∞ ── TECH.STACK
Stack
Tools and technologies I use to build ML systems, agentic pipelines, and full-stack applications. Focused on the intersection of research and production.
Languages
Python
Primary language for all ML/AI work
C++
Performance-critical ML components
TypeScript
Frontend & fullstack applications
SQL
Data wrangling and analytics
Bash
Scripting, automation, server ops
ML / Deep Learning
PyTorch
Primary DL framework for all model work
HuggingFace Transformers
Fine-tuning and inference of LLMs
scikit-learn
Classical ML, preprocessing, evaluation
NumPy / Pandas
Data manipulation and numerical ops
OpenCV
Computer vision pipelines
Weights & Biases
Experiment tracking and visualisation
LLMs & Agents
LangChain / LangGraph
Agentic pipelines and state machines
OpenAI API
GPT-4 and embedding API integration
Groq (Llama 3)
Ultra-fast inference for agent tasks
Ollama
Local model serving and prototyping
Pinecone / ChromaDB
Vector stores for RAG systems
Streamlit / Gradio
Rapid ML app prototyping
Backend & APIs
FastAPI
Async REST APIs for ML model serving
Next.js
Full-stack web apps with server components
PostgreSQL
Primary relational database
Redis
Caching, queues, session management
Docker
Containerised model deployment
Tools & Infra
Git / GitHub
Version control, CI/CD via Actions
Jupyter / VS Code
Exploration and production code
Linux (Ubuntu)
Primary dev and server environment
Vercel
Frontend deployment and edge functions
Cursor
AI-assisted development
Currently Learning
Triton
Custom CUDA kernels for GPU ops
CUDA / C++ extensions
Low-level DL performance engineering
Rust
Systems programming for ML tooling