Notes on backends, AI automation, microservices, and real-time systems from the team building them.
Go vs Rust vs Node.js for high-throughput APIs: what decides it above 10k req/sec (p99 latency, memory, GC, hiring) and a load-test harness to settle it on your own workload.
Modular monolith vs microservices: a decision framework with the real costs, the signals that justify extracting a service, and the hybrid most teams should run.
Kafka vs NATS JetStream for Go event pipelines: retention and replay, ordering, delivery semantics, and ops burden, with Go consumer code for both and a decision table.
Build an MCP server in Go with the official SDK: typed tools, Streamable HTTP, bearer-token auth, tenant scoping, and the checklist to run before agents touch real data.
Why AI agents fail in production, and the engineering patterns that fix them: deterministic workflows, confidence thresholds, human-in-the-loop review, idempotency, and tracing.
A production RAG architecture: evaluate retrieval first, then hybrid search in PostgreSQL (pgvector + full-text + RRF), reranking, and adaptive routing, with the SQL shown.
Reduce LLM costs without hurting answer quality: cache repeated prompts, route easy requests to smaller models, cap output, and measure each saving in Go.
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