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.

What does an agent's underlying language model retain from one step to the next?

Nothing; it is stateless, and the orchestrator resends the whole transcript each step, The model is stateless and carries nothing between calls. On every step the orchestrator resends the entire transcript, so what the model 'knows' is only what you place in that transcript on that step. This is why what you include and what you leave out are both design choices.

How do working memory and long-term memory differ?

Working memory is the transcript the model sees each step (a cost-and-context problem); long-term memory is facts kept across runs in an external store, retrieved by relevance (a retrieval problem), Working memory is the running transcript: it grows each step, is capped by the context window, and is billed every call. Long-term memory is durable facts held outside the prompt and pulled back by relevance. One is a cost-and-context problem, the other is a retrieval problem, and solving one with the other's tool is a common mistake.

The transcript is over the context budget. Why is truncating the oldest turns the riskiest of the three options?

It silently drops whatever fact lived in the early turns, with no warning, so the agent answers later questions with a guess, Truncation is the cheapest option, no model call, but it deletes early turns blindly. If a needed fact lived there, like an account id given once, it is gone with no signal. Summarizing keeps a lossy gist at the cost of a model call, and relevance-keeping is precise but needs a relevance signal that can still miss a turn that mattered.

Why can compaction quietly remove a safety instruction like 'never delete without confirmation'?

Summarizing is lossy and optimizes for the task, so a short one-time rule looks like old chatter and gets dropped, leaving later steps with no memory of the constraint, A summarizer keeps what looks important to the task and discards what looks like conversation. A safety rule is short, stated once, and easy to mistake for chatter, so it can vanish in the summary. The fix is to treat compaction as lossy on purpose and pin hard constraints outside the compacted transcript, for example in a protected system-prompt block.

Agent Memory and Context: What the Model Sees, and What It Forgets

Name: System Design Masterclass
Availability: InStock

in Agents In Production

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