isn't just curious. They're vindicated. The tech industry laid off over 260,000 workers in 2023 alone, according to Layoffs.fyi , with many companies explicitly citing AI automation as justification. Now, in 2026, the bills are coming due. The companies that swung hardest at the "AI replaces engineers" thesis are the ones scrambling hardest to undo the damage. Why Did Companies Fire Engineers for AI in the First Place? The logic seemed airtight. AI can generate code faster than humans. AI can handle customer queries at scale. AI doesn't need benefits, PTO, or performance reviews. Executives saw a clean line from "AI generates output" to "we need fewer people," and they drew it with a Sharpie. I've been in enough executive planning meetings to know exactly how this plays out. Someone demos an AI tool that produces a working prototype in 20 minutes. The room gets excited. The CFO asks how many engineers they can cut. Nobody asks the harder question: what happens when that prototype needs to survive contact with production? The answer is that it breaks. Badly. Klarna is the poster child, but they're far from alone. Apple has spent two full years struggling with AI-driven improvements to Siri, despite being one of the most well-resourced engineering organizations on the planet. Even with virtually unlimited budget and talent, replacing deep engineering expertise

In Chapter 1 we argued that automation fails from a lack of structure , not a lack of tooling — and we met Inkwell , the dockerized app we test against. Now we install Playwright + TypeScript and write our first UI and API tests, deliberately simple. We'll also hit two real bugs along the way. I'm leaving them in on purpose — they're the exact problems the framework we build later is designed to prevent. Code for this chapter is tagged ch-02 in the repo: https://github.com/aktibaba/playwright-qa-course Before you start Make sure Inkwell is running (from Chapter 1): cd sut docker compose up -d --build --wait # web :3000, api :3001/api Install Playwright + TypeScript From the repo root: npm install -D @playwright/test typescript @types/node npx playwright install chromium That's it — Playwright bundles its own test runner, assertion library, and TypeScript support. No extra config to make .ts test files work. A minimal config — not a framework yet Two small files keep us honest from day one. First, never hard-code URLs in tests — put them in one place: // src/utils/env.ts export const env = { /** Inkwell SPA (nginx) — the UI base URL. */ webURL : process . env . WEB_URL ?? " http://localhost:3000 " , /** Inkwell API base, including the /api prefix. */ apiURL : process . env . API_URL ?? " http://localhost:3001/api " , } as const ; Then the Playwright config. We split tests into two projects — a fast api project and a Chromium ui project — because API tests need no browser and should run in milliseconds: // playwright.config.ts import { defineConfig , devices } from " @playwright/test " ; import { env } from " ./src/utils/env " ; export default defineConfig ({ testDir : " ./src/tests " , fullyParallel : true , reporter : " list " , use : { trace : " on-first-retry " , screenshot : " only-on-failure " }, projects : [ { name : " api " , testDir : " ./src/tests/api " , use : { baseURL : env . apiURL } }, { name : " ui " , testDir : " ./src/tests/ui " , use : { baseURL : e

When we build something complex—whether it’s a skyscraper, a gourmet meal, or a piece of software—we usually start with a plan. In software development, however, it’s easy to skip that step. We often jump straight into implementation, focusing on how to write the code instead of the intent behind it. Over time, this leads to rework, confusion, and systems that don't quite match our original goals. Spec-Driven Development (SDD) is an approach that shifts the focus back to the plan. Instead of starting with code, you start with a Specification : a clear, structured description of what the software should do. You then use an AI coding agent as a high-speed collaborator to help turn that specification into working code. 🔍 What is a “Spec”? A Specification (or “Spec”) is a written contract between your intention and the final product. It isn't a 50-page manual; it's a living document that defines: What the system should do. How it should behave in different scenarios. Which constraints and rules it must follow. From Prompts to Specifications There is a massive difference between a vague prompt and a structured spec. Loose prompts often lead to inconsistent results and "hallucinations," whereas clear specifications give the AI a much better target to hit. Bad Prompt: > “Build me a login system.” Good Spec: A good spec provides the clarity an AI (or a human) needs to succeed. You don’t need a 10-page document to benefit from specs; you need clarity, not length. 🛠️ Example Spec: Login Endpoint Overview Allow users to log in using email and password. Endpoint POST /api/login Request { "email" : "user@example.com" , "password" : "string" } Behavior Success: If email and password are correct → return a token and user info. Invalid Credentials: If credentials don't match → return INVALID_CREDENTIALS . Invalid Input: If fields are empty or the email format is wrong → return INVALID_INPUT . Rules Passwords must be stored hashed (e.g., bcrypt). Token expires in 24 hours. Security:

