OpenCode is a curated platform for open-ended programming problems. We explore system design, architecture trade-offs, defensive concurrency, and pragmatic software engineering.
Why Traditional LeetCode Fails Senior Engineers
LeetCode tests rote puzzle memorization. Open-ended coding measures what actually matters on day-to-day production systems.
Real customer tickets rarely have strict constraints. Great engineers ask probing questions, identify unstated assumptions, and design adaptable interfaces.
Every technical choice has consequences. Latency vs. consistency, memory footprint vs. query speed, simplicity vs. extreme throughput.
Network partitions happen. Redis restarts. Webhooks duplicate. Open-ended problems test how defensively and reliably you handle partial failure.
Featured Open Challenges
Each challenge presents realistic scenario requirements, deliberately omitted edge constraints, and invites candidates to document design decisions.
Design and implement a multi-tenant sliding window rate limiter that handles 100k requests/sec. You must gracefully handle Redis failover by falling back to in-memory token buckets without crashing upstream services.
Architect an event dispatch engine guaranteeing at-least-once delivery to 3rd-party endpoints with arbitrary response delays. Candidate must model jittered exponential backoffs, idempotency tokens, and manual dead-letter queue recovery.
Given a buggy legacy payment ledger with subtle race conditions, memory leaks under peak load, and locking order deadlocks, identify the root cause and refactor it into an atomic, auditable double-entry accounting engine.
Build a secure micro-sandbox capable of executing untrusted user-submitted Python/JavaScript scripts with strict cgroups memory caps, CPU quotas, process isolation, and network egress firewalling.
What An Open Challenge Looks Like
Notice how the prompt gives room for architectural creativity rather than restricting you to a single rigid method signature.
Your service protects an downstream billing API with an allowance of 10,000 requests per minute per tenant.
type RateLimiter interface {
// Allow evaluates whether a given request should be permitted.
// Must return latency, remaining quota, and retry-after header if blocked.
Allow(ctx context.Context, tenantID string, tokens int64) (Result, error)
// HealthCheck inspects downstream cluster state & falls back automatically.
HealthCheck(ctx context.Context) ComponentStatus
}The 4-Pillar Evaluation Rubric
How open-ended solutions are reviewed and graded by our engineering panel.
Did you identify hidden constraints? Do you handle cold-starts, clock drifts, and boundary conditions?
Can you articulate why you chose an algorithm or datastore? Are the memory and CPU trade-offs justified?
Readable, idiomatic, easily testable code. Minimal coupling, strong separation of concerns, zero clutter.
Structured metrics, graceful degradation, health probes, and clear mitigation paths when third parties fail.
Whether you are designing custom technical assessment tracks or submitting your take on our open problems, get in touch directly.