Skip to content
View p3nGu1nZz's full-sized avatar
🌎
Working from Space
🌎
Working from Space
  • N. America

Block or report p3nGu1nZz

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
p3nGu1nZz/README.md

Hi there 👋

I’m p3ngu1nZz, a passionate gamer and developer working on a cat game engine written with Meow. I’m working on creating fun and immersive cat games using Meow, C++, and WebAssembly. I also love to contribute to open source projects and learn new technologies. 😊

Summary

  • Experienced and versatile software engineer with over 15 years of experience in various domains such as game development, AI, web development, and cloud computing.
  • Skilled in multiple programming languages, frameworks, and tools such as Java, C#, C++, Python, Unity, Unreal, ExtJS, GWT, NodeJS, PyTorch, and more.
  • Passionate about creating innovative and engaging solutions that solve real-world problems and delight users.

Skills

Artifical Intelligence

  • AI Operator
  • AI Scientist
  • ML Trainer
  • Robotist
  • Cybernetics

Programming Languages

  • C#, C, Cpp
  • Python
  • Java/JavaScript\
  • Dragon

Game Engines

  • Unity
  • Unreal

Tools

  • ML-Agents
  • PyTorch
  • TensorFlow
  • NodeJS
  • Electron
  • Dragon

IDEs / Editors

  • Visual Studio
  • Nano
  • Vi/m

Pinned Loading

  1. Tau Tau Public

    Tau LLM made with Unity 6 ML Agents

    C# 11 4

  2. ophrase ophrase Public

    generate paraphrase using ollama and python

    Python 3

  3. oproof oproof Public

    Validate prompt-response pairs using Ollama and Python.

    Python 2

  4. pca-optimizer pca-optimizer Public

    A Python package for efficient PCA-based dimensionality reduction using scikit-learn.

    1