Learn how to build a real IoT weather station using the Arduino UNO R4 WiFi and BME280 sensor, sending live temperature, humidity, and pressure data to Arduino IoT Cloud — with full code, wiring diagrams, and dashboard. What We're Building In this project, you'll build a cloud-connected weather station that measures: Temperature (°C / °F) Humidity (%) Atmospheric Pressure (hPa) All three readings will be streamed live to the Arduino IoT Cloud , where you can monitor them from anywhere in the world via a browser or the free Arduino IoT Remote app on your phone. Components Required Component Qty Notes Arduino UNO R4 WiFi 1 Built-in ESP32-S3 WiFi module BME280 Sensor Module 1 Measures temp + humidity + pressure via I²C Breadboard 1 Full or half size Jumper Wires (M-M) 4 For I²C connections USB-A to USB-C Cable 1 For power & programming Why BME280 over DHT22? The BME280 gives you three measurements (including barometric pressure) over a single I²C bus using just 2 wires, making it more capable and cleaner to wire. The DHT22 only gives temperature and humidity. Wiring the BME280 to Arduino UNO R4 WiFi The BME280 uses the I²C protocol , so it only needs 4 wires: BME280 Pin → Arduino UNO R4 WiFi Pin ────────────────────────────────────── VCC → 3.3V GND → GND SDA → A4 (I²C Data) SCL → A5 (I²C Clock) Important: The BME280 runs on 3.3V , not 5V. Connecting it to the 5V pin can damage the sensor permanently. Here's the schematic overview: ┌────────────────────────────┐ │ Arduino UNO R4 WiFi │ │ │ │ 3.3V ──────────────► VCC │ │ GND ──────────────► GND │ ← BME280 │ A4 ──────────────► SDA │ │ A5 ──────────────► SCL │ └────────────────────────────┘ ☁️ Step 1 — Set Up Arduino IoT Cloud Before writing any code, you need to configure the Arduino IoT Cloud . It's free for up to 2 devices. 1.1 Create a Free Account Go to cloud.arduino.cc and sign up or log in. 1.2 Create a New "Thing" Click Things in the left sidebar Click + Create Thing Name it WeatherStation 1.3 Add Your Device Click

"# The AI Cost Crisis: How Startups Can Survive the Tokenpocalypse\n\n## Introduction\n\nThe artificial intelligence boom has brought unprecedented innovation, but it has also ushered in a era of spiraling costs. Training state-of-the-art models now requires millions of dollars in compute resources, while simultaneously, the cryptocurrency token market shows signs of a potential collapse—a \"Tokenpocalypse.\" For AI startups, this dual crisis presents an existential threat: how to sustain innovation when both traditional funding avenues and speculative token economies are under pressure? This post explores practical strategies for AI startups to navigate this landscape, focusing on cost optimization, alternative funding, and strategic pivots that can turn crisis into opportunity.\n\n## Understanding the Cost Explosion\n\n### The Compute Crunch\n\nModern AI models, particularly large language models (LLMs) and multimodal systems, demand vast computational resources. Training a single cutting-edge model can consume exaflops of processing power, translating to cloud bills that easily exceed $10 million for a single training run. For startups without deep-pocketed backers, these costs are prohibitive.\n\n### The Token Market Volatility\n\nParallel to the AI boom, the cryptocurrency space experienced explosive growth through token launches—initial coin offerings (ICOs), decentralized finance (DeFi) tokens, and utility tokens for AI-driven projects. However, regulatory crackdowns, market saturation, and declining investor sentiment have led to a sharp downturn. Many tokens have lost significant value, and launching new tokens has become increasingly difficult, removing a once-viable funding path for AI startups.\n\n## Strategies for Survival\n\n### 1. Embrace Model Efficiency\n\nInstead of chasing ever-larger models, startups can focus on efficiency techniques that deliver comparable performance at a fraction of the cost:\n\n- Model Distillation : Train smaller \"student\

