
Understanding LLM APIs and Model Families: A Production Guide for April 2026
A practitioner's map of Claude Opus 4.7, Claude Sonnet 4.6, GPT-5.4, and Llama 4. Current pricing, model IDs, and the provider abstraction you should ship on day one.
Practical guides on AI development tooling, workflows, and production engineering.

A practitioner's map of Claude Opus 4.7, Claude Sonnet 4.6, GPT-5.4, and Llama 4. Current pricing, model IDs, and the provider abstraction you should ship on day one.

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 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.

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.

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.

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.

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.

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.

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.