Two weeks ago I shared a collection of 6 financial calculators. The response was incredible, so I kept building. Today, fi-calc.com has 25 completely free calculators covering every major personal finance need: 🏠 Housing • Mortgage Calculator (full PITI + amortization schedule + pie chart) • Rent vs Buy Calculator • House Affordability Calculator • Refinance Calculator 💰 Investing & Retirement • Compound Interest Calculator • Investment Return Calculator • Future Value & Present Value Calculators • ROI Calculator • Retirement Savings Calculator • 401(k) Calculator • FIRE Calculator (Financial Independence) 💳 Debt & Loans • Loan Comparison Calculator • Auto Loan Calculator • Student Loan Calculator • Credit Card Payoff Calculator • Debt Payoff Calculator (Avalanche method) 📊 Everyday Finance • Budget Planner (50/30/20 rule) • Savings Goal Calculator • Inflation Calculator • Salary & Take-Home Pay Calculator • Net Worth Calculator • Currency Converter (15+ currencies) • CD Calculator • Sales Tax Calculator ✨ Tech Stack • Pure vanilla HTML/CSS/JS — no frameworks • Chart.js for animated interactive charts • Responsive design (mobile-friendly) • All calculations run client-side in your browser • No data collection, no accounts, no cookies 🔗 Give it a try: fi-calc.com The entire site is free and open. I built it because I was tired of calculator sites with paywalls, signup walls, and bloated ad experiences. Would love feedback from the community! What other calculators should I build next?

A story about knowledge extraction, Kafka consumer rebalance, and what happens when a company...

A WIRED timeline shows how dozens of governments, companies, and other organizations across Europe are moving, or planning to shift, away from US Big Tech.

Im an undergraduate studying CS at a state school in the US. I’m interested in researching a specific style of self supervised learning (JEPA) and want to eventually go to grad school to study further. I have experience working in a lab similar to this topic, and I’ve become fairly comfortable with the literature and have a basic understanding of what its going on, but right now km only doing applied research in a specific domain (physics). I hope to eventually go to grad school to study this. But right now my opportunities are kinda limited as my school’s CS department is pretty mid. I was wondering if y’all have any advice on how to approach things? I know i can perform research independently but its not ideal due to: 1. Limited compute, less resources compared to a proper lab 2. Lack of a supervisor/guidance on the nuances of the field My current lab would be supportive if i do try to do things, but pure ml research is not really their main thing. I’ve heard people do REUs or cold email profs. But Im not sure if i could find something that specifix in an reu (also am international). And the labs i have seen working in this are either private or quite prestigious so im not sure how far cold emailing would take me. Sorry for the long post. Tldr; want to do pure ml research but theres no existing lab/professor at my current school who does something similar, wondering if any other pathways exist Any advice would be appreciated thanks submitted by /u/QuickStar07 [link] [留言]

If ICML conference paper is rejected and no one opts-in or opts-out to keep the reviews visible, will the reviews be visible to everyone? There was clear instruction that only papers with at-least 1 opt-in AND zero opt-out options will be visible. None of the authors selected any option, But it in my openreview profile, it shows visible to everyone. please clarify. (Just above paper decision, there is a block with "filter by type", "filter by author" etc options. in that block there is eye symbol and everyone is written.) submitted by /u/Curious-Monitor497 [link] [留言]

The way developers write software has genuinely changed. Not incrementally — fundamentally. A year...

https://xcancel.com/rasbt/status/2063649136323252397 https://arxiv.org/abs/2602.11988

Open-source video emulation of analog TV and VHS artifacts Discussion | Link

I keep seeing more companies giving AI agents access to real stuff like email, databases, internal tools, customer data, etc. And what’s weird is how normal it’s starting to feel now. Like not long ago everyone was worried about chatbots just giving wrong answers. Now we’re basically like yeah sure go ahead and do things for us. I don’t know that jump feels kind of big when you actually think about it. Maybe it all works out fine. Or maybe we’re just moving fast without fully realizing what we’re doing. I’m honestly surprised there hasn’t already been some big headline like an AI agent doing something really wrong. It feels like we’re kind of close to one of those moments where everything suddenly changes overnight. Anyone else feel like we’re closer to something like that than people are admitting? submitted by /u/Comfortable_Box_4527 [link] [留言]

personalized AI food journal + nutrition coach Discussion | Link

Relive vintage operating systems right on your desktop Discussion | Link

I built a flashcard app for interview prep and wanted to share some of the more interesting technical problems I ran into. The app has 1500+ questions across DSA and System Design, and the core challenge was: how do you order cards intelligently without it feeling robotic? Problem 1: Slot Assignment for Spaced Repetition Standard SR (like Anki) just shows the most overdue card next. That works for vocabulary but feels terrible for algorithms because you get 3 Hard questions in a row and want to quit. My approach: generate a target difficulty pattern (Easy, Medium, Easy, Medium, Hard, repeat) based on a 40/40/20 distribution, then assign due cards to matching-difficulty slots. Most-overdue cards get placed first within their tier. Unseen cards fill remaining slots. This means a Hard card that's overdue still lands in a Hard slot, not position 1. You get difficulty variety while still seeing overdue cards at the right time. fun assignSlots(pool: List<Question>, dueCards: List<ProgressEntity>): List<Question> { val pattern = generatePattern(size = pool.size, distribution = "40/40/20") val dueByDifficulty = dueCards.groupBy { it.difficulty } val result = Array<Question?>(pattern.size) { null } // Place due cards in matching slots, most overdue first for ((difficulty, cards) in dueByDifficulty) { val sorted = cards.sortedBy { it.nextReviewDate } val availableSlots = pattern.indices.filter { pattern[it] == difficulty && result[it] == null } sorted.zip(availableSlots).forEach { (card, slot) -> result[slot] = findQuestion(card, pool) } } // Fill remaining with unseen // ... } Problem 2: Re-ranking after every swipe without jank After each swipe, the deck needs to re-rank. But the top visible card (position 0) is already animating into view, so you can't move it. Solution: lock position 0, re-rank positions 1+, then check for constraint violations across the boundary (e.g., if locked card is Hard and new position 1 is also Hard, swap position 1 with the first non-Hard card d

