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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?

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.

What is a quality SLO for an LLM system?

A quality SLO is a target on the fraction of answers that pass a defined per-request success criterion, for example 92 percent of evaluated answers pass schema, groundedness, and judge checks over 30 days. It replaces uptime as the primary reliability promise because an LLM's characteristic failure is a well-formed response with wrong content, which availability metrics cannot see.

How does an error budget work for probabilistic output?

The budget is the allowed failure fraction implied by the SLO: a 92 percent SLO on 60,000 monthly requests allows 4,800 failed answers. Each answer that fails the success criterion spends one unit, burn rate is spending speed relative to that allowance, and exhausting the budget triggers a pre-agreed consequence such as a freeze on prompt and model changes until the pass rate recovers.

What is a graceful degradation ladder?

An ordered chain of fallbacks where each rung is less capable but more trustworthy than the last: primary model, then a constrained smaller model, then a template or cached answer, then a human handoff or an honest refusal. Validators route failed candidates down the ladder so the user receives a validated answer or a safe outcome, and a confident wrong answer survives only when a validator misses it.

Why is the same pass rate acceptable for one feature and unacceptable for another?

Because the required success rate is set by the cost of one wrong answer times volume, not by the model. A 92 percent pass rate behind a human-reviewed draft costs seconds of reviewer attention per failure, while the same rate wired to irreversible actions executes 80 wrong actions per thousand. Flows differ on reversibility and human oversight, so each flow needs its own SLO.

How do burn-rate alerts apply to LLM quality?

The same multiwindow method as availability SLOs: page on a fast burn, such as 14.4 times the allowance sustained for an hour after a bad prompt deploy, and ticket on a slow burn, around 1 to 2 times sustained for days, which indicates drift. One adjustment: judged SLIs are sampled and delayed, so fast-burn detection leans on all-traffic code validators like schema checks while the judge confirms over hours.

Where should the SLO attach in a multi-step agent flow?

At the user-visible task, never at individual steps, because per-step reliability compounds multiplicatively: five steps at 92 percent deliver about 66 percent task success. Translate the task SLO inward to per-step budgets with p equals T to the power one over n, and if the required per-step rate is unreachable, shrink the number of probabilistic steps with deterministic scaffolding rather than chasing model accuracy.

An LLM feature shows 99.97 percent availability, normal latency, and near-zero error rate, yet users increasingly receive fluent answers citing policies that do not exist. What is the correct diagnosis?

The failures are semantic, invisible to status-code SLIs; the system needs a quality SLI fed by validators and a judge, The infrastructure metrics are telling the truth about what they measure: requests are being served, quickly, without transport errors. The failure lives in the content, which no status code carries. The fix is a new counted event, a per-request semantic pass or fail from schema checks, groundedness gates, and a validated judge, which becomes the SLI a quality SLO and error budget run against.

A team runs a 92 percent quality SLO on 2,000 requests a day (4,800 failures allowed per 30 days). After a prompt change, daily pass drops from 93 to 88.5 percent. What follows?

Daily burn rises from 140 to 230 failures, a 1.44x burn rate, and the budget exhausts around day 25, days after a slow-burn alert should have flagged it, The SLO is a window-level promise, so one bad day neither violates it nor is ignorable: it spends budget. At 88.5 percent, failures run at 230 a day against a 160 allowance, a 1.44x burn that exhausts the 4,800 allowance around day 25 of 30. A multiwindow burn alert catches the sustained overspend within days, which is the entire point of alerting on burn rate rather than on the raw daily number.

In the ladder computation (primary passes 92 percent, fallback 80 percent, validators catch 85 percent of wrong answers, deterministic floor), what did the ladder chiefly buy?

It cut confident wrong answers reaching users from 8 percent to about 1.4 percent, converting most failures into rescues or honest fallbacks, Correct answers rose only modestly, 92 to 97.4 percent, because the fallback rescues just over half of what the primary misses. The dramatic effect is on the trust-destroying outcome: wrong answers reaching users fell nearly six-fold, from 8 to 1.4 percent, because caught failures become rescues or honest fallbacks. And since user-facing wrongness scales with the validator miss rate, the catch rate, not the primary model, is usually the cheaper lever to improve.

A flow that auto-executes refunds needs roughly 99.9 percent reliability, but its single LLM decision step measures 95 percent. What does this lesson prescribe?

Change the blast radius: put a deterministic action gate or human approval between the model's recommendation and the irreversible action, The required reliability exceeds what a raw stochastic step can offer, and the lesson's answer is architectural: move the flow to a cheaper cell of the blast-radius matrix. A deterministic policy check or an approval step means the model only recommends while code or a human arbitrates, so the flow's effective reliability no longer rests on the model alone. Buying the same risk reduction as raw model accuracy is a research project; changing the blast radius is an engineering task.

Reliability for Stochastic Systems: Error Budgets for Probabilistic Output

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