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https://youtu.be/kL1PDqzqhM4 This video has corrected information on the history of Lorem Ipsum! submitted by /u/GreatRedditorThracc [link] [留言]

I added a few more asteroids to my build asteroids project and suddenly my FPS just tanked. Like completely unusable. Now I’m looking at my code and realizing my build asteroids loop is basically just redrawing everything every frame with no real optimization. I assumed request animation frame would kinda handle it automatically but yeah… that was me being naive. Is there a normal pattern people use when building asteroids so it doesn’t just fall apart as soon as you add more objects? submitted by /u/Tricky-Highway-7099 [link] [留言]

Maybe it’s just me, but I’ve been running into more and more half-working products lately. Buttons that do nothing. Checkouts that fail silently. Forms that throw errors with no explanation. And not from random small sites either, from companies that should absolutely know better. I think it’s the result of AI + fast shipping + less quality control. Teams are pushing out features at a speed that wasn’t possible 2 years ago, but the QA, testing, and ownership of quality hasn’t scaled with it. AI didn’t break the web. It just made it easier to ship things that were never properly checked. The other thing I’ve noticed: when something breaks now, you can’t even get to a real person. Support bots loop you in circles, and the actual humans who could fix it are buried somewhere behind 5 layers of auto-responses. Curious if others are seeing the same thing, or if I’m just unlucky lately. submitted by /u/Good-Locksmith-4978 [link] [留言]

As part of my game development journey, I recently created Hidden Collector , a Unity-based game where players explore levels and collect hidden items while progressing through different challenges. This project started as a way for me to improve my Unity and C# skills, but it quickly became an opportunity to learn about game design, UI systems, audio management, scene transitions, and player experience. What I Worked On While building Hidden Collector, I implemented: Player movement and interactions Collectible item systems Multiple game levels UI menus and game screens Audio and sound effects Progress tracking Game flow and scene management Challenges During Development One of the biggest challenges was making different game systems work together smoothly. Something as simple as collecting an item often required updates to UI elements, game state management, and progression systems. Debugging these interactions taught me a lot about organizing Unity projects and writing maintainable code. What I Learned This project helped me gain experience with: Unity Engine C# scripting Game architecture UI implementation Audio management Debugging and testing Most importantly, I learned that building complete projects teaches far more than following tutorials. Play the Game You can try Hidden Collector here: https://sinxcos07.itch.io/hiddencollector Screenshots What's Next? I'm continuing to improve my game development skills by building new projects, experimenting with different mechanics, and learning more about creating engaging player experiences. If you try the game, I'd love to hear your feedback. By Suryansh Sinha (sinxcos07) Connect With Me GitHub: https://github.com/sinxcos07 LinkedIn: https://www.linkedin.com/in/suryansh-sinha/ Play Hidden Collector: https://sinxcos07.itch.io/hiddencollector

GitHub - nikhilt101/email-blast-tool: Open source HTML email sender tool using CSV/XLSX + Gmail SMTP · GitHub Open source HTML email sender tool using CSV/XLSX + Gmail SMTP - nikhilt101/email-blast-tool github.com

You build a tournament bracket with a popular React library. In Chrome it's perfect — neat columns, clean connector lines. Then you open it on an iPhone, or in Safari, or inside your Capacitor app… and every match is crammed into the top-left corner, stacked on top of the round headers. If you've ever shipped a bracket to iOS, you've probably seen this exact bug. Here's why it happens — and a tiny library that fixes it for good. The symptom It looks fine everywhere Chromium runs (Chrome, Edge, Android WebView) and completely broken everywhere WebKit runs: Safari (macOS and iOS) iOS WKWebView Capacitor / Cordova apps Electron-on-WebKit The matches don't just shift a little — they all render at coordinate (0,0) of the bracket, piling on top of each other and the headers. The cause: SVG <foreignObject> in WebKit Most React bracket libraries — @g-loot/react-tournament-brackets , react-tournament-bracket , and friends — render the bracket as an SVG and place each match's HTML inside a <foreignObject> positioned with x / y attributes. WebKit has a long-standing bug: it ignores x , y , and transform on <foreignObject> and positions the content relative to the top-level <svg> instead of the foreignObject's own coordinates. Every match therefore collapses to the origin. And there's no CSS escape hatch — x , y , and transform are all ignored on foreignObject in Safari, so you can't nudge the content back into place. I even tried patching a library to wrap each match in a <g transform="translate(x,y)"> instead of a nested <svg x y> ; WebKit ignores ancestor transforms for foreignObject positioning too. The SVG approach is simply a dead end on WebKit. The fix: don't use SVG at all A bracket is really just columns of cards joined by connector lines — and both are expressible in plain CSS. Here's the key insight. Put each round in a flex column where every match sits in an equal flex: 1 slot. Because each round has half the matches of the previous one, a match's slot spans exactl