With more software engineers entering into data science and AI, I feel it's equally important for a person with data and AI background to dive into software development to survive, thrive in industry. I Know it's a very broad question, so suggestions with broad subjects, topics are welcome , like I often wonder how DSA is relevant. I totally understand the needs of the skills are deeply coupled with domain, industry and specific problems but unfortunately the industry doesn't understand this, it judges you, rewards you based on what you already know or pretend rather than your ability to learn or adapt. submitted by /u/Dapper_Chance_2484 [link] [留言]

I had 4 Chrome windows with ~80 tabs each, mostly duplicates of the same Stack Overflow page. Tried OneTab (saves to a list - not what I wanted) and Workona (cloud sync, overkill). So I wrote ~50 lines of vanilla JS. Click the toolbar icon → every duplicate tab across every window is removed (matched by URL) → survivors merge into one window → remaining tabs auto-group by hostname (collapsed). Two permissions: tabs, tabGroups. No background activity, no server, no analytics. Whole source is in the README so you can audit it before installing. Chrome Web Store: https://chromewebstore.google.com/detail/tab-vacuum/apdjhdjcejehjiomcolfgfgjhaedoieb GitHub: https://github.com/mayhsundar/tab-vacuum Please give your comments submitted by /u/mayhsundar [link] [留言]

The Problem Wasn't Writing It was starting. Sometimes I wanted: A social media caption A motivational quote A writing prompt A meaningful message But my mind would go completely blank. Not because I had nothing to say. Because: Coming up with the right words at the right moment is surprisingly difficult. We've All Done This Open a new tab. Search: "Motivational quotes" "Success quotes" "Life quotes" "Funny quotes" Scroll for 10 minutes. Copy one. Close the tab. Why I Built This Tool So I built something simple: 👉 https://allinonetools.net/quote-generator-tool/ A tool that instantly generates quotes across different categories. Whether you need: Motivation Success Life Leadership Creativity Social media inspiration You can generate quotes in seconds. No signup. No setup. Just: Click → Generate → Use What I Realized People don't always look for quotes because they need content. Often they're looking for: A different perspective. A good quote can do something interesting. It can say in one sentence what takes us paragraphs to explain. The Surprising Part The most popular quotes are rarely complicated. They're simple. Short. Easy to remember. Yet somehow they stick with us for years. Why Quotes Still Matter In a world full of endless content: Attention is limited Time is limited Patience is limited A strong quote delivers an idea instantly. That's powerful. The Problem With Searching Manually Most quote websites feel: Cluttered Slow Full of ads Hard to browse And sometimes you spend more time searching than actually reading. What I Focused On I wanted the experience to feel: Fast Clean Inspiring Fun to explore Because finding inspiration shouldn't require effort. What Surprised Me After building it: Some people used it for: Social posts Presentations Daily motivation Writing inspiration But one thing surprised me most. People kept generating quote after quote. Not because they needed one. Because they enjoyed discovering them. The Real Insight Sometimes tools aren't abo

In Modern Time applications are no longer simple monolithic systems. Today organizations run: Microservices Kubernetes Containers Serverless Functions Multi-Cloud Platforms Distributed Systems As infrastructure becomes more distributed, troubleshooting becomes significantly harder. A single user request may travel through: Frontend ↓ API Gateway ↓ Microservice A ↓ Microservice B ↓ Database When something breaks, the biggest challenge becomes: "What exactly happened?" This is where Observability becomes critical. 🔗 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 Observability? Observability is the ability to understand the internal state of a system by analyzing the data it produces. In simple words: Can we understand what is happening inside our systems? Observability helps engineers answer: Why is the application slow? Which service is failing? Which request caused the issue? What changed recently? Where is latency occurring? Without observability: Problem Exists ↓ Guessing Begins With observability: Problem Exists ↓ Evidence Available ↓ Faster Resolution Why Observability Matters Modern cloud-native systems generate enormous amounts of data. Example: 100 Microservices ↓ Millions of Requests ↓ Thousands of Containers Traditional monitoring alone is no longer sufficient. Organizations need: Visibility Insights Correlation Root Cause Analysis Observability provides all of them. Monitoring vs Observability Many people confuse monitoring and observability. Monitoring asks: What is wrong? Observability asks: Why is it wrong? Example: Monitoring: CPU Usage = 95% Observability: Which service? Which request? Which dependency? Which deployment caused it? Observability provides context. The Three Pillars of Observability Modern observability is built on three primary pillars. Metrics Logs Traces Or: Monitoring Logging Tracing Together they provide a