Last weekend I started building SiteGrade — a free instant SEO audit tool that gives any website an A–F letter grade and the top fixes ranked by impact. I just launched it. This post is the build log: the architecture decisions that worked, the ones that didn't, and the gotchas you'd save time knowing about. If you're considering building a similar audit/scanner tool, or just want to see what a 2026 Next.js + Supabase + Stripe stack looks like in practice, this should be useful. Why I built it I got tired of family and friends asking me "how's the SEO on my website?" and not having a good answer to send back. Every existing tool I tried either cost $99+/month (Ahrefs, Semrush) or threw 200 metrics at people who just wanted to know if their site was OK. The wedge I built around: one letter grade, three top fixes, plain English, no signup. The paid tier ($29/mo) re-audits weekly and emails the report so non-technical users can track improvements as their developer makes them. The stack Nothing exotic. Everything is the obvious choice for a 2026 indie SaaS, which is the whole point — boring stack means I spent zero time fighting infrastructure and 100% of my time on the actual audit logic. Next.js 14 App Router — frontend + API routes in one repo Supabase — Postgres + auth + RLS + file storage, EU-hosted for GDPR Stripe — subscriptions + Billing Portal + webhooks Resend — transactional email (audit reports, weekly reports, Supabase auth via Custom SMTP) Vercel — hosting + cron jobs for the weekly re-audit cheerio — HTML parsing for the audit checks Google PageSpeed Insights API — Core Web Vitals + mobile performance Total monthly infrastructure cost at zero traffic: ~$0 (everyone on free tiers). At 100 paying customers it'd still be under $30/mo. The audit logic — what 15 checks look like in code Each audit runs 15 checks and produces a score from 0-100, then maps that to a letter grade (A: 90+, B: 75+, C: 60+, D: 45+, F: below 45). The checks are structured as pure fu

Meme Monday! Today's cover image comes from the last thread . DEV is an inclusive space! Humor in poor taste will be downvoted by mods.

Welcome to the first chapter of a hands-on course where we build a production-grade Playwright + TypeScript automation framework — covering both API and UI testing — against a real, dockerized web app you run on your own machine. This isn't a "here are 5 Playwright tips" post. By the end of the series you'll have a framework with the same shape a real QA team ships: layered, parallel-safe, authenticating once and reusing the session, seeding data through the API and verifying it in the UI, and running sharded in CI. We build it one chapter at a time, and every line of code is in a public repo you can clone and run. Who this is for You can read basic JavaScript (variables, functions, async/await ). That's it. No Playwright or TypeScript experience required — we introduce both from zero. You've maybe written a few UI tests before and felt them turn into a tangle. This course is about the structure that prevents that. How the course works Each chapter is one post in this series, in order. Read them top to bottom. There's a companion GitHub repo — the single source of truth for all code: 👉 https://github.com/aktibaba/playwright-qa-course The repo carries one git tag per chapter ( ch-01 , ch-02 , …) so you can check out the exact state of the code at any point and compare it to what you have. Every chapter ends with what changed, so you can either build along or just read the diff. Get the code and run the app We don't test toy pages. The course runs against Inkwell — a small but real React + Express + PostgreSQL blogging app (articles, comments, tags, follow/favorite, JWT auth). It lives in the same repo under sut/ ("system under test") and ships as a one-command Docker stack with deterministic reset / seed endpoints, so your tests never race startup or fight flaky data. You'll need Node.js 18+ and Docker . # 1. Clone the course repo git clone https://github.com/aktibaba/playwright-qa-course.git cd playwright-qa-course # 2. Start the app (db + API + web), wait until eve

Used 5 different programming language for this course. GitHub repository link: https://github.com/mohammadijoo/Control_and_Robotics_Tutorials submitted by /u/abolfazl1363 [link] [留言]