Unlock WebAssembly Performance Gains for Compute-Heavy Web Apps

If you keep writing scrolls of text trying to explain to your agent where a button is and how to try and fix it, you might be missing a tool in your arsenal - App Aware Coding Agents I am seeing 3 different architecture models in the market: 1) Framework middleware 2) Browser proxy 3) MCP bridge They solve different jobs and fail in different ways. For visual/frontend workflows, one practical metric beats feature lists: How often does click -> correct code change work on first pass? I wrote a breakdown of the tradeoffs and where each model tends to fit: https://lnkd.in/deg-2njB Curious what teams here are seeing in production on real apps.

Your clean async/await code might be quietly adding 10 seconds to your load time 🦥 We had 15 API calls in SSR. It looked fine. It took 12.8s. While digging into it, I noticed most of them were running sequentially… even though they didn’t need to be. The fix? We didn’t change a single API. Just how they run. • Promise.all → run critical data in parallel ⚡ • Promise.allSettled → don’t let non-critical data block 🛡️ New load time: 2.5s (–80%) 🚀 Same APIs. Same backend. Way faster product. Quick rule of thumb: • Independent + critical → Promise.all • Independent + non-critical → Promise.allSettled • Dependent → await sequentially If you’re learning async/await, this is a trap worth avoiding early. — Honored to have this story featured on tiket.com's Medium publication. Read the full breakdown here: https://lnkd.in/gkhju-e5 #SoftwareEngineering #WebPerformance #JavaScript

Look at the stars... 🌟✨🌌 One .csv and a few iterations with Claude Code were able to produce this app in ~2 hrs. Crazy times we are living in. The dataset is the HYG Star Catalog, combining Hipparcos, Yale, and Gliese data. Over 120,000 stars, each with real 3D coordinates, brightness, and spectral color. Every single point is a real star, positioned exactly where it sits in space relative to our Sun. This is not a final product and is probably filled with possible improvements, but for someone without a traditional developer background, this is crazy. Made with Claude Code, #React, and Three.js #30DayChartChallenge | Day 25: Space #dataviz PS: Special thanks to Yan Holtz for the tremendous quality of his D3 ❤️ React course (can't recommend it enough!). I still have a lot to learn, but it is being immensely helpful. Feel free to explore the app! Link in the comments 👇

A function serves you. A stream doesn't care you exist. This is the mindset shift nobody tells you when you start building real-time systems. A function: → You call it → It runs → It returns → It's gone A stream: → It opens → It emits → It emits → It emits... → (you were never in charge) The problem? Most devs approach streams like functions. They poll instead of subscribe. They call the data instead of listening. They treat state like it's stable — it's not. By the time you read it, it's already stale. So what do you actually do differently? In Flutter, it looks like this: ✗ fetchData() on every event ✓ StreamBuilder — but ONLY at the leaf widget ✗ setState on every emission ✓ Pipe the stream directly into your UI layer ✗ try/catch around a function ✓ .handleError() on the stream itself The catch nobody tells you: StreamBuilder high in the widget tree rebuilds EVERYTHING beneath it on every emission. That's not reactive — that's just expensive. Scope it to the exact widget that needs to react. Not the screen. Not the scaffold. The pixel that changes. When you get the scoping right: The code gets smaller. The bugs get fewer. The system gets faster. Not because you wrote more — because you stopped fighting the data. —— Streams are powerful. Poorly scoped streams are just expensive functions. 

Most AI side projects look finished at the demo screen. They usually break the second real users run into API keys, CORS, downloads, and messy task handling. So I built Meshy App. It’s a React + FastAPI web app for generating 3D models with the Meshy API, but the part I cared about most wasn’t just “make the API call.” It was making the workflow feel usable and production-minded. What I focused on: keeping API keys out of the frontend using a backend proxy to handle CORS and download headaches supporting both text-to-3D and image-to-3D flows adding task polling, history, and cleaner downloads building a UI that feels modern instead of stitched together in a User interface that is private and runs locally on your windows machine. https://lnkd.in/gkvhyasw #Generative3D #3DModeling #3DWorkflow #AIBuilder #AIDev #TextTo3D #ImageTo3D #React #FastAPI #PythonDeveloper #FrontendDevelopment #BackendDevelopment #CreativeCoding #GameDevelopment #3DArtist #IndieDev #MakerCommunity #OpenSource #BuildInPublic #DeveloperExperience

