Audit every test tier you own against two questions asked separately: can it block a merge, and can it fail. Most teams verify neither, and a suite that misses on either one is decoration. I run this audit across my own agent stack on a schedule, and the run worth
I run a real Kubernetes cluster on a stack of arm64 single-board computers. Not a toy — it hosts my Git server, this blog, and the automation I lean on every day. It's cheap, quiet, and sips power, and I'd build it the same way again. But
I've rebuilt the way my agents talk to language models three times in about three months. Each rewrite solved the previous one's biggest pain and then exposed the next one. The version I run today routes everything through a multi-provider gateway called OmniRoute — but that choice
Six months ago I got tired of introducing myself to my own AI assistant. Every session started the same way: re-explain the architecture, re-list the repos, re-state the decisions I'd already made and the mistakes I'd already learned from. The model was brilliant and amnesiac, and
Every major technological shift begins with a distraction. Early motorists were captivated by engines, not the quiet discipline of the assembly line. Early computer users marveled at silicon, not the operating systems that made it usable. And today, in the early days of AI, we are all fixated on the
Everyone sells the medallion architecture on the same promise: your Silver layer is cleaner than your Bronze. Deduplicated, conformed, typed, joined. That's true, and it's also not the payoff that matters. After building a governed memory store on Databricks — Delta for the raw tier, Postgres for
A few weeks ago I stopped trusting my own traffic graphs. The edge dashboard told me one story — steady visitors, healthy numbers, nothing alarming. So I built a second view that reads the origin's own access logs instead of the edge's summary, and the story fell
I set out to build a memory layer for my agents — somewhere they could write what they learned and read it back later without me hand-feeding context into every prompt. A few weeks in, I looked at the schema I'd drawn and laughed, because I'd reinvented
Most agent-authorization schemes I've seen ask the wrong question. They gate on confidence: is the model sure enough to act? Set a threshold, and if the score clears it, the agent does the thing. It feels rigorous. It's backwards. The axis that should decide whether an
I ran git shortlog -sne on my orchestrator repo last week, the way you do before writing a release note and wanting to thank the right people. I scrolled to the top expecting my own name. It wasn't there. The top committer is forgeai-dev. Second place goes to
I keep two completely different reflexes in my head for the same task, and last week they finally collided. The task is ordinary: an app needs a real database — proper transactions, low-latency reads and writes, not an analytics table playing dress-up. For years my hand went to the same place
I wrote recently about Azure Agent Mesh and OpenSharing — two infrastructure layers that between them cover how enterprises register, discover, share, and execute agents. Between them, they address a lot of the plumbing that has been missing from the enterprise agent stack. But there's a gap neither of