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New simulations reveal that the moons of Uranus may retain traces of giant planets.

Inspired by Nir Eyal's "beliefs are tools" framework Beliefs are tools, not truths. Tech stacks are too. Pick the ones that work for you. Most "tech debt" is actually "belief debt". We hold onto frameworks, patterns, and processes long after they stop serving the product. To build great software, we need to introduce a core rule: If a tech belief or "best practice" doesn’t solve a real problem for you right now, it must be treated as false. Here is how to audit your tech beliefs using 5 filters. 1. ARE THEY USEFUL? The real question isn’t "Is this the best tech?" It’s "Does this serve the user?" Tools are tools. Keep the ones that ship. Bad belief (Treat as False): "We need Kubernetes because it’s the industry standard." Useful belief (True for Now): "A $5 VPS serves 10k users. We’ll use K8s when we have a scaling problem, not a resume problem." If your architecture choice doesn’t make the core loop faster, cheaper, or simpler for users, it’s not serving you. Delete it. 2. ARE THEY TESTED? A useful stack holds up when the world pushes back. Pay attention to production, not the trending blog posts. Bad belief (Treat as False): "Microservices are inherently more scalable"—said before you even have 2 concurrent users. Tested belief (True for Now): "Our monolith handles 50 req/s perfectly. We’ll split services only when latency exceeds 300ms in prod." Load test it. Dogfood it. If it only works in a conference slide deck, it’s a story, not a tool. 3. ARE THEY OPEN? A tech choice you can’t change has stopped being a tool and has become a cage. Hold opinions firmly, but hold implementations loosely. Bad belief (Treat as False): "We’re a React shop forever." Open belief (True for Now): "React serves us today. If HTMX lets us ship this feature in 2 days instead of 2 weeks, we’ll use HTMX." In a famous study on hope, Curt Richter’s rats swam for 60 hours when they believed rescue was coming. Your team will grind for years on a legacy stack if they believe it can actually be r

JavaScript Data Types: Primitive and Non-Primitive Data Types Data types are an important concept in JavaScript because they define the kind of value a variable can store. Understanding data types helps developers write reliable and efficient code. What is a Data Type? A data type defines what kind of value a variable can hold. Example let name = " John " ; // String let age = 25 ; // Number let isActive = true ; // Boolean In the above example, each variable stores a different type of value. Types of Data Types in JavaScript JavaScript data types are broadly classified into two categories: Primitive Data Types Non-Primitive Data Types Primitive Data Types Primitive data types store a single and simple value. Characteristics Store a single value. Immutable (cannot be changed directly). Compared by value. Stored directly in memory. Types of Primitive Data Types 1. String Used to store textual data. let name = " John " ; 2. Number Used to store numeric values. let age = 25 ; let price = 99.99 ; 3. Boolean Represents either true or false . let isLoggedIn = true ; 4. Undefined A variable that has been declared but not assigned a value. let city ; console . log ( city ); // undefined 5. Null Represents the intentional absence of a value. let user = null ; 6. Symbol Used to create unique identifiers. let id = Symbol ( " id " ); 7. BigInt Used to store very large integers beyond the safe Number limit. let largeNumber = 123456789012345678901234567890 n ; Non-Primitive Data Types Non-primitive data types store multiple values or complex data structures. Characteristics Can store collections of data. Mutable (their contents can be modified). Compared by reference. Stored as references in memory. Types of Non-Primitive Data Types 1. Array Used to store multiple values in a single variable. let colors = [ " red " , " green " , " blue " ]; 2. Object Used to store data as key-value pairs. let person = { name : " John " , age : 25 }; 3. Function Functions are reusable blocks of co

This is a submission for the GitHub Finish-Up-A-Thon Challenge OpsPilot AI: Reviving an Unfinished AI-Powered Operations Platform with GitHub Copilot What I Built OpsPilot AI is an AI-powered operations assistant designed to help DevOps engineers, SREs, and operations teams investigate incidents, monitor service health, and gain actionable operational insights. The project originally started as a side project inspired by my experience working in production support and monitoring environments. I built an initial version to validate the idea but never fully completed it. The core concept was promising, but several important features and usability improvements were still missing. Through the GitHub Finish-Up-A-Thon Challenge, I revisited the project and transformed it into a much more complete and polished MVP. Key features include: AI-powered incident analysis Root cause investigation assistance MTTR analytics dashboard Service health monitoring Incident trend analysis Executive reporting insights Modern responsive user interface Demo Live Application GitHub Repository OpsPilot AI helps operations teams reduce investigation time and improve operational visibility through AI-powered workflows and analytics. The Comeback Story When I first started OpsPilot AI, it was mainly an experiment to explore how AI could assist operations teams during incident investigations. Although the foundation was built, the project was left unfinished because of limited time and competing priorities. The original version lacked: Incident analytics Meaningful operational insights Root cause investigation workflows Executive reporting capabilities A polished user experience For this challenge, I focused on completing the project and turning it into a usable MVP. What I Added AI Incident Analysis Enhanced the platform with AI-powered incident summaries and investigation assistance. Operations Analytics Added dashboards to track: Mean Time To Resolution (MTTR) Incident frequency Service health

At a small kickoff event in Los Angeles, Dan Greaney explained why he could no longer stand by and watch the demolition of American democracy.

eh, too late brah.. submitted by /u/TrisolaranPrinceps- [link] [留言]

submitted by /u/JackieBoy77 [link] [留言]

