video1.0<iframe src="https://www.loom.com/embed/4b496c33fd4d4f46822e83db8da236f2" frameborder="0" width="1662" height="1246" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>12461662Loomhttps://www.loom.com12461662https://cdn.loom.com/sessions/thumbnails/4b496c33fd4d4f46822e83db8da236f2-a1665b0247c4c032.gif503.467This Loom explains a solo project, NewDrop, built on PortIO to create repeatable intelligence and a closed-loop SDLC workflow. The author describes how GitHub pushes trigger PortIO webhooks, which use an embedded MCP to ingest code into topics where a review service applies fixes via a core Schema Ancient Harness. The harness follows an Observe, Hypothesize, Act, Observe loop using deep-seek and Bright data for breadth-focused exploration, producing outputs that become recommendations and are fed back into PortIO’s institutional memory. They note schema harness has been outperforming coding models in RKGI at 99% to the benchmark and mention OpenTelemetry and signals as evidence for incident-linked evaluations.