Turn a Quadratic Timeout Into a Reproducible Free-Cloud Performance Lesson

Start with a duplicate-ID finder that compares every pair. At 100,000 records it times out; at 20 records it looks fine. “The optimized version feels faster” is not evidence.

MonkeyCode's official README says its online service is free to start, needs no client download or local setup, provides built-in models, and runs tasks in real server-side cloud environments. The operator confirms that the current launch includes free cloud-server and model access. Eligibility, allowances, and availability may change; verify what your account shows before relying on them.

Sources: MonkeyCode repository and MonkeyCode Online.

Keep correctness beside speed

def slow(xs):
    return sorted({x for i,x in enumerate(xs) if x in xs[i+1:]})

def fast(xs):
    seen, dup = set(), set()
    for x in xs:
        (dup if x in seen else seen).add(x)
    return sorted(dup)

Generate fixed-seed inputs at 100, 1,000, 10,000, and 100,000 rows. Assert slow(sample)==fast(sample) on bounded samples, then benchmark each size five times and report median—not the fastest run. Inject repeated IDs at the beginning and end, all-unique input, and an empty list.

Evidence	Pass
semantics	outputs match property tests
scale	input size and seed recorded
timing	trials and median reported
portability	script runs after clean export

Absolute cloud timing is noisy and is not a MonkeyCode platform benchmark. The useful result is the growth curve and preserved behavior. At what input size does your real workload justify the extra implementation complexity?

Disclosure: I'm a MonkeyCode user sharing my own experience, not affiliated with the project. This account is managed by the same operator as other recent MonkeyCode evaluations; this is not an independent endorsement. Free cloud-server and model availability reflects current operator-confirmed launch information and may change; verify current eligibility and limits in the service.