Practice · Spaced repetition

Flashcards.

Active recall on the ideas that matter — scheduled by an SM-2 algorithm so each card comes back right before you'd forget it. Grade yourself; the harder ones return sooner. Progress saves in your browser.

The Agent Loop & Harness

Plan-act-observe, tool calling, context management — the runtime behind every AI agent.

10 cardsStudy →

Transformers & Attention

Tokens, attention, and the architecture under every modern LLM.

9 cardsStudy →

System Design Foundations

Caching, scaling, consistency — the load-bearing ideas of every large system.

9 cardsStudy →

LLM Engineering

Decoding, RAG, evals, and cost — building reliable products on top of models.

9 cardsStudy →

How it works: each card gets an ease score. Rate a card Again / Hard / Good / Easy and the scheduler picks when you'll see it next — failed cards return tomorrow, easy ones drift weeks out. Come back daily and the deck teaches itself. Pairs with your daily streak.