architecture
17 posts found
Your Model Isn’t the Bottleneck. Your Queue Is.
A classifier consumed 0.3% of request time; the queue ate the rest. Why AI deployments fail around the queue, not the model.
AI Agents Don’t Need More Memory. They Need Better State Management.
Memory alone doesn't solve agent problems. Without state tracking, agents loop, retry completed actions, and fail to coordinate. Here's what actually works.
Agents Aren’t Chatbots: Why Distributed Systems Matter
AI agents make API calls, manage state, and fail under load. Treat them like distributed systems, not chatbots. Backoff, idempotency, and structured traces aren…
Why Agent-Driven Code Review Needs Real-Time Observability Hooks
Agent code review tools ship blind without observability into LLM decisions. Learn five steps to instrument your agent pipeline, track filtering decisions, and …
Streaming Mixture-of-Experts Weight Tensors Directly from NVMe Storage
Sparse Mixture-of-Experts models waste massive RAM holding idle expert weights. By combining zero-copy kernel memory mapping, io_uring transfers, and predictive…
The Evolution of Web Application Firewalls: From RegEx Rules to AST Tokenizers and Wasm Filters
A technical timeline of how HTTP inspection evolved from brittle regular expressions and ReDoS traps to lexical tokenizers, WebAssembly filters, and streaming p…
The Evolution of Telemetry: From Syslog Scrapers to eBPF and ClickHouse
A technical timeline tracing how telemetry evolved from isolated Prometheus scrapers in 2018 to OpenTelemetry standards, eBPF in-kernel probing, and ClickHouse …