Multi-Model

TL;DR Before I merge anything where being wrong would be silent, I paste it into a live Gemini session and ask that model to attack it. Not “review this,” which gets you a rubber stamp with bullet points. Attack it. Over four days in April, that gate killed a performance optimization 88 minutes after it shipped, found two engine bugs that meant every historical number in my trading leaderboard was inflated, and identified five separate ways my “out-of-sample” evaluator was reading the future. Claude wrote all of that code. Claude also reviewed all of that code. Claude caught none of it. The useful property is not that Gemini is smarter, it is that Gemini did not write the thing and has no investment in the design being correct. ...

TL;DR The Jellyfin HA conversion touches a .NET 10 codebase, Entity Framework Core migrations, Kubernetes manifests, Terraform infrastructure, PostgreSQL operations, and FFmpeg transcoding pipelines. No single AI model understands all of this equally well. So I used four of them — the same multi-model planning pattern that shipped dnd-multi in a single day and that I documented in the LLM GitHub PR workflow. This post covers how I adapted that pattern for infrastructure work, what each model caught, and why planning is where all the human time should go. ...