Why start here
Aulafy is designed for building with AI without depending on one account, one cloud, or one tool. That requires a small foundation: terminal, Python, Git, isolated environments, Docker, and judgment about what runs locally and what can call an external API.
You do not need to be a systems administrator. You do need to repeat a project tomorrow, explain how to run it, and review what changed when AI edited files.
The stack pieces
- Terminal: where you start models, servers, scripts, and tools such as Codex, Claude Code, Ollama, or Docker.
- Modern Python: the glue for scripts, RAG, evals, automations, and model tests.
- Git: the history of code, prompts, configuration, and technical decisions.
- Docker: the practical way to run services such as Qdrant, n8n, Open WebUI, or databases without polluting your machine.
- Local models: useful for privacy, predictable cost, repetitive tasks, and reproducible learning.
Local vs cloud rule of thumb
Use local when data is sensitive, repetitive, cheap to process on your machine, or when you want to learn how the system works. Use cloud when you need the highest quality, huge context, stronger reasoning, or an integration you do not have locally yet.
decision: sensitive_data: local fast_prototype: cloud_or_local repetitive_cost: local highest_quality: frontier technical_learning: local hybrid_production: gateway_with_routing
Deliverable
By the end of this lesson you should be able to draw your stack: which tool edits code, which model answers, where data lives, how the project is versioned, and how it starts again.