Uses
Tools, technologies, and resources that power my AI engineering workflow. Always evolving as I discover better ways to build and deploy ML systems.
Development Environment
Visual Studio Code
Primary editor with Python, TypeScript extensions
PyCharm
For complex Python projects and debugging
Jupyter Lab
Data exploration and experiment notebooks
Terminal (Zsh)
Command line with Oh My Zsh configuration
Git
Version control with GitHub for collaboration
AI/ML Stack
Python
Primary language for AI/ML development
PyTorch
Deep learning framework for model development
Transformers
Hugging Face library for NLP models
FastAPI
Building production ML APIs
Pandas
Data manipulation and analysis
NumPy
Numerical computing foundation
Infrastructure & Deployment
Docker
Containerization for consistent environments
AWS/GCP
Cloud platforms for training and deployment
CUDA
GPU acceleration for deep learning
PostgreSQL
Relational database for structured data
Redis
Caching and session management
Vector Databases
Pinecone, Weaviate for embeddings
Productivity Tools
Notion
Project management and documentation
Obsidian
Knowledge management and note-taking
Figma
Design and prototyping
Slack
Team communication
Linear
Issue tracking and project planning
Hardware
MacBook Pro M1
Primary development machine
External Monitor
27" 4K display for extended workspace
Mechanical Keyboard
Custom build for comfortable typing
GPU Servers
Remote access to high-performance computing
Learning Resources
Papers With Code
Latest ML research implementations
Distill.pub
Visual explanations of ML concepts
Fast.ai
Practical deep learning courses
Coursera
Structured AI/ML courses
ArXiv
Latest research papers
Tech Twitter
Following AI researchers and engineers
A Note on Tool Choice
This list represents my current setup as of 2025. The AI/ML landscape evolves rapidly, and I'm always experimenting with new tools and frameworks. For the most up-to-date insights on my workflow, check out my Super Sunday posts where I often discuss new tools and techniques.