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Adaptive system design practice

System Design FlashcardsBeta

Mini architecture decisions. Adaptive spaced repetition. Master the trade-offs.

Train the decisions, not just the facts

System design is about choosing between trade-offs. Each card drops you into a mini architecture problem between two nodes with a few plausible options โ€” you pick the best wiring, then learn why it wins.

  1. Read the scenario. e.g. "A client pushes 1,000 events/s to a downstream service."
  2. Pick an approach. 2-4 options; one fits the constraints best.
  3. Learn the trade-offs. Every card explains why the winner wins and the rest fail.
  4. Study adaptively. Sessions target your weakest topics, and spaced repetition resurfaces cards before you forget.
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Progressive challenge

Level 1 ยท Foundations

Name the right building block.

Next: build evidence with your first session.

    Concept lab: make a prediction, then see the system respond.

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    Explore ยท choose ยท test

    Concept Lab

    Choose a system-design idea to open its explanation and pressure test on one child page.

    The map of ideas

    What do you want to understand?

    Every concept is available. Larger words reflect connections and study coverage as a discovery hint, not a ranking or a gate.

    Sandbox experiments are prepared in the background.

    Create a Card

    Write your own system design scenario to add to the deck.

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    Choose any topic to practice. Prerequisites show the expected background, not locked content.

    Understand ยท test ยท explain

    Concept Lab

    Read the concept on one side, change a constraint on the other, and keep both views in context. Everything is deterministic and educational โ€” it never connects to infrastructure.

    Side A ยท Understand

    Concept Wiki

    Find the idea, trade-off, or failure mode behind the experiment.

    Side B ยท Test

    Concept Sandbox

    Make a prediction, change one assumption, and observe the modeled result.

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    Level 1

    Choose a situation

    Focus:

    Make your prediction

    Make a prediction before you run the model.

    Change an assumption, choose the outcome you expect, then run the experiment to see the causal chain.

    Model boundary: these outputs are teaching signals based on the assumptions shown below each result, not production capacity recommendations or vendor benchmarks.

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    System Design Flashcards ยท Beta