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

Intro AI Avatar is a completely free app that lets your VRoid (VRM) 3D avatar animate in...

All tests run on an 8-year-old MacBook Air. All results from shipping 7 Mac apps as a solo developer. No sponsored opinion. You plug in your Android device. A few seconds later, you unplug the cable. The connection stays alive — wirelessly, automatically, without touching a single setting. That's Seamless Link. The Problem ADB over USB is reliable. ADB over Wi-Fi is convenient. But switching between them manually is friction: Plug in USB Run adb tcpip 5555 Find the device IP Run adb connect <IP>:5555 Unplug cable Every. Single. Time. If you're working with multiple Android devices, or doing this across multiple sessions per day, it adds up fast. What Seamless Link Does The moment you plug in a USB cable, Seamless Link runs that entire flow automatically in the background: Detects the USB connection Runs adb tcpip 5555 Grabs the device IP Establishes a Wi-Fi ADB connection By the time you've sat back down, the device is already connected wirelessly. Pull the cable out — everything keeps working. No manual steps. No IP hunting. No re-running commands every session. Working with Multiple Devices This gets more useful the more devices you have. Plug in Device A → Seamless Link connects it wirelessly. Plug in Device B → same thing, simultaneously. Each device goes through the full handover flow independently, in parallel. The more devices on your desk, the more time this saves. Android 16 Compatibility Android 16 changed how wireless debugging ports are assigned — random ports instead of the fixed 5555. Seamless Link handles this automatically. You don't need to know which port the device is using. If you're on an older Android version, it works the same way it always has. Why This Matters for Daily Workflows If you're an Android developer on Mac, you probably already have a USB cable on your desk. Seamless Link just makes that cable optional after the first few seconds. It's one of those features that's hard to go back from once you've used it. The cable becomes a "char

I Tried PewDiePie's Open-Source AI Workspace. It's Actually Good. Yes, that PewDiePie. Felix Kjellberg (110M YouTube subscribers) spent late 2025 building a home AI lab — 8 modified RTX 4090s, 256GB of VRAM, running on Arch Linux. He called it "The Swarm." He crashed it running 64 models in parallel. The web frontend he built for it? He open-sourced it. Called it Odysseus . It hit 59,000 GitHub stars fast. I dug into the code expecting a glorified Ollama wrapper. It's not. What it actually is Odysseus isn't just another chat UI. It bundles things no other self-hosted tool does in one place: Chat — local or cloud models (Ollama, vLLM, llama.cpp, OpenAI, OpenRouter, GitHub Copilot) Agent mode — shell, files, web, MCP tools, per-tool toggles Cookbook — scans your GPU, recommends models that actually fit, downloads and serves them in one click Deep Research — multi-step web research that writes you a cited report Email — IMAP/SMTP with AI triage, auto-tagging, draft replies Calendar — CalDAV sync with Radicale, Nextcloud, Apple, Fastmail Memory — persistent, evolving across all your conversations No cloud account. No telemetry. MIT license. Everything lives in your data/ folder. The Cookbook is the standout feature Every other self-hosted UI assumes you already know what model to run. Odysseus doesn't. It scans your hardware, scores 270+ models against your actual VRAM, and gives you a one-click download-and-serve. It understands GGUF vs FP8 vs AWQ. It picks the right backend (vLLM, llama.cpp, Metal on Apple Silicon). Downloaded models persist in a volume — no re-downloading after container restarts. For someone who wants local AI but finds the ecosystem confusing, this is the most accessible on-ramp that currently exists. The code is better than the meme suggests The README has a little ASCII bear face. Don't let it fool you. The entry point app.py is 1,092 lines of real production thinking. A few things that stood out: The .env loader handles Windows BOM silently: loa

Let me start with a confession: I have been writing concurrent code since the only tool in the box was a mutex and a prayer. After a decade of Swift I feel suspicion of any code that touches two threads and claims to be fine. So when Swift 6 showed up promising to prove my concurrency correct at compile time, I had two reactions at once. The grizzled half of me said "sure, kid." The other half — the half that has spent actual weekends chasing a heisenbug that only reproduced on a customer's M1 under sync load — said "...please. Please be real." This is the story of moving Ditto Edge Studio — a SwiftUI debug-and-query tool for the Ditto edge database — to Swift 6's strict concurrency mode. It's a real app: SQLCipher persistence, an embedded MCP server, a SpriteKit presence graph, live sync over Bluetooth and WebSocket. Not a to-do list. The kind of app where concurrency bugs hide in the cracks and wait for a demo. Spoiler: it was worth it. It was also more work than the WWDC talk implied, and the most valuable thing the compiler did happened in the one place I told it to stop looking. Let me show you. First, the Wall: A Dependency That Wasn't Coming to Swift 6 Here's the thing nobody warns you about. Swift 6 language mode isn't really a per-file setting. Your code can be immaculate — every actor isolated, every Sendable accounted for — and you'll still be stuck, because one dependency that isn't Swift 6-ready can hold your entire module hostage. Mine was a code editor. I'd been using a popular SwiftUI editor package for the DQL query editor, and it transitively pulled in a syntax-highlighting library. Both were lovely. Both were also written for a more innocent time, and neither was going to compile under Swift 6 strict concurrency without upstream changes that weren't happening on my timeline. I had the usual three options, and I want to be honest about how tempting the cowardly ones were: Pin the dependency and leave the whole app at Swift 5. Free today, expensive

