This is personal blog about the human side of engineering. About learning, growing, and sharing experiences along the way. Authored by Yuriy Polyulya.
At the edge, a radio transmission costs 100× more energy than a local computation, and the network may be unreachable for hours. This article builds the formal foundation: how to model contested connectivity with Markov chains, when local autonomy mathematically beats cloud control, and what keeps autonomous control loops stable when they can't phone home.
When the monitoring service is unreachable, anomaly detection has to run on the node being monitored. This article covers on-device detection, gossip health propagation with bounded staleness, Byzantine-tolerant aggregation, and a proxy-observer pattern for legacy hardware — along with a frank note on what happens when you miscalibrate your priors.
Detection is the easy part — acting without making things worse is harder. This article works through the MAPE-K autonomic loop adapted for edge conditions: stability conditions, confidence-gated action thresholds, dependency-ordered recovery to prevent cascades, and a self-throttling law that keeps the loop from consuming the very resources it's trying to protect.
When two clusters reconnect after hours apart, merging their state means choosing between information loss and accepting Byzantine-injected garbage — neither is acceptable. This article covers CRDT merge with HLC timestamps, a reputation-gated admission filter for Byzantine state, and a burst-process divergence model that's more realistic than the usual Poisson assumption.
Resilience returns you to baseline; anti-fragility means coming out better than you went in. This article formalizes that distinction, shows why anti-fragile policies win under fleet-wide policy competition, and builds the bandit and Bayesian update machinery that makes improvement possible — with a caveat: the math only works if you defined success before the failure happened.
Building analytics before you've validated basic survival is a reliable way to fail in an unexpected order. This closing article derives the correct build sequence: a prerequisite graph over the five core capabilities, a constraint surface that shifts priorities based on connectivity, resources, and adversary presence, and the uncomfortable meta-constraint that your autonomic infrastructure competes for the same resources it's trying to protect.
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.
New users arrive with zero history. Algorithms default to what's popular - which on educational platforms means beginner content. An expert sees elementary material three times and leaves. The personalization that retains power users actively repels newcomers. This is the fourth constraint in the sequence.
Users tolerate slow loads. They don't tolerate lost progress. A 16-day streak reset at midnight costs more than 300ms of latency ever could. At 3M DAU, eventual consistency creates 10.7M user-incidents per year, putting $6.5M in annual revenue at risk through the Loss Aversion Multiplier. Client-side resilience with 25x ROI prevents trust destruction that no support ticket can repair. This is the fifth constraint in the sequence.
A synthesis of Theory of Constraints, causal inference, reliability engineering, and second-order cybernetics into a unified methodology for engineering systems under resource constraints. The framework provides formal constraint identification, causal validation protocols, investment thresholds, dependency ordering, and explicit stopping criteria. Unlike existing methodologies, it includes the meta-constraint: the optimization workflow itself competes for the same resources as the system being optimized.
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.