We ran Octokraft (code health platform) on 24 open source projects. 14 heritage repos like Kubernetes, Django, Terraform and 10 AI-heavy ones like Supabase, cal.com, n8n.
Both groups have issues, but they look different. Heritage repos show accumulated coordination burden: protocol evolution, config hubs, large controllers, years of backfilled responsibilities.
AI-heavy repos show initial over-aggregation: one adapter, one dataloader, one plugin file with too many methods. AI-heavy projects also had about 2x the testing issue density.
1 comment
[ 0.23 ms ] story [ 11.3 ms ] threadBoth groups have issues, but they look different. Heritage repos show accumulated coordination burden: protocol evolution, config hubs, large controllers, years of backfilled responsibilities. AI-heavy repos show initial over-aggregation: one adapter, one dataloader, one plugin file with too many methods. AI-heavy projects also had about 2x the testing issue density.
You can explore the full analysis of all the projects in the showcase at https://app.octokraft.com/showcase/BENCHMA785