Reducing our monorepo size to improve developer velocity

Here's a concise summary of the blog post in 2-3 sentences: Dropbox's immutable blob store, Magic Pocket, faced a significant increase in storage overhead due to increased fragmentation and under-filled storage volumes after a new service rollout. The existing compaction strategy couldn't effectively reclaim space from the long tail of severely under-filled volumes, highlighting a limitation in the steady-state approach. Dropