When I started learning Data Science, I expected to spend my first week writing Python code, exploring machine learning models, and working with advanced tools. Instead, I spent most of my time in Excel. At first, it felt underwhelming—just rows, columns, and simple spreadsheets. But within a few days, I realized something important: Excel is not a basic tool at all. It is one of the most widely used tools in data analysis, business decision-making, and reporting. 📊 Real-World Uses of Excel Excel is widely used across industries for handling and analyzing data. Some of the most common uses include: Business Analysis - Tracking sales and identifying trend Accounting and Budgeting - Managing Expenses, Profits and Financial reports Marketing Analysis - Measuring campaigns performance and customer behavior Data Entry and Management - organizing large datasets efficiently Businesses rely on Excel because it helps turn raw data into meaningful insights for decision making. 🛠️ Key Excel Features I Learned In my first week, I explored several important Excel Features that help with data organization and analysis: Excel Interface Overview - I first explored how Excel is organized, including Ribbon, Worksheets, Cell, Row, Columns, and formula bar. this helped me understand how to navigate the tool before working with data Data Sorting - Organizing data by numbers, Text and Dates Filtering - Showing only relevant data based on condition Data Validation - Ensuring accurate and consistent data entry Freeze Panes - Keeping header Visible while scrolling through large datasets. These features make working with data much easier, faster and more structured. 🧮 Basic Excel Functions I learned I was also introduced to some basic Excel functions used in Data Analysis. Aggregate Functions - SUM - Add all values in a range - AVERAGE - Calculate the mean of a dataset - COUNT - Counts numerical entries in a dataset Conditional Functions - SUMIF () and SUMIFS()** - Add values that meets one

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

I grew tired of mocks lying to me and the extra complexity they bring. This led me back to a classic design pattern: the Command. The idea is simple: separate an action from its execution. I wanted a functional take on this, though: no classes, no mutation, just pure functions returning plain data. Most importantly, I wanted it without the academic vocabulary of category theory. The result is a tiny library a developer could master in a single afternoon. It removes the need for mocking libraries and comes with additional benefits such as time-travel debugging. submitted by /u/aijan1 [link] [留言]

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

Optimizing Laravel Performance: Conquering the N+1 Query Problem with Eager Loading As full-stack developers, building performant applications is a continuous challenge. One of the most insidious yet common performance bottlenecks encountered in Laravel applications is the "N+1 query problem." This issue can significantly degrade response times, inflate database load, and ultimately lead to a poor user experience. Fortunately, Laravel provides a powerful and elegant solution: eager loading using the with() method. This tutorial will walk you through understanding the N+1 problem and effectively using eager loading to keep your applications fast and efficient. Understanding the N+1 Query Problem Imagine a scenario where you need to display a list of blog posts, and for each post, you also want to show the name of its author. In a typical Laravel application, your Post model would likely have a belongsTo relationship with a User model. Let's look at a common, yet inefficient, way this might be implemented: 1. The Inefficient N+1 Approach Consider a controller fetching all posts and a view attempting to display the author's name: app/Http/Controllers/PostController.php (N+1 Example): namespace App\Http\Controllers ; use App\Models\Post ; use Illuminate\Http\Request ; class PostController extends Controller { public function index () { $posts = Post :: all (); // Fetches all posts return view ( 'posts.index' , compact ( 'posts' )); } } resources/views/posts/index.blade.php (N+1 Example): <h1>Blog Posts</h1> @foreach ($posts as $post) <div class="post-item"> <h2>{{ $post->title }}</h2> <p>Author: {{ $post->user->name }}</p> <!-- Accessing related user inside loop --> <p>{{ Str::limit($post->body, 150) }}</p> </div> @endforeach Why this is N+1: 1 Query: SELECT * FROM posts; – This initial query fetches all your posts. N Queries: For each $post in the loop, when you access $post->user->name , Laravel lazy-loads the associated User model. If you have 10 posts, this will exe

