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No. This is an interactive, slide-based learning platform. Each lesson has rich text, animated diagrams, live code editors, and quizzes. You learn by reading, interacting, and doing, not by watching videos passively.

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Forever. Both pricing tiers are one-time payments with lifetime access. This includes all current 766 lessons and any future content we add.

What level of experience do I need?

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What technologies are covered?

Everything from DNS and load balancers to Kubernetes, Kafka, distributed databases, consensus protocols, stream processing, security architecture, and observability. We cover principles and real-world implementations used at Netflix, Google, Amazon, Uber, Stripe, and more.

Is this useful for system design interview preparation?

Yes. The lessons are structured around the exact topics asked in system design interviews at FAANG and top-tier companies. Interactive diagrams help you practice whiteboard-style explanations. Covers everything from URL shortener design to distributed payment systems.

How is this different from ByteByteGo or Educative?

766 interactive lessons (4x more than most competitors), 16 different diagram types that build step by step, real production examples from Netflix, Google, Amazon, Uber, and Stripe, and lifetime access for a one-time payment of $7.99 instead of annual subscriptions costing 100 to 200 dollars per year.

What is a golden dataset?

A golden dataset is a curated set of inputs, each paired with a known-good answer, that you run before and after every change to check the answers still match. It is the answer key for your AI system, hand-chosen so every case is there for a reason.

Why use a stratified golden set instead of a random sample of traffic?

Because production traffic is mostly the happy path, so a failure mode that hits 1 percent of requests gets only a case or two in a random sample and a regression there barely moves the average. A stratified set fixes the number of cases per failure mode on purpose, so a break in any mode is loud.

How big should a golden dataset be?

A few dozen to a few hundred well-chosen cases beat thousands of random ones, because every case needs a correct expected answer a human stands behind. Coverage of your failure modes rises fast then flattens while answer-key trust falls as the set grows, so aim for the range where both are still high and treat size as a side effect of coverage.

How do you catch a regression when you change a prompt or swap a model?

You run the golden set before and after the change and diff the results case by case, instead of watching the overall average. A flat average can hide equal numbers of fixes and regressions, which is exactly how Anthropic shipped a Claude Code regression that its standard suite never caught.

Can you use synthetic data in a golden dataset?

Yes, when a real failure mode is too rare to fill with real traces, but a human must review every synthetic case. If a model generates both the inputs and the expected answers, it bakes its own blind spots into the answer key and you end up testing the model against its own weaknesses.

Why must golden cases never appear in training data or few-shot examples?

Because the model would then be tested on material it has already seen, so it passes by memorization and the score stops measuring real quality. This is the same reason machine learning keeps a test set walled off from the training set.

Why is a stratified golden set better than a random sample of production traffic for catching regressions?

Production traffic is mostly the happy path, so a rare failure mode gets only a case or two in a random sample, and a regression there moves the overall score too little to notice, A failure mode that hits 1 percent of requests shows up in about 1 percent of a random sample, so a real break there barely moves the average and hides in the noise. A stratified set fixes the number of cases per failure mode on purpose, so a regression in any mode is loud. The cost is that you must do error analysis first to know the modes.

You change a prompt. The overall golden-set pass rate is unchanged at 75 percent before and after. What should you conclude?

Nothing yet. A flat overall rate can hide equal numbers of fixes and regressions, so you must diff the results case by case to see which specific cases broke, A flat average is exactly the trap. The change can fix some cases and break an equal number of others, leaving the aggregate unmoved while real regressions ship. This is the mechanism behind Anthropic's postmortem, where the standard suite showed no regression but a specific eval dropped 3 percent. You have to diff per case, not watch the single number.

When is it acceptable to use synthetic inputs in a golden dataset, and what is the required safeguard?

When a real failure mode is too rare to fill with real cases, but a human must review each synthetic case so you do not bake the model's own blind spots into the answer key, Synthetic data is a legitimate way to give a rare but important failure mode enough weight to be measurable. The safeguard is human review of every synthetic case, because a model that writes both the test input and the expected answer will produce a key that matches its own mistakes, and you end up testing the model against its own weaknesses and passing it.

Why must golden cases never appear in training data or as few-shot examples in the prompt?

Because the model would then be tested on material it has already seen, so it passes by memorization and the score no longer measures real quality, This is the same reason machine learning keeps a test set separate from the training set. If a golden case leaks into training data or into the prompt as a few-shot example, the model has effectively seen the answer, so it passes by recall and the eval measures memorization rather than quality. Keep the golden set walled off and used only to judge a system that has not been shown the answers.

Building a Golden Dataset: The Set That Catches Regressions

Name: System Design Masterclass
Availability: InStock

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