Notes on engineering leadership, agentic AI, and building platforms at scale.

What harness optimization research teaches us about completion checks, verified repository memory, and improving coding agents through controlled experiments.

A practical guide to inference architecture, KV cache economics, prefix reuse, routing, and operating vLLM and SGLang under production latency targets.

Reliable AI delegation needs more than a capable model. It needs an isolated execution boundary for context, access, workflow, cost, and human escalation.

In 2019, SQLite had over a trillion deployments and analytics had nothing like it. A SIGMOD paper named the gap, DuckDB filled it - and hardware quietly made single-machine analytics the default.

Every token an LLM generates must attend over everything before it. The KV cache is why that doesn't collapse under its own cost - and why GPU memory, not compute, is the real bottleneck in inference.

Slack runs its message store on MySQL at 2ms median latency - because Vitess makes sharding someone else's problem. What that teaches us about scaling databases.

When to reach for a queue, how the ack loop actually works, and how partitioning scales consumption - without creating hot partitions.