trace
AI-generated problems · human-proof grading

Your code passes. Does your thinking?

Trace writes the problem, checks your output against real test cases, then grades the way you solved it — the reasoning, the complexity, the tradeoffs you missed. Stuck? Hints unlock one step at a time.

AI learning session

Start with what you want to learn

A conversation, not a complicated setup.

  • You

    I want to get better at hash maps.

  • AI mentor

    What feels hardest right now?

  • You

    Knowing which pattern to use.

Your mentor is shaping the next question
The loop

Three steps, every single time

No dashboards to configure. No curated lists to grind through. The problem exists only because you showed up to solve it.

  1. 01

    Problem is written for you

    Tell Trace what you want to get better at — graphs, DP, concurrency — and it drafts a problem with constraints, examples and a hidden test suite.

  2. 02

    You solve it, not guess it

    Write the solution in a real editor. TypeScript and Python run today, and every result is checked against generated test cases.

  3. 03

    Your approach gets graded

    Then the interesting part: AI reads your code and explains what your solution reveals about your thinking, and what to fix next.

Two judges

Output is checked by tests. Approach is checked by AI.

We split grading on purpose. Machine-checkable results stay deterministic — no hallucinated pass, no generous judge. The parts a test cannot see are where the AI earns its place.

Value checking

Deterministic. 14 cases, 5 visible and 9 hidden, exact output comparison, time and memory limits.

AI review

Reads your code for complexity, edge cases, naming and the tradeoffs you skipped.

Value check · 14 / 14 pass0.8ms · 44.1MB
#01[2,7,11,15], 9[0,1]
#02[3,2,4], 6[1,2]
#03[3,3], 6[0,1]
#04[], 0[]
#05[-1,-2,-3], -5[1,2]
+ 9 hidden casescorrectness: verified
Hint ladder

Nudges that keep you thinking

Hints never hand you the code. Each one is a single step forward — a question, a constraint to re-read, a pattern to recognise — generated from your own attempt.

Hints · 2 of 5 used−0.2 score each
  1. 1

    You are re-scanning the array for every element. What would let you answer “have I seen this value?” without looking again?

  2. 2

    A hash map turns a second loop into a constant-time lookup. Which value should be the key, and which the value?

  3. 3

    Your map stores values seen so far. At each index, the partner you need is `target − nums[i]`. Check before you insert.

  4. 4

    Edge case: duplicate values such as [3,3]. Does your check run before or after the insert?

    locked · unlock with a click
  5. 5

    Full walkthrough with the optimal solution and reasoning

    locked · unlock with a click

Structured, not spoon-fed

Five rungs from “re-read the constraint” to a full walkthrough. You choose when to stop climbing.

Scored transparently

Every hint you open is recorded in your review, so a perfect score means you got there unaided.

Learns your gaps

Hints cluster around the patterns you keep missing — sliding window, monotonic stacks, off-by-one bounds.

Start free · no card

Solve one problem tonight. Read what it says about you.

Five AI-generated problems a week, unlimited AI review, and the full hint ladder. Upgrade only when you want deeper roadmaps.