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Insights on AI infrastructure, Decision Coherence, and building systems for the machine-driven era.
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70 articles
LLM Orchestration: How Frameworks Coordinate Control Flow Across Multiple LLM Instances
LLM orchestration frameworks — LangGraph, CrewAI, OpenAI Agents SDK, LangChain — coordinate which agent runs next and how handoffs happen. They do not coordinate the shared state every agent reads and writes. Production multi-agent failures are usually state-coherence failures, not workflow failures, and the orchestrator can’t catch them.
Alex Kimball|May 1, 2026
Postgres Materialized Views: Create, Refresh, and Optimize
How to create, refresh, and optimize a Postgres materialized view — plus the structural limit every team hits when reads need fresh derived state under concurrency.
Alex Kimball|Apr 29, 2026![Stateful Stream Processing for Decisions: Where Flink Stops Being Enough](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1 1"%3E%3C/svg%3E)
Stateful Stream Processing for Decisions: Where Flink Stops Being Enough
Flink gives you stateful stream processing. It does not give you a decision-coherent serving layer. The gap is what teams discover when they put Redis or Postgres in front of Flink to serve decisions — and hit the same split-state problem Flink was supposed to have solved.
Xiaowei Jiang|Apr 24, 2026Real-Time ML: Architecture, Feature Freshness, and Where ML Models Make Bad Decisions
Real-time ML — the architecture that runs ML models against live requests for instant decisions — is bottlenecked by feature freshness, not model latency. The model serves in 8 milliseconds; the features it scored are 40 seconds old. For real-time machine learning systems committing against fresh state, the freshness budget is the binding constraint, and most stacks never measure it.
Xiaowei Jiang|Apr 24, 2026Real-Time Fraud Detection Architecture: Where Coherence Breaks
Fraud detection architectures converge on the same canonical stack — Kafka → Flink → feature store → model serving → rules engine — and fail at three predictable seams under concurrent load: velocity counter staleness, feature-store / rules-engine divergence, and cross-channel retrieval gap. Sub-50ms p99 on each component doesn’t fix any of these.
Xiaowei Jiang|Apr 23, 2026Real-Time Credit Decisioning Architecture
Real-time credit decisioning is not batch underwriting with a faster SLA. Every transaction reads three derived signals — exposure, velocity, and risk — from separate pipelines that drift under concurrent load. The composite a decision reads is a chimera, correct only in the sense that each part was correct against its own snapshot.
Xiaowei Jiang|Apr 23, 2026Ready to get started?
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