Hopefully this is the right place to ask, but I'm generally curious if my personal usage of AI does any harm to myself or not. To explain how I use it, I mostly use ChatGPT for things. This would include help with job searching, help with solving problems like on games or technology, and using it to brainstorm about ideas. Sometimes I like to just have conversations with the AI about random topics and ask it for their perspectives as if it were sentient/sapient. And from those, I can learn new information. I've heard that using AI can apparently cause a reduction in cognitive function in a person, but I don't know exactly how it happens or if it's just purely from using AI overall, or if it comes from how it's used. Hearing this has made me worried on whether or not the way I use AI would be harmful to myself and my own brain. I don't use AI for art or ask it to do things for me unless I'm trying to learn a new skill with its help, which should be okay right? What do y'all think of this? Edit: I forgot to mention that I also have used Polybuzz in recent months or last year talking to certain characters, I'd like to hear thoughts on this as well. submitted by /u/Kotal_total [link] [留言]

Last week I did something dumb: I sat down and wrote 50 Claude Code prompts in one sitting. Halfway through I was sure most of them would never get used. But I finished, pushed them to GitHub, and made myself use them for an entire week -- no ad-hoc prompting allowed. The result surprised me. Some skills were life-changing. Others were useless. Here is the honest breakdown. The Methodology I categorized the 50 skills into 5 types: Analysis (12), Generation (14), Debugging (8), Planning (10), Maintenance (6). Rule: every time I hit a task, I must use a skill file or admit I did not have one. The 7 That Actually Saved Me Hours 1. Code Review Assistant (saved ~3h) This was the biggest surprise. I usually review PRs by gut feel -- scan the diff, look for obvious bugs, approve. The Code Review skill forced me to be systematic: On a 400-line PR it caught 2 security issues I would have missed. That alone justified the experiment. 2. Bug Investigator (saved ~2h) Instead of pasting errors and asking "why?", this skill forces you to provide: error message, file context, hypothesis. 3. Dependency Audit (saved ~1h) Scanned a 3-year old Node project. Found 2 CVEs, 8 unused devDependencies (21 MB). 4. Auto Commit Messages (saved ~30m) Saves 2 minutes per commit. Over 15 commits in a week that is 30 minutes. 5. Test Generator (saved ~2h) Generates 5-8 test cases per function in seconds. 6. Refactoring Planner (saved ~1h) Reads the function, identifies extraction candidates, outputs a dependency-ordered plan. 7. Performance Audit (saved ~30m) Found an unoptimized 3 MB hero image and a render-blocking script. The Ones I Never Touched About 8 out of 50 were "not this week" -- Database Migrations, API Documentation, CI/CD Pipeline. What I Learned The ROI is in the analysis skills. Code review, bug investigation, dependency audit -- these are high-judgment tasks where Claude thoroughness beats speed. Skills are habit, not technology. The hardest part was not writing the prompts -- it w

submitted by /u/lune-soft [link] [留言]

Hey DEV community! 👋 I just completely rebuilt and launched Mailfo v2.0 , an privacy-focused temporary disposable email app for Android[cite: 1, 2]. The first version got the job done, but it lacked the fluid user experience, offline reliability, and sleek UI that modern mobile apps need. I decided to tear it down and rewrite it from scratch using Jetpack Compose , Room DB , and modern Android architecture[cite: 1]. Here is a breakdown of what went into the rebuild, why privacy tools like this are essential, and what's new[cite: 1]. 🛡️ Why Use a Temporary Disposable Email? We’ve all been there: you want to download a single source-code snippet, test a new app, or sign up for a one-time service, but you don't want your primary inbox flooded with endless marketing spam[cite: 1]. A disposable email address acts as your personal buffer[cite: 1]. It protects your personal data, prevents tracker exposure, and keeps your real inbox clean[cite: 1]. ⚠️ Pro-tip: Mailfo is designed strictly for low-risk temporary email needs[cite: 1]. Do not use disposable email addresses for banking, legal accounts, or primary account recoveries[cite: 1]! 🛠️ The Tech Stack & Architecture Upgrades Moving from version 1.0 to 2.0 meant migrating away from legacy views to a fully declarative UI workflow[cite: 1, 2]. UI/UX Architecture: Built entirely with Jetpack Compose for smooth performance, a modern look, and native Light, Dark, and System theme switching[cite: 1]. Navigation: Implemented an intuitive bottom navigation system dividing the app into distinct Home, Inbox, and Profile tabs[cite: 1]. Caching & Offline Stability: Integrated a local Room SQLite database to cache inbox summaries and message data[cite: 1]. This guarantees instantaneous screen transitions without annoying network loading spinners[cite: 1]. ✨ Key Features in Mailfo v2.0 Custom & Random Email Generator: Instantly spin up a completely randomized address or build a custom email username using multiple searchable domains[ci

​We just deployed a major update to our SQL Server web sandbox, moving from an individual tool to a collaborative environment: ​✅ Group Rooms (Beta): Create private rooms using security tokens to chat and write code together in real time. ✅ Simulated Linked Servers (Beta): Connect with your friends' sandboxes to run cross-queries strictly in read-only mode to preserve performance. ✅ Gamification & XP: Earn experience points and unlock technical badges as you run queries or build database schemas. ✅ Social Layer: Manage your friends list and customize your public developer profile to showcase your achievements. ​Currently, all features are completely free to use during this phase of the platform. Try it out with your colleagues at tsqlsandbox.online and leave your feedback below!