Question 1

How much does zuzu.codes cost?

Accepted Answer

The starter track is free — read all lessons and practice for free. Full access to every track (current and future) is $14.99/month. Cancel anytime.

Question 2

How long does each track take?

Accepted Answer

Each track is designed as a 30-day challenge — one lesson per day, about 15 minutes each. Go at your own pace, but the structure is built around daily consistency.

Question 3

What's the lesson format?

Accepted Answer

Each lesson is a student-teacher dialogue with code examples, followed by a hands-on code challenge in an in-browser editor. You read, you understand, then you write real code.

Question 4

Do I need prior coding experience?

Accepted Answer

Our beginner track starts from absolute zero — no prior experience needed. Advanced tracks build on earlier ones, and the platform tells you exactly where to start.

Question 5

How is zuzu.codes different from freeCodeCamp or Codecademy?

Accepted Answer

zuzu.codes uses a structured 30-day track format with dialogue-based teaching, an in-browser code editor, and gamification (XP, streaks, progress tracking). The format builds genuine understanding through daily practice.

Lesson	Concept
1	LLM mental model — next-token prediction, hallucination, sampling
2	`Agent(model).run_sync(prompt).output` — the call shape
3	Prompt specificity — constraints shape the output
4	Iterating on prompts — change one thing at a time
5	Summarize — reduce text to essence
6	Infer — pick a label from a closed set

Lesson	Concept
1	LLM mental model — next-token prediction, hallucination, sampling
2	`Agent(model).run_sync(prompt).output` — the call shape
3	Prompt specificity — constraints shape the output
4	Iterating on prompts — change one thing at a time
5	Summarize — reduce text to essence
6	Infer — pick a label from a closed set

Lesson	Concept
1	LLM mental model — next-token prediction, hallucination, sampling
2	`Agent(model).run_sync(prompt).output` — the call shape
3	Prompt specificity — constraints shape the output
4	Iterating on prompts — change one thing at a time
5	Summarize — reduce text to essence
6	Infer — pick a label from a closed set

Lesson	Concept
1	LLM mental model — next-token prediction, hallucination, sampling
2	`Agent(model).run_sync(prompt).output` — the call shape
3	Prompt specificity — constraints shape the output
4	Iterating on prompts — change one thing at a time
5	Summarize — reduce text to essence
6	Infer — pick a label from a closed set

What week 1 covered

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What week 1 covered

What week 1 covered

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What week 1 covered