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

Rails GuardDog: Advanced Security Scanner for Rails Introduction Today I'm excited to announce Rails GuardDog v0.1.0 — an open-source security scanner for Rails that goes beyond traditional tools like Brakeman. While Brakeman is excellent for catching basic Rails vulnerabilities, Rails GuardDog focuses on newer vulnerability classes that most tools miss: AI/LLM prompt injection, DoS/ReDoS patterns, supply chain attacks, and more. The Problem Modern Rails applications face new security challenges: AI/LLM Integration - How do you prevent prompt injection when integrating with ChatGPT, Claude, or Anthropic? ReDoS Attacks - Catastrophic backtracking in regex can bring down your app Supply Chain Attacks - Typosquatted gems that look like popular libraries IDOR Gaps - Objects accessible without proper authorization checks Advanced Secrets - Hardcoded API keys that Brakeman misses Rails GuardDog detects all of these. What is Rails GuardDog? Rails GuardDog is a lightweight gem that adds comprehensive security scanning directly to your Rails applications. 12 Security Checkers SQL Injection - String interpolation in queries XSS - Unescaped output in views CSRF - Disabled protection verification Mass Assignment - permit! vulnerabilities (fixes Brakeman #1942, #1918) Open Redirect - User input in redirects Hardcoded Secrets - API keys, tokens, passwords (always-on, fixes #1989) DoS/ReDoS - Unbounded queries, dangerous regex patterns IDOR - Object access without authorization AI/LLM Prompt Injection - User input flowing to LLMs Rate Limiting - Missing rack-attack configuration Supply Chain - Typosquatted gems using Levenshtein distance GraphQL - Missing field-level authorization Features 📊 Multiple report formats : Console, HTML, JSON 🔍 AST-based analysis : Uses parser gem for deep code understanding ⚡ Async support : Built-in Sidekiq integration 📈 Zero dependencies : Only requires parser and ast gems 🚀 Production-ready : Tested and battle-ready 📝 CWE/OWASP mappings : Every find

https://preview.redd.it/xse99kmwom5h1.png?width=3072&format=png&auto=webp&s=af121137b68e58c524be2f99f3a1b2ab6dacc22e After years of frustration with cloud-based video editors that charge by the minute and require massive server farms, I built OpenVideo - an AI-native video editing platform that does everything in your browser. **What makes it different:** 🚀 **Browser-Based 4K Rendering** - Hardware-accelerated using WebCodecs + PixiJS. Export 4K video without any server-side processing. 🤖 **AI-Native from Day One** - Semantic search across your video library (find clips by content, not filenames), automatic captions with AI transcription, and an AI Director that helps organize your footage. **Tech Stack:** - Frontend: Next.js 15 - Backend: NestJS + Fastify - DB: PostgreSQL + Drizzle ORM - Rendering: PixiJS + WebCodecs - AI: Gemini API + pgvector It's open source and I'd love feedback from the community. Check it out: https://github.com/openvideodev/openvideo What features would you want to see in a browser-based video editor? submitted by /u/snapmotion [link] [留言]

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/lelanthran [link] [留言]

A lot has changed since I posted this 6 months ago. serverlist.dev is a comparison tool for VPS, Dedicated and GPU Servers. I fetch data multiple times a day and present it fairly with no prioritization or hidden advertisement. You decide which columns to sort, which values to filter and which product matters most to you. When I last posted this on r/webdev I got five main pieces of feedback: We would like a "Compact view" option --> Done Some CTA and other strings seem pushy ("Claim Deal") --> Improved The site is lacking any additional value beside being a data catalogue --> read more below The filter need debounce and the whole table has very bad performance --> I significantly imrpoved the table performance by using tanstack virtualization. Sorting and filtering anything is now instant! We would like cPanel, Plesk, Managed properties --> still working on that. I am also thinking of "support IaC" what other information might be relevant for you? Since the last time I also worked on many new features: Hourly Pricing where applicable I now show the hourly price of a product. You can also filter for "Hourly price available" In-Table Comparison (desktop version only) when you select one product with the checkbox on the left, all other product's values are either green or red depending on their relative performance. Helping you to quickly identify if there might be a better deal that you overlooked. Product specific page clicking the compare button on a product or clicking its name now brigns you to a more detailed page showing the historical price change of that product and also two categories "What you get for a similar price" and "Similar servers by specs" where differences are also marked in green or red colour. Price Index alongside the product specific historical data I am also collecting averages for the entire industry so you can compare all providers at once. Right now I have "RAM per 1€", "CPU Cores per 1€" and the average price for generic SKU tiers like 4G

maybe this should be asked in the Fc26 game subreddit but not sure. Anyway I just saw a video of someone predicting the winner of the world cup using the simulate match feature in the game but he only did it once. Would running this feature 100-1000 times give a significant result ? or is that feature only based on luck ? submitted by /u/Stillane [link] [留言]

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

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