Browse courses and lessons by topic, difficulty and tag.
Testing systems that are too large, too distributed or too non-deterministic for examples alone. Strategy and risk, property-based and mutation testing, fuzzing, contracts, load and resilience, testing in production, and the gates that decide a release.
Learn containers from nothing at all. Every command arrives as the answer to a problem you can feel — from your first docker run to an image you built, configured, debugged and published yourself. No container experience assumed.
Kubernetes from the ground up, one object at a time. Pods, Deployments, Services, Ingress, configuration, storage, probes and rollouts — each introduced by the failure it absorbs, on a cluster running on your own laptop.
Learn software testing and QA from scratch. What testing is actually for, how software really breaks, how to design cases and find bugs nobody wrote a case for, and how to report them so they get fixed. No experience assumed.
The testing that shows up in real codebases: suite structure, unit and integration boundaries, fakes and fixtures, API and browser automation, flakiness, CI, coverage, visual and accessibility checks, and the metrics worth reporting.
How Python works underneath, and what that buys you. The data model, descriptors, metaclasses, the MRO, the import system, memory and the GIL, threads and asyncio, performance work, advanced typing, and authoring libraries other people depend on.
What a language model actually is, and what changes when you stop chatting with one and start building on it. Vendor-neutral: tokens, context, sampling, prompts, system instructions, hallucination, and the first evaluation you should write. No prior AI experience assumed.
Turning a prompt that works in a notebook into a feature that holds up in production. Context engineering, tools, skills, retrieval, agent loops, evaluation, injection defence, observability and cost — the practices that make AI systems reliable, whichever provider you use.
Operating AI systems at scale and under adversarial conditions. Evaluation harnesses and their statistics, judges and their biases, multi-agent orchestration, MCP, the threat model, caching, routing, model upgrades, incident response, and the unit economics that decide whether any of it survives.
Learn Python from nothing at all. No programming experience assumed: every concept arrives as the answer to a problem you can feel, from your first line of code to a small program you actually run. Ends with a real command-line tool you wrote yourself.
The Python that shows up in real codebases: environments and packaging, type hints, generators and context managers, decorators, testing and mocking, logging, dates, HTTP, configuration and profiling. Written for someone who can already write Python and now has to maintain it.
Go under the hood and stay there: the object model, bisect, whole-history rewrites, merge drivers and rerere, worktrees, submodules, scaling large repositories, signed commits, and Git inside CI/CD.
Start from nothing and finish with a real repository pushed to GitHub. Every command is introduced by the problem it solves, in plain language, before the syntax — no prior terminal experience assumed.
The everyday craft of Git on a real team: hairy merges, rebase, clean commit history, stash and cherry-pick, branching models and pull requests, releases, reflog rescue, hooks, and the config that pays for itself.