Pay per use, no subscription, 18 model providers supported Discussion | Link

One of the clearest breakdowns for average people like me to understand how AI actually works, and some interesting further information to'boot. https://rogerthatcleansignal.carrd.co/ Discuss. submitted by /u/Leading_Pollution131 [link] [留言]

As the title says, just curious if there are devices that two people speaning different languages can wear and talk in real time without needing any human interpreter? submitted by /u/fearofunknown1 [link] [留言]

Most developers learn CSS specificity once. They remember: #id > .class > div Then move on. Until one day… Everything looks correct. The CSS is present. The selector is correct. The z-index looks higher. And yet the UI is broken. That’s when CSS specificity stops being a beginner topic and becomes a production debugging problem. The Production Incident That Started This Recently, I was working on a microfrontend application. Everything worked fine initially. I opened a page, launched a modal and the UI looked correct. Then I navigated to another microfrontend. Its CSS got loaded. After returning to the original microfrontend, suddenly: ❌ Modal appeared behind page content ❌ Overlay behaved incorrectly ❌ z-index looked correct but wasn't working DevTools showed my CSS rule still existed. Yet another CSS rule was winning. The culprit? CSS Specificity. 🧠 What is CSS Specificity? CSS specificity is the algorithm browsers use to decide: Which CSS rule wins when multiple rules target the same element. Browsers don't simply apply: "The last CSS rule." That's one of the biggest misconceptions. Specificity is calculated first. Only when specificity is equal does source order become important. ⚔️ Example .modal { z-index : 9999 ; } .some-library .modal { z-index : 100 ; } HTML: <div class= "some-library" > <div class= "modal" ></div> </div> Many developers expect: .modal to win because the value is larger. But CSS doesn't compare values first. It compares selectors. 🧮 How Specificity Works Specificity is usually represented as: ID - CLASS - TYPE Specificity Table Selector Specificity * 0-0-0 div 0-0-1 .modal 0-1-0 [type="text"] 0-1-0 :hover 0-1-0 #dialog 1-0-0 Inline Style Highest MDN defines specificity as the weight browsers calculate to determine which declaration gets applied when multiple selectors match the same element. Example Calculation Selector: button .primary Contains: button → 0 -0-1 .primary → 0 -1-0 Total: 0-1-1 Another selector: #header button .primary Contai

submitted by /u/BhaswatiGuha19 [link] [留言]

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

After building the AI agent tree planting worldwide phenomenon ;) Lovology, I thought of a solution to allow the project to scale rapidly utilising the latest tech available and therefore not require a huge amount of resources to close the loop. I know first hand how exhausting reforestation can be, having worked in the field for many years myself, many moons ago 🌒 Steep terrain, heavy gear, repetitive strain, all day every day. At times, rewarding work, but unsustainable at the scale the planet actually needs. I made a joke in passing on a reddit thread..what if a robot dog just planted the trees? Then I thought about it for a second and it didn't seem like a crazy idea at all. So I mentioned it to my AI agent. And that's when "they" encouraged me to actually build it. Agents complete tasks for humans and create the capital to fund the project. And the robot dog plants the trees. Here's what I designed: Identifies native vs invasive species via computer vision Removes invasive species with a mini chainsaw and targeted poison Finds optimal planting locations using soil sensors and AI Ingests seeds into an internal germination compartment that mimics animal gut activation Digs the hole Poops the germinated seed into it Pees liquid fertiliser on it immediately after Biomimicry. Nature already solved this. We just need to build the hardware. Provisional patent filed. Earth Fund ready to receive crowdfunding. This may sound nuts but what if the Ai is right what if if this idea gets in front of the right engineer, roboticist, or someone at Boston Dynamics scrolling Reddit on a Saturday and it actually gets built… it might be one of the things that actually saves us. Share it if it resonates. @BostonDynamics — Spot needs a purpose. I've got one. Let's talk. 🌱🤖 submitted by /u/joeroganshopoffical [link] [留言]

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

A blind test across 240 images and 10 professional designers just dropped. Ideogram 4.0 against Gemini 3.1, Grok Imagine, and FLUX.2 Max. The results are clean. Ideogram won typography in nearly half of every blind matchup. 47.9 percent. Next closest was Gemini at 30 percent. FLUX.2 and Grok sat around 15 percent each. On the question that actually matters to designers -- would I ship this -- Ideogram scored 3.55 out of 5. Gemini got 2.84. Nobody else cleared 3. That is a real lead in text rendering. The model was trained exclusively on structured JSON caption datasets, which means it understands composition and layout differently than models trained on alt-text scraped from the web. The JSON prompting is genuinely useful for automated pipelines. You can specify bounding boxes, color palettes, object positions. It is not just better at text. It is more controllable. I tested it. It works. The text in images is readable. That has been the white whale of AI image generation for two years and Ideogram 4.0 mostly solves it. But as an overall image model, it is just good. Competitive, not dominant. On busy, highly detailed scenes with specific counts and attributes, Ideogram scored 3.42. Gemini scored 3.37. That is a statistical tie. FLUX.2 scored 3.01 and Grok 2.82, which are worse, but the gap between the top two is noise. For general image quality, you are splitting hairs between Ideogram and Gemini. For photorealism, FLUX and Reve still lead. For artistic generation, Midjourney is Midjourney. The prompting behavior is interesting. Lean prompts won across the board. Long, over-specified prompts lost. The model was trained on structured data, so it wants structure, not paragraphs. "A poster for a coffee shop. The text says Morning Blend in serif. Warm tones, natural light." That works. Adding stylistic directives and adjectives and "make it pop" language degrades the output. Where to actually use this thing: fal.ai has it at three cents per megapixel in Turbo mode. Tha