AI writes the code. Can you tell if it's right?

The gap between senior engineers and prompt-typers isn't who writes better code — it's who can diagnose what breaks, reason about scale, and know when something is actually production-ready.

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Scenario · Intermediate · 45 min

The Slow Orders Endpoint

Python / Django

# before.py — what the LLM generated

def get_orders(user_id):

  orders = Order.objects.filter(user=user_id)

  result = []

  for o in orders:

    result.append(o.item.name)# ← why is this slow?

  return result

Viveka mentor

“How many database queries does this loop execute for 100 orders?”

Why now

The easy part of software changed first.

AI made code generation cheap. A developer can produce a service, test, component, or migration before lunch. That is useful — but it also makes surface-level output easier to fake.

What has not changed is the work behind senior judgment: noticing the edge case, reading the data flow, seeing the scale failure before production, and deciding whether a fix is safe enough to ship.

Viveka exists for that gap: the space between “the code runs” and “the engineer understands why it is correct.”

How it works

A learning loop for engineering judgment.

Every scenario moves through the same disciplined loop: understand the system, solve the defect, debug the behavior, improve the design, then explain the reasoning.

1

Understand

You're dropped into a real codebase with a real bug report. No hints. Read the code, understand the system.

2

Solve

Fix it in a browser IDE. Tests give you signal; they don't tell you why. That's on you.

3

Debug

Your mentor asks questions — never answers. "What does this variable hold after the second iteration?" You think out loud.

4

Improve

Tests pass. Now make it production-ready. The LLM mentor keeps asking until your reasoning is airtight.

5

Explain

Three questions: root cause, scale, production readiness. Your answers are scored on reasoning depth, not keywords.

What you earn

Not a badge. A verified record of your reasoning.

Tests prove the fix works once. The artifact shows what you understood: root cause, scale behavior, production readiness, and the concepts you covered or missed.

Share it with recruiters, managers, mentors, or your future self — as evidence of engineering judgment, not just completion.

Verified Artifact

The Slow Orders Endpoint

Verified by Viveka

Performance

51 → 1

database queries

Reasoning

8.4/10

depth score

Concepts

N+1 patternLinear scaling

“The endpoint was making one additional query per order. It passed with small test data, but production volume would make latency grow linearly with every returned order.”

Early signals

Built around the moments engineers remember.

Closer to a production war room than any learning platform I've seen.

Senior Backend Engineer · 13 years experience

Tests whether you think small picture first or big picture first. Interviews never do that.

Tech Lead · 9 years experience

This might be how engineering interviews look in the future.

Software Engineer · 5 years experience

No other platform encouraged this level of thinking.

Software Engineer · 1 year experience

Made me stop, think, then build. In that order.

Software Engineer · 3 years experience

The code is just the beginning. The three questions at the end — that's the real test.

Tech Lead · 9 years experience

Join engineers building the judgment AI can't replicate.

Practice with real scenarios. Learn from a mentor that refuses to spoon-feed. Leave with proof that your reasoning holds up.

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