Stories from the Factory Floor: Running my baseball side project on an AI software factory
Blog post from LaunchDarkly
A LaunchDarkly engineer describes using an internal AI software factory to build and operate a personal AI baseball analytics application, testing how automated feature-flag workflows affect software delivery. The Docker-based application combines baseball datasets with AI models to support chat, reports, player analysis, game replay, and pitch-sequencing recommendations across local, home-server, and public cloud deployments. Its roughly 50 feature flags govern feature availability, destructive operations, authentication, access controls, runtime model prompts, and interface layouts, while the factory automatically creates flag wiring, metrics, tests, and in-app status information during pull requests. The approach enabled a rapid rollback after a redesigned interface caused a blank-page bug and reduced risks around bulk deletion, public access, and authentication rollout. The author also notes operational challenges, including remembering to activate dark-by-default flags after deployment, ensuring clients support both enabled and disabled paths, and resolving mismatches between configured flags and code integration.
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