python

SQLAlchemy 2.0 from the production angle. The engine/session/transaction layering, the typed declarative, the identity map, the N+1 query problem, async-only gotchas (no lazy loading), savepoint nesting, and the connection-pool knobs that decide whether the backend survives load.

Dependency injection as a pattern, FastAPI's Depends as one concrete implementation. Sub-dependencies and per-request caching, yield-based cleanup, where Pydantic v2 validation runs, lifespan-scoped resources, BackgroundTasks vs real queues, and the override trick that makes the whole thing testable.

Python's type system has two audiences — static checkers and runtime frameworks. Generics, Protocol, TypedDict, ParamSpec, type narrowing, and runtime introspection, framed as the spec that Pydantic, FastAPI, and SQLAlchemy actually execute.

Async Python from the bytecode up. Coroutines vs return values, how the event loop schedules tasks, when async beats threading or multiprocessing, structured concurrency with TaskGroup, and the cancellation rules that production backends live or die by.

Move beyond toy ReAct loops. Learn how to build a production-grade agent harness with a robust control loop, tool registry, schema validation, retry logic, token budgets, abort signals, and a persistent journal that survives crashes.

Learn to build Model Context Protocol servers from scratch using the official Python and TypeScript SDKs. Cover tool schemas, resource URIs, prompt templates, and how Claude Code, Claude.ai, and Cursor consume them.

A beginner's guide to LangChain, the popular framework for composing applications with Large Language Models. Learn the core concepts of Models, Prompts, and Chains, and build a simple application using the LangChain Expression Language (LCEL).

Dive into Apache Airflow, the powerful platform for programmatically authoring, scheduling, and monitoring complex data workflows. Learn about DAGs, operators, and how to build robust, scalable pipelines for modern data engineering.