Skip to content
View wahabzh's full-sized avatar
👋
👋

Achievements

Achievement: YOLOAchievement: Pull SharkAchievement: QuickdrawAchievement: Pair Extraordinaire

Achievements

Achievement: YOLOAchievement: Pull SharkAchievement: QuickdrawAchievement: Pair Extraordinaire

Highlights

  • Pro

Block or report wahabzh

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Please don't include any personal information such as legal names or email addresses. Maximum 100 characters, markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
wahabzh/README.md

👋 Hey, What's Up?

const aboutMe = {
    "description": "🧑‍💻 Product-minded Full-Stack Software Engineer with 3+ years' experience.",
    "highlight": "🚀 Built products that boosted traffic by 2,400+ and conversions by 40%.",
    "passion": "🤖 Love shipping AI-driven apps that make a difference",
};

🟢 Open to new opportunities!

🛠️ Tech & Tools

JavaScript TypeScript Python React Next.js Tailwind Material UI Node.js Express Flask PostgreSQL MongoDB PyTorch Git Postman Vercel Figma

Pinned Loading

  1. StudySync StudySync Public

    🧠✨ AI-powered collaborative learning platform with real-time document editing, instant quiz generation, and smart flashcards. Built with Next.js 15, Supabase, and Gemini AI.

    TypeScript

  2. transformer-from-scratch transformer-from-scratch Public

    🤖 Complete Transformer implementation from scratch using PyTorch. Trained on Sherlock Holmes stories to generate Victorian-era prose.

    Jupyter Notebook

  3. usjav/DevLink usjav/DevLink Public

    🚀 LLM-powered job platform connecting developers & companies • React + Node.js + MongoDB + OpenAI • AI-generated cover letters, bios & job descriptions

    JavaScript

  4. lora-smollm-finetuning lora-smollm-finetuning Public

    ⚙️ LoRA implementation for efficient SmolLM fine-tuning. Achieves comparable performance with only 0.24% trainable parameters.

    Jupyter Notebook