This is personal blog about the human side of engineering. It's about the journey, not the destination.
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.