By Suryansh Sinha (sinxcos07) Introduction Recently, I built AstroFit , a fitness-focused web application as a personal project to learn more about modern web development, deployment, databases, and building complete applications from idea to production. This project helped me understand how different parts of a web application work together, from the user interface to backend functionality and deployment. Why I Built AstroFit I wanted to work on a project that felt practical and useful while also helping me improve my development skills. Instead of creating a simple clone project, I decided to build a fitness application where I could experiment with real-world features and deployment workflows. Development Journey Building AstroFit involved much more than just creating pages and connecting them together. Some of the areas I explored while working on this project included: Frontend development Backend integration Database management Authentication systems Deployment and hosting Debugging production issues One of the biggest learning experiences was understanding how different technologies communicate with each other in a complete application. Future Plans I plan to continue improving AstroFit by adding more features, refining the user experience, and expanding its capabilities over time. This project is still evolving, and I'm excited to keep working on it. Project Links Live Demo: astrofit-fitness.vercel.app GitHub: sinxcos07 / astrofit-frontend Fitness platform combining workout tracking and astrology-inspired personalization. AstroFit AstroFit is a modern fitness web application that combines workout tracking with astrology-inspired personalization to create a unique and engaging fitness experience. Features Modern responsive UI Astrology-inspired fitness experience Workout tracking interface User authentication system Backend integration Smooth and interactive design Mobile-friendly layout Tech Stack Frontend HTML5 CSS3 JavaScript Backend Node.js Express.js SQL

This is a submission for the Hermes Agent Challenge : Write About Hermes Agent What if the biggest limitation in AI today isn't reasoning, model size, or context windows? What if it's memory? Every morning, millions of people open ChatGPT, Claude, Gemini, or another AI assistant and start a conversation. The AI seems intelligent. It writes code. It explains concepts. It helps brainstorm ideas. It can even help design an entire software architecture. Then the conversation ends. Tomorrow? It remembers nothing. Imagine hiring a senior engineer who forgets everything at the end of every workday. Every morning you would need to explain: What your company does How your product works Which technologies you use Why certain decisions were made What happened yesterday Nobody would call that employee productive. Yet this is exactly how most AI systems operate. And it reveals something important: Most AI agents aren't actually learning from experience. They're simply reasoning over whatever context happens to be available right now. That distinction may define the future of agentic AI. Because the next generation of AI won't just need better reasoning. It will need memory. And that's where Hermes Agent becomes interesting. The Strange Reality of Modern AI The public perception of AI often looks like this: User → AI → Intelligence But the reality is closer to this: User → Context Window → AI → Response The AI only knows what exists inside its current context. Once that context disappears, so does most of its understanding. This is why many AI experiences feel surprisingly repetitive. You spend 30 minutes explaining your project. The AI finally understands your goals. The answers become better. The recommendations become more relevant. Then the session ends. The next conversation starts from scratch. Not because the model isn't powerful. But because the knowledge never became persistent. Context Windows Are Not Memory A context window is not memory. It is temporary working space.

This is a submission for the Hermes Agent Challenge : Write About Hermes Agent Most AI agents have a dirty secret: they forget everything the moment the session ends. You explain your project once. Then again next time. And again. The agent never gets better at your workflow — it just stays a general-purpose tool that happens to be smart. Hermes Agent is built differently. It ships with two systems that together form something closer to a genuine long-term memory: a Skills System and a Persistent Memory layer. This post digs into how they actually work — not the marketing summary, but the mechanics. The Problem With Stateless Agents Before getting into Hermes, it's worth understanding what problem this solves. Standard LLM-based agents operate inside a context window. Everything the agent knows during a session lives in that window. When the session ends, it's gone. The next time you open a conversation, you're talking to an agent with no memory of you, your codebase, your preferences, or the workflows you've developed together. Some tools patch this with naive "memory" — they dump a text blob of past conversations into the system prompt. This works up to a point, but it's not selective, it gets expensive as context grows, and it doesn't help the agent get better at tasks — just recall facts. Hermes takes a different approach with two distinct systems serving different purposes. System 1: The Skills System (Procedural Memory) Skills in Hermes aren't plugins you install. They're on-demand knowledge documents — markdown files the agent loads when it needs them, and more importantly, creates on its own when it discovers something worth remembering. The SKILL.md Format Every skill is a structured markdown file with a YAML frontmatter header: --- name : deploy-runbook description : Our deployment runbook — services, rollback, Slack channels version : 1.0.0 metadata : hermes : tags : [ deployment , runbook , internal ] requires_toolsets : [ terminal ] --- # Deploy Runbook

