The interesting breakthrough is not that the model can generate prettier output. It is that it can stay tethered to source material without a human dragging assets into the loop. That changes the cost of correctness.
Most people are still evaluating AI image models like toys. That mistake is expensive. The real shift is not quality, it is throughput.
47 repos. 455 merged PRs. 24 knowledge base docs generated automatically. Documentation doesn't drift when a god of knowledge is watching.
49 services. 7 agents running 24/7. 54 monitors. One dashboard. Here's how I built the cognitive hub that makes running an autonomous AI ecosystem survivable.
The shift from AI-as-assistant to AI-as-agent isn't just a capability upgrade. It's a fundamental reorganization of how engineering teams are structured, how work flows, and what the engineering manager's job actually is.