I build real-time distributed systems using AI pipelines. I write about what worked, what didn't, and the numbers behind both.
Throughput doubled. Change-failure rate doubled with it. Both are in the posts below, with the caveats and the counts behind them.
An agent optimises the ticket in front of it and cannot see the fix fighting the wider architecture. Design intent is the only thing that rejects a narrow local fix, and where you put it decides whether it works.
The last post blamed our architecture records for confusing the agent. That was the wrong way round. The agent was reading an ambiguity we had written in ourselves, and the reason we wrote it in was that our review rules were never in the repository.
An outage left days of video sitting in storage, processed by nothing. None of it was lost. Getting it processed took two weeks, because every stage in the pipeline stamped its own arrival time and called that the truth.
What changes when an agent becomes the daily driver. Throughput, cycle time and change-failure rate, with their caveats.
Real-time quality analysis of video streams. Event-time semantics, the consistency boundary, the bugs that only show up in production.
Build versus buy, instrument before you roll out, when to defer. The decisions that never make it into a postmortem but should.
Terraform at a startup, schema evolution, and the database choice you regret six months later.
Most teams have adoption numbers, seats bought and suggestions accepted, and nothing on whether anything shipped sooner or broke less. I answer that from your own Git and CI data. Read-only, five days, fixed price.