I’ve been working on Hooklistener for a long time now, several months, probably closer to years. It’s a free tool for receiving and inspecting webhook requests. The CLI is open source and lets you forward captured requests to localhost whenever you want, as many times as you need. It can also create ngrok-like tunnels, but the feature I personally find most useful is listen : it forwards incoming webhook requests from Hooklistener directly to your local service. I recorded a short video showing how it works. CLI repo: http://github.com/hooklistener/hooklistener-cli/ The rest of the app is built with Elixir and Phoenix. If you would like to try it: https://hooklistener.com , thanks! submitted by /u/absoluterror [link] [留言]

submitted by /u/Wise_Stick9613 [link] [留言]

This is a submission for the [GitHub Finish-Up-A-Thon Challenge] What I Built LeadBotX is an AI-powered lead generation platform prototype designed to simulate how modern businesses can automatically discover, filter, and manage high-quality leads using intelligent automation workflows. This project is not just a simple frontend demo — it represents a revived college-level concept that I initially started earlier but left incomplete due to time constraints and complexity. For this challenge, I revisited the idea and transformed it into a fully structured SaaS-style frontend system with improved UI, better workflow representation, and a more realistic product-like experience. The main goal of LeadBotX is to visually demonstrate how an AI-based lead generation system works end-to-end in a real-world SaaS environment. Tech Stack This project was built using modern frontend technologies: React.js → Component-based UI structure CSS3 → Custom responsive styling system AOS (Animate On Scroll) → Smooth scroll animations Lucide Icons & React Icons → UI iconography GitHub Copilot → Assisted in code generation, debugging, and UI improvements Demo Source Code: https://github.com/Khushisingh-dev/LeadBotX Production Landing Page: https://lead-bot-x.vercel.app/ Before (Initial Version)- After (Final Version)- The Comeback Story This project originally started as a college-level concept during my development learning phase. At that time, I built only a basic structure and initial UI, but I was unable to complete it due to time limitations and complexity of the idea. It remained an unfinished project for a long time. When I came across this challenge, I decided to revisit LeadBotX and transform it into something more meaningful and complete. Instead of just polishing the UI, I focused on: Rebuilding the structure into a proper SaaS layout Improving workflow clarity and user journey Enhancing UI/UX consistency across all sections Making the product feel like a real-world AI tool prot

Most apps don't just call one API endpoint. They call a whole chain of them. For example, you might log in, get a token, and then pass that token to another service. Tracking these multi-step chains can get messy quickly. To help fix this, the OpenAPI Initiative created the Arazzo Specification . It gives us a standard way to link different endpoints into clear workflows. But writing these workflow files by hand in a regular text editor is tough. It is very easy to lose track of how data moves from one step to the next. That is why I built Arazzo Visualizer for VS Code. It is a free, open-source extension that makes the Arazzo spec visual and easy to use. Live Interactive Graphs The extension reads your workflow files and turns them into interactive maps on the fly. See Data Flow: Look at exactly how data moves between steps. Catch Errors Early: Spot broken paths before you even run your code. Clean Layouts: Navigate large workflows without getting lost in thousands of lines of text. Built-In Workflow Runner Seeing the map is great, but testing it is even better. The tool has a step-by-step runner built right into your editor. Run a single step or execute the whole chain. See real-time data payloads and HTTP headers. Watch requests happen live to pinpoint bugs fast. Give it a Try The project is fully open source, and you can grab it or check out the code using the links below: Download: Install it directly from the VS Code Marketplace . Source Code: Check out the repository, report bugs, or contribute on GitHub . Deep Dive: Read my full technical breakdown and design on Medium . If you are working with API chains, I would love for you to try it out. Drop your feedback in the comments below! Note: Arazzo v1.1.0 is out with official AsyncAPI support. I am currently updating the VS Code extension to support these new features. Stay tuned for future updates!

