This is personal blog about the human side of engineering. About learning, growing, and sharing experiences along the way. Authored by Yuriy Polyulya.
Users abandon before experiencing content quality. No amount of supply-side optimization matters. Latency kills demand and gates every downstream constraint. Analysis based on Duolingo's business model and scale trajectory.
Once latency is validated as the demand constraint, protocol choice determines the physics floor. This is the second constraint - and it's a one-time decision with 3-year lock-in.
While demand-side latency is being solved, supply infrastructure must be prepared. Fast delivery of nothing is still nothing. GPU quotas—not GPU speed—determine whether creators wait 30 seconds or 3 hours. This is the third constraint in the sequence—invest in it now so it doesn't become a bottleneck when protocol migration completes.
Building the architectural foundation for ad platforms serving 1M+ QPS with 150ms P95 latency. Deep dive into requirements analysis, latency budgeting across critical paths, resilience through graceful degradation, and P99 tail latency defense using low-pause GC technology.
Implementing the dual-source architecture that generates 30-48% more revenue by parallelizing internal ML-scored inventory (65ms) with external RTB auctions (100ms). Deep dive into OpenRTB protocol implementation, GBDT-based CTR prediction, feature engineering, and timeout handling strategies at 1M+ QPS.
Building the data layer that enables 1M+ QPS with sub-10ms reads through L1/L2 cache hierarchy achieving 85% hit rate. Deep dive into eCPM-based auction mechanisms for fair price comparison across CPM/CPC/CPA models, and distributed budget pacing using Redis atomic counters with proven ≤1% overspend guarantee.
Taking ad platforms from design to production at scale. Deep dive into pattern-based fraud detection (20-30% bot filtering), active-active multi-region deployment with 2-5min failover, zero-downtime schema evolution, clock synchronization for financial ledgers, observability with error budgets, zero-trust security, and chaos engineering validation.
Series capstone: complete technology stack with decision rationale. Why each choice matters (Java 21 + ZGC for GC pauses, CockroachDB for cost efficiency, Linkerd for latency). Includes cluster sizing, configuration patterns, system integration, and implementation roadmap. Validates all requirements met. Reference architecture for 1M+ QPS real-time ads platforms.
How to engineer resilient decision-making in multi-agent AI systems. Explores weighted voting, robust aggregation, and governance architectures with mathematical frameworks and practical implementation ideas.
How engineers can develop frameworks for decision-making that become stronger when LLM systems fail, building cognitive resilience through adversarial thinking and dynamic trust calibration.
As AI transitions into a cognitive partner role, here are insights on the evolving engineering mindset.
Based on the assumption that mindset exists and can be defined.