qodebase
Algorithms, past the whiteboard.
Engineering editorials that take data-structure theory all the way to production. Written for junior and mid-level devs ready to think like seniors.
Why this matters now
Algorithm knowledge isn’t the moat anymore.
AI coding agents can recall every data structure and produce a working solution in seconds. What they can’t do is decide what to build, weigh the trade-offs, and own the system once it’s in production.
In the agent era, the engineers who pull ahead are the ones with product judgment — the instinct for requirements, scale, latency, and cost that turns a correct answer into the right one. That is what will separate the 10x developers from everyone shipping the same generated code.
qodebase trains exactly that: every unit starts from a real product problem and ends at a system that holds up at scale.
Product thinking, not puzzle reflexes
Every problem starts from a real product requirement and ends at a system that ships — the questions seniors actually get asked.
Real data, real benchmarks
We run algorithms on real road networks and datasets, measure p99s, and show where the textbook answer falls apart at scale.
Beyond LeetCode
Stop optimizing for the interview trick. Learn the trade-offs, infrastructure, and design calls that separate mid-level from senior.
Uber track
Build a ride-hailing platform, one system at a time
From fastest-route queries to dispatch, ETA prediction, and pricing — the algorithmic spine of ride-hailing at scale.
- 01Route Finding & ETA BackboneBuild the fastest-route and ETA engine behind every ride request.OSMnx, OSRM
- 02Nearest DriversGeo indexing, ETA ranking, matrix queriesH3 / OSRM Table
- 03Dispatch & Ride MatchingWaterfall, batching, timeouts, state machinesRedis, Kafka
- 04Pickup & Trip ETA PredictionSegment travel time, live updates, feedback loopsOSRM durations
- 05Fare Estimation & PricingBase fare, surge, zone aggregation, smoothingPricing service
- 06Advanced MarketplaceGlobal matching, pooling, dynamic pricing controlOptimization / RL
Stripe track
Build a payments platform, one system at a time
From idempotent payments to ledgers, webhooks, rate limiting, fraud scoring, and distributed sagas — the systems spine of a payments platform.
- 01PaymentIntent & IdempotencyExactly-once charges under retries and concurrency.Redis, Postgres
- 02Double-Entry Ledger & ReconciliationRecord every cent twice; prove the books always balance.Postgres, WAL
- 03Webhooks & Reliable DeliveryAt-least-once delivery, backoff, signatures, dead letters.Queues, HMAC
- 04Rate LimitingFixed window, sliding window, and token buckets at scale.Redis, Lua
- 05Fraud Detection & Risk ScoringFeature engineering and tree models under 50ms.scikit-learn
- 06Distributed Transactions & SagasCoordinate payment, ledger, and webhooks without 2PC.Orchestration