By Takeshi Yokoyama — Onecarat Labs Hi. I'm Yokoyama, and I build a local-first AI text editor as a side project, along with a few other experimental tools. Working on them, I keep running into the same question about where the web is going. This post is one observation, plus a small experiment I built to test it — including a Chrome extension you can actually try. The short version: I think websites will increasingly be read through AI agents, reshaped per reader, on the fly. And once that happens, there's a clear gap between sites that are easy for an AI to read and sites that aren't. What's starting to happen Until now, people read websites as websites. You open the top page, follow the menu, read the body, click a button — tracing the path the maker designed. As local AI and AI agents become normal, that breaks. People stop opening the page directly. They tell an AI what they want — "Can I try this quickly?" , "I just want to check it's safe" , "Just the gist" — and the AI reads the web and reshapes it into the form that reader wants. What the reader receives is no longer the layout the maker built. This isn't speculation. The idea that AI generates the interface for the reader already has a name — Generative UI — and it's one of the hottest areas in frontend right now, with Google, Vercel and others building toward it. But notice who's holding the pen in almost every version of that story: the site , or an AI embedded in an app — something under the maker's control. What I'm looking at is one step past that: a local AI, in the reader's own hands, reshaping any site into that person's preferred form — with no involvement from the maker at all. The initiative moves from the maker to the reader. The part that nags at me as a builder I build software too. So this shift nags at me. A site carries its maker's intent and rights. The order things appear in, what gets emphasized, the tone. Design, copy, flow — all of it is deliberate. Having an AI quietly reorder, rewri

A walkthrough of the architecture decisions behind Flacron Gamezone a production full-stack app built with Next.js, Express, PostgreSQL, and Redis. When a client approached me to build a live football match discovery platform, the requirements sounded straightforward on the surface: show live scores, let users subscribe, handle authentication. But the moment you start thinking about how those pieces connect in production, straightforward gets complicated fast. This is the story of how I designed the backend for Flacron Gamezone — what decisions I made, why I made them, and what broke along the way. Table of Contents The Problem With "Just Building It" The Architecture: Four Distinct Layers Why This Matters to a Client The Bug That Taught Me Something Real The Full Stack at a Glance What I'd Do Differently The Problem With "Just Building It" The easiest version of this app is a single Express file: one route handler that queries the database, formats the data, and sends a response. I've seen this pattern in tutorials everywhere. It works for demos. It falls apart in production. The problems are predictable: you can't test business logic without hitting the database, a change in one feature quietly breaks another, and the moment a second developer joins the codebase, nobody knows where anything lives. I wanted to build something I could actually be proud to show an employer or a client. That meant committing to a proper layered architecture from day one, even on a project this size. The Architecture: Four Distinct Layers The entire Express backend is organized into four layers. Each layer has one job and talks only to the layer directly below it. Route → Controller → Service → Repository Here's what each one actually does. Routes are just maps. They declare that POST /api/v1/subscriptions exists, attach the auth middleware, and hand off to the controller. No logic lives here. Controllers handle the HTTP boundary. They extract data from req.body or req.params , call th

I've recently been getting reacquainted with frontend dev after having spent the last few years focusing mostly on design. I plan to do a combination of both again from now on. The last full-time job I was at we used drag-and-drop website builders that seem to be all the rage these days. I gotta say it was a truly painful experience. The websites we would handover would be a complete mess, clients would have to edit columns, widgets, layouts themselves which just felt like a recipe for disaster. We would often have to duplicate various widgets such as image sliders, one of mobile and one for desktop. I'd then have to explain to clients that they needed to update them twice, with the same content. Also, a lot of times I'd have to set up HTML widgets because of how limited the software was, so I'd also have to explain to clients that they'd sometimes need to edit actual code when simply updating content on their site. But the concerning thing is, I've had a number of other job interviews at marketing agencies lately and it seems like EVERYONE is doing the same thing. I also sometimes hear on podcasts people describing default WordPress as being 'old fashioned', as though the future of web dev is just doing everything with these hideous website builders WTF 🤮 Are these people mainly just taking the basic / shitty web dev projects while leaving the proper ones to those who know how to do it properly? or have a lot of developers lost out on work because of these? submitted by /u/Weekly_Frosting_5868 [link] [留言]