🚀 From Static UI to Real-World Data! Excited to share my latest Flutter project — a News App that fetches real-time data from an API 📰 🎥 Check out the demo video below to see it in action! 💡 What I worked on: - Integrating REST APIs to fetch live news - Parsing JSON data using custom models - Handling loading & error states for better UX - Dealing with null values and edge cases safely - Writing clean, reusable, and scalable code 🔗 GitHub Repository: [https://lnkd.in/diuTsrtz] This project helped me understand how real apps communicate with servers and handle real-world data. Still learning and improving… next challenge loading 🔥 #Flutter #Dart #MobileDevelopment #API #CleanCode #LearningJourne

If you've built complex, highly dynamic apps in Shiny, you know the pain. I love the framework. But somewhere around the Nth time I was knee-deep in JavaScript workarounds for dynamic UI, I started asking: what's the root cause here? My answer: the UI/server split. It works beautifully until structure has to react to state -- then the workarounds start to pile up. So I built irid, an R package that brings component-based reactivity to Shiny. State lives next to markup. No string IDs, no observer lifecycle to manage. A whole category of pain points just disappears. I wrote it all up with live demos you can try in the browser: https://lnkd.in/dtKkJmCD I'd love to hear what you think -- especially if you've been down this rabbit hole yourself!

JS Canvas 2D starts struggling at ~40k particles. WebGPU handles 500k+ at stable FPS. This dev built a live demo combining WebAssembly and WebGPU inside a regular React app to show what the browser platform can actually do. { author: Sylwia Laskowska } https://lnkd.in/edyCGCQd 

I've been using Claude Code heavily for a while now. But I had no idea what it was costing me — per day, per model, per session. So I built something about it. AI Pulse is a native macOS menubar app that reads directly from Claude Code and Codex CLI's local session files — token usage, context window monitoring, daily spend, model breakdown — all without an API key. No data ever leaves your machine. What made this genuinely interesting to build was the process: Step 1 - UI designed in Google AI Studio (Gemini) I used Gemini as a design collaborator, iterating on component layout, visual hierarchy, and the colour system until I had a working React prototype. No Figma. No hand-written CSS from scratch. Gemini handled the full UI layer. Step 2 - Claude Code migrated and built the entire codebase I handed the prototype to Claude Code and had it wire the real data pipeline: Rust JSONL parsers, IPC commands, Zod schema validation, Zustand state management — while enforcing strict TypeScript and Clippy pedantic on every commit. The pre-commit hook ran on every change. Zero manual debugging of type errors. Step 3 - Tauri v2 turned it into a native macOS app Transparent vibrancy window. System tray icon. Global keyboard shortcut (Cmd+Shift+A). LaunchAgent for login startup. A recursive kqueue file watcher for live updates the moment a session file changes. All in Rust. No Electron. The final binary is under 10 MB. Three tools. One weekend. A production desktop app. As a VP, I spend a lot of time thinking about where AI fits into engineering workflows. This was a deliberate experiment: could you take three different AI tools - one for design, one for development, one as a runtime - and ship something real? The answer is yes. And the seams were smaller than I expected. → Download: https://lnkd.in/gBuxEn5b

How do you manage persistent data without a backend? 🤔 For my Blinkit Clone project, I utilized LocalStorage to ensure that a user’s cart remains intact even after a page refresh. This project was a great exercise in state management and DOM manipulation using Vanilla JavaScript. Highlights: Handled complex UI updates for the cart. Implemented a fake delivery tracking system to mimic real-world apps. Clean and modular code structure for better scalability. Full project code is live on my GitHub! (https://lnkd.in/d95EwtQk) Project Deploye Link (https://lnkd.in/di26sUu7)