Taking an AI code-review agent from one pilot repo to 40 services: a per-repo policy file, four rollout gates, and eight weeks of acceptance and defect numbers.

We rewrote one AGENTS.md for coding agents and measured it: agent PR rework fell from 41% to 17%. The five rule types that worked, and the one that did not.

How we cut coding-agent spend per merged PR from $31 to $9 with a per-task token budget, a retry cap and a context-size guard, without lowering merge rate.

An Azure OpenAI on-call runbook: the five alerts allowed to page, the first KQL or APIM check for each, and how MTTR fell from 74 to 23 minutes.

Screenshots of edmx.lamplitlabs.com on a D365 F&O metadata file: upload, trim to three sales entities, explore one entity and convert to OpenAPI JSON.

Five real screenshots of tools.lamplitlabs.com: each tool empty, then after one concrete input, so you can see exactly what changes on screen.

One 2M-record pipeline in C# 13 on .NET 9 and Rust 1.91: each ownership concept mapped from C#, with measured peak RSS, allocations, GC pauses and wall time.

Performance audit of this Jekyll blog from its own build output: 10.8 KB gzipped post HTML, 96% of images sized, 8 images over 300 KB, and what is still open.

Same embedding client in .NET 9, Python 3.12 and Go 1.23 against one text-embedding-3-small deployment: texts/s and p99 at batch sizes 1 to 256.

Azure OpenAI versions retire and auto-upgrade. How we inventory deployments, pin versions in Bicep, gate upgrades on a golden eval set and canary via APIM.