Is this a video course?

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.

How long do I have access?

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?

None. We start from absolute basics like 'What is latency?' and build up to distributed consensus protocols. The Foundation level assumes zero prior knowledge of system design.

How much does the system design course cost?

7.99 US dollars for lifetime access globally, or 499 Indian rupees for lifetime access in India. One-time payment, no subscription, no hidden fees. 11 lessons are free with no signup required.

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.

On a validation set where 80 percent of outputs are genuinely good, a judge that marks every single output PASS will have which scores?

High accuracy but a TNR of 0, because it catches none of the failures, Because 80 percent of outputs are good, always saying PASS is right 80 percent of the time, so accuracy looks fine. But TNR = TN / (TN + FP), and an always-pass judge produces zero true negatives, so its TNR is 0. It catches no failures. This is exactly why accuracy alone is a trap under class imbalance and why you always report TPR and TNR.

A safety judge screens outputs for harmful content. Which metric matters most, and why?

TNR, because a low TNR means harmful outputs get passed through to users, TNR = TN / (TN + FP) is the fraction of truly bad outputs the judge also fails. A low TNR means the judge passes harmful content, which reaches users. That is the dangerous direction for a safety judge, so TNR is the number to defend. Accuracy would hide a low TNR under imbalance, and TPR only measures how the judge treats good outputs.

Two human experts labeling the same set agree at a Cohen's kappa of 0.69. Your tuned judge reaches kappa 0.61 against the human labels. What is the right read?

The judge is excellent, because 0.61 sits just under the human-human ceiling of 0.69, Humans do not perfectly agree with each other, so human-human agreement is the ceiling a judge can reach. At kappa 0.69 for two humans, a judge at 0.61 is close to that ceiling and is trustworthy. Chasing 1.0 would mean forcing agreement on the very cases humans themselves disagree about, which is chasing noise. This mirrors Shopify's reported result of driving a judge from 0.02 to 0.61 against a 0.69 ceiling.

Your judge's TNR came out at 0.75, but its 95 percent confidence interval runs from 0.30 to 0.95. What should you do?

Add more failing examples to the validation set so the interval tightens before you rely on the TNR, A wide interval means the estimate rests on too few cases. TNR is computed only from the failing examples, so if there are just a handful, the interval is huge and the 0.75 is not yet reliable. The fix is to oversample failures so TNR rests on more rows and the interval narrows. Reporting the bare point estimate, or falling back to accuracy, hides the uncertainty rather than reducing it.

Validating Your Judge: TPR, TNR, and the Human Ceiling

Name: System Design Masterclass
Availability: InStock

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