Read the Constraints First: Using LeetCode's Limits and Custom Test Runner to Pick the Right Algorithm

Most LeetCode frustration comes from a backwards workflow: write a solution, submit, get Time Limit Exceeded, then guess at fixes. A faster loop exists, and it starts before you write any code: the problem's stated constraints tell you which complexity classes are viable, and the Run Code feature lets you debug edge cases without touching your submission record.

The thesis is simple: treat constraints as a specification, not fine print. They are the judge telling you the performance envelope up front.

Constraints are a complexity budget

Every problem lists input bounds such as 1 <= n <= 10^5. These numbers are deliberate. Judges typically allow on the order of 10^7–10^8 simple operations per test run, so you can map n directly to acceptable complexity:

Max n	Usually safe	Usually too slow
10^4	O(n²)	O(n³)
10^5	O(n log n)	O(n²)
10^6	O(n) or O(n log n)	O(n²)
20–25	O(2^n) backtracking	O(n!)

So if a problem gives you n <= 10^5 and asks for pair sums, the brute-force double loop is off the table before you start. That immediately points you toward a hash map (O(n)) or sort-plus-two-pointers (O(n log n)). You have narrowed the design space from "anything" to "one or two known patterns" by reading one line.

Run Code is your free debugging pass

LeetCode's Run Code button executes your solution against test cases you supply, and it does not count as a submission. That makes it the right place to break things deliberately. The hidden judge suite is much larger than the visible samples, so passing the samples proves almost nothing.

A practical custom-case checklist before submitting:

1. Minimum input: empty array, single element, or whatever the smallest constraint allows.
2. Boundary values: the largest/smallest permitted values (overflow and off-by-one live here).
3. Adversarial shape: all duplicates, already sorted, reverse sorted — whichever stresses your logic.
4. Maximum size: generate an input near the constraint ceiling to smoke-test performance before the judge does.

For example, on a two-sum style problem with n <= 10^4 and values down to -10^9, a useful custom case is [-10^9, 10^9, 3] with a target that forces the negative value to participate. It catches solutions that assumed non-negative numbers — a bug the friendly samples rarely reveal.

Read the verdict, not just the failure

Each verdict type points at a different failure class, and treating them interchangeably wastes iterations:

• Wrong Answer: logic bug or missed edge case. Go back to Run Code with a targeted custom input.
• Time Limit Exceeded: almost always an asymptotic problem, not a constant-factor one. Micro-optimizing an O(n²) solution rarely rescues it at n = 10^5; you need a different algorithm. Check the constraints again.
• Runtime Error: index out of bounds, null dereference, or overflow — usually on an edge case the samples skipped.
• Memory Limit Exceeded: your auxiliary structure scales too aggressively; look for an in-place or streaming alternative.

The habit worth building: when you see TLE, don't profile — recount. Derive your solution's complexity, compare it against the budget from the constraints table, and change approach if it doesn't fit.

The trade-off: judge-optimized isn't production-optimized

Two caveats keep this workflow honest. First, the runtime and memory percentile graphs shown after acceptance are noisy — identical code resubmitted can land in noticeably different percentiles because the judge runs on shared infrastructure. Treat them as a rough sanity signal, not a benchmark. Second, the tricks that win on LeetCode (dense bit-hacks, terse one-liners, precomputed tables) are often the opposite of what you would ship in production code, where readability and maintainability dominate. Use the platform to train algorithmic judgment, not as a style guide.

Try it on your next problem

Pick any medium problem. Before coding, write down the complexity you predict is required based on the constraints. Implement the simplest approach that fits that budget, run your three or four custom edge cases with Run Code, then submit once. If you want to calibrate your intuition, deliberately submit the brute-force version first and watch whether it TLEs — that single experiment will make the constraints table above feel concrete instead of theoretical.