Practical guides on AI development tooling, workflows, and production engineering.
A production-focused guide to configuring VS Code v1.110 for AI engineering, covering essential extensions, workspace settings, and the new Python Environments extension for professional AI workflows.
How to use Claude Code as a production-grade pair programmer in your terminal. Covers CLAUDE.md configuration, prompt patterns for scaffolding, refactoring, and debugging, plus the critical difference between vibe coding and AI-assisted engineering.
A practical guide to managing Python environments for AI projects using uv, the Rust-based tool that replaces pyenv, pip, poetry, and virtualenv. Covers dependency pinning, reproducible environments, and managing heavy ML dependencies.
Production-tested Git strategies for AI projects. Covers branching for ML experiments, Git LFS for model artifacts, .gitignore patterns for AI repos, and commit hygiene that prevents the chaos AI codebases accumulate.
A complete guide to secrets management for AI engineers. Covers .env files, python-dotenv, pydantic-settings, cloud secret managers (AWS, GCP, OCI Vault), and the twelve-factor app pattern for production AI services.
A practical guide to Docker for AI services. Covers writing production Dockerfiles with multi-stage builds, GPU-aware containers, docker-compose for local multi-service development, and Docker Engine v29 optimizations for ML workloads.
Opinionated folder structure patterns for RAG applications, multi-agent systems, and API-backed AI services. Learn how project layout communicates architecture and prevents the spaghetti that kills AI project maintainability.
Configure Ruff v0.15, ty, and pre-commit hooks for AI/ML Python codebases. Learn how static analysis catches the subtle bugs in LLM response handling that runtime testing misses, and why the Astral toolchain has replaced the old Python linting stack.