Ideas on how to improve my teaching repo? (github.com)

chainliterate is a teaching repository that walks developers through building a production-ready AI application incrementally: each numbered folder is a standalone, deployable app that adds a new capability. The stack centers on LlamaIndex Workflows for orchestration, Chainlit for chat UI, Pydantic for data modeling, and Invoke for CLI utilities; Docker is assumed for deployment. The course pairs the code with step-by-step lessons and exercises and expects use of OpenRouter for LLM completions plus cloud services (GCP for OAuth2, AWS for S3, managed PostgreSQL, and Amplify). It recommends a Mac/Linux environment or VM with access to port 8000 (and optionally 5432). Expect modest cloud costs and prompts to terminate resources after use. This repo is significant because it bridges model experimentation and operational concerns—showing concrete patterns for persistence (Postgres/S3), auth (OAuth2), scaling (caching/pooling, concurrency), grounding (web/file retrieval and RAG), evaluation pipelines, observability/analytics, agentic tooling, and multi-environment deployment (Terraform for AWS, Kubernetes for scale). For practitioners and educators it’s a practical curriculum for production ML engineering: modular, repeatable deployables that teach infra, CI-like workflows, monitoring, and real-world trade-offs when moving from prototypes to resilient services.