I'm passionate about building modern, fast, and highly optimized web applications. I love experimenting with cutting-edge technologies and contributing to the open-source ecosystem.
Contact me for collaboration or questions!
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Building and tuning the ultimate Node.js tech stack for Web App:
Component/Layer Solution Web Framework Vike Lite Frontend Framework SolidJS CSS Framework TailwindCSS CSS Components daisyUI Web Server Hono Web APIs REST-as-RPC Database MongoDB PWA Vite PWA Cloud Provider Vercel -
Contributing to vLite.js player to replace Video.js player → vLite.js currently lacks some features (see issues and PRs)
- 📻 Radio Playlist ~ website with a Playlist of Radio stations
- Deprecated Packages
- mongodb-query-parser-esm ~ fork of mongodb-query-parser to support ESM
- shell-bson-parser-esm ~ fork of shell-bson-parser to support ESM
- mongo-solid ~ rebuild of Mongo Express using a modern technology stack
- depatcher ~ apply patches (
.patchfiles) to dependencies - vite-patcher ~ patch Vite/Vike config to add plugins
- puppeteer-stealth-launcher ~ launch Puppeteer as stealth and without advertisements
- vike-lite ~ lite version of Vike web framework
- node-zip-stream ~ minimal, dependency-free streaming ZIP reader/writer for Node.js
- repo-to-llm ~ convert a source code of a GitHub repository into a single PDF to use as context of an LLM
- Deprecated Templates
- Yarn Berry
- Vike Lite + React + daisyUI + Hono
- Vike Lite + SolidJS + daisyUI + Hono
- Vike Lite + Vue + daisyUI + Hono
I'm looking for contributors to help build these projects. If you're interested, contact me.
A web app that visualizes an exercise progression tree as an interactive graph, grouped by difficulty and rank. Includes an AI-assisted wizard that generates custom workouts based on:
- Target skills/exercises
- Current level and experience
- Exercise progressions (repetitions/seconds required to advance)
Tech stack idea: SolidJS, D3.js/vis.js, OpenAI API, IndexedDB
A real-time fitness web app that provides live feedback and error corrections during exercise execution. Uses pose recognition from the device's camera and compares it with a training set generated from input videos.
Tech stack idea: TensorFlow.js/MediaPipe, WebRTC, SolidJS, Web Workers