If you've ever "practiced" system design by redrawing a URL shortener from a blog… same.
It feels productive. It rarely is.
Because in a real interview, nobody claps when your boxes are neat. They ask:
- Why Redis here?
- What breaks at 10× traffic?
- What do you cut under time pressure?
- How do you generate unique IDs without a global lock?
Passive walkthroughs don't train that muscle. You need a loop where you draw, you argue, and something pushes back.
That's why we built System Design Dojo on Codeground.
The loop: draw → argue → survive the follow-up
Every lesson is structured like an actual HLD round:
- Clarify — functional + non-functional (QPS, latency, consistency, cost)
- Sketch — architecture on an in-browser canvas
- Write tradeoffs — APIs, data model, sync vs async, bottlenecks
- Get AI critique — strengths, gaps, alternatives, scores
- Answer one sharp follow-up — the hire / no-hire question
No whiteboard photos. No copy-pasting notes into a generic chatbot. Scenario, canvas, notes, and critique live in one flow.
What a lesson looks like
Example: Design a URL Shortener.
You get interview-shaped constraints, not vibes:
- 100M new URLs / month
- 10k redirects / second peak
- 99.9% redirect availability
- Links that aren't trivially guessable
Left side: brief, hints, design notes.
Right side: architecture canvas (services, DBs, caches, arrows — the usual vocabulary).
Then hit Get AI critique.
The goal isn't "one correct topology." There usually isn't one. The critique rewards structured arguments:
- ID generation (Base62 / hash vs counter)
- Redirect path optimization
- Cache-before-DB on the hot path
- Analytics off the critical path
- Where uniqueness / availability actually breaks
Rubric energy, not buzzword bingo.
What's in the course (~10 hours)
| Module | Scenarios |
|---|---|
| Foundations | Design interview playbook |
| Classic systems | URL shortener, Pastebin, distributed rate limiter |
| Realtime & social | Chat, news feed, video streaming |
| Platform patterns | Notifications, search autocomplete |
| Capstone | Ride sharing |
Each lesson has a rubric focus (caching, ownership, failure modes, etc.) so you're practicing what interviewers actually probe.
Why AI critique helps more than another diagram
Self-study fails for a boring reason: your own architecture never argues with you.
In Dojo, the AI behaves more like a tough interviewer than a cheerleader. You get:
- Strengths
- Gaps
- Alternatives worth defending
- Scores where the argument is thin
- One follow-up question
Playbook-style example:
A cache stampede hits your origin after a deploy — what do you change in the next 30 minutes?
If you can answer that calmly, you're closer to interview-ready than if you memorized someone else's Uber diagram.
Who this is for
- Mid/senior candidates prepping HLD rounds
- Backend / full-stack engineers who code fine but freeze on the whiteboard
- Anyone done watching "let's design X" videos and wanting reps
Try it
Pick a scenario. Sketch a real request path. Write the tradeoffs. Take the critique.
👉 https://codeground.ai/courses/system-design-dojo
Whiteboard energy. Browser canvas. An interviewer that pushes back.
Draw it. Argue it. Survive the follow-up.
Built this on Codeground — if you try a lesson, tell me which scenario felt hardest (rate limiter and ride-sharing usually start fights in the comments 👀).
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