174 million smart TVs, most of which run Linux. 3.9 billion Android phones. Zero marketing. Tonight, somewhere around the world, a person will press the power button on their Samsung TV. A proprietary Samsung logo will appear. A polished menu will load. They will open Netflix, scroll through recommendations, and pick a movie. They will never know that every frame they see is being scheduled, managed, and rendered by a Linux kernel, the invisible engine that sits between apps and hardware. They will then reach for their Android phone to check something on social media. Another Linux kernel. If they are sitting in a Tesla, the touchscreen showing their charging status is running yet another Linux kernel. The “year of the Linux desktop” debate has been running for two decades. Entire forums exist to argue about whether 2025, 2026, or 2027 will finally be the year Linux takes over the PC market.

CSS @function - DEV Community CSS just got its biggest quality-of-life upgrade since custom properties. For years, if you wanted... dev.to

You’re Overpaying Every Day — You Just Can’t See It Think about the last time you asked an AI to clean up your meeting notes. You probably opened a new chat, pasted in the transcript — maybe 1,500 words — then pasted your usual notes template on top of that, then said something like “format this, bold the action items.” It worked. Useful, even. But here’s what actually happened: the model just read ~3,000 words to produce ~300 words of output. Do that five times a week. Every week. And now think about what’s riding along in that context every single time — your template, your formatting preferences, all the background you’ve already explained before. The model doesn’t remember any of it. It reads it fresh on every call. Every repeat. Every charge. This isn’t a flaw in ChatGPT. It’s the fundamental nature of chat as a paradigm. 2. Chat Is Great — But It Has a Structural Bug Chat is the most natural way to start with AI. Unclear what you want? Talk it out. Need to change direction? Just say so. The feedback loop is instant, the barrier is zero. That’s why everyone starts there. But chat has a structural problem: every single turn carries the entire history. This is how context windows work — the “conversation history the model reads every single time.” Every API call packages up your full history and sends it to the model. You pay for every token the model reads. Ten rounds in, round ten doesn’t cost the price of one message. It costs the price of all ten, stacked. Here’s a concrete version of this. Say you use AI to write your weekly status update. You paste in your bullet points from the week, say “turn this into a proper update,” tweak the tone, go back and forth a couple times. Feels efficient. But those bullets, plus the AI’s draft, plus your follow-up messages, plus the format you’re implicitly re-explaining each time — the real token cost of one weekly update is probably 5 to 8x what you’d guess. You’re paying for repeated context. The bill just isn’t obvious e

How I Built a Bilingual AI-Powered Business Manager for Street Vendors in Jamshedpur The Problem Walk through any street in Jamshedpur, Jharkhand, and you'll find hundreds of small shop owners and hawkers running their entire business from memory — stock levels, daily sales, employee wages, profit margins. They have no tools. Notebooks get lost. Mental math fails. And at the end of the day, most don't even know if they made a profit. These vendors are not tech-illiterate. They have smartphones. They use WhatsApp. But every existing business app is either too complex, too expensive, or only available in English. I wanted to solve that. The Solution — Dukaan Manager Dukaan Manager is a free, offline-capable Progressive Web App (PWA) and Android app built for small shop owners and street vendors in India. It runs entirely in the browser — no server, no subscription, no data charges beyond the initial load. Built using HTML, CSS, JavaScript , and powered by the Gemini 2.0 Flash API , it gives every small vendor access to a smart business advisor in their pocket. What It Does 📦 Stock Management Vendors can add items by name or photo. Each item stores buying price, selling price, and quantity. The app automatically calculates profit margins and flags low-stock items with colour-coded alerts. 💰 Daily Sales Recording Sales are recorded item by item, linked to which employee made the sale. The app auto-fills prices and calculates the total. Unsaved drafts survive accidental page refreshes using sessionStorage. 👥 Employee Tracking Add employees with their role, salary, phone number, and joining date. Mark daily attendance (present/absent) with one tap. Track total sales made by each employee across all time. 📅 Sales History Every saved sale is stored permanently in the browser's localStorage. History is grouped by date, collapsible, and shows daily revenue and profit — clean and simple. 🤖 AI Business Advisor (Gemini-Powered) This is where Google AI comes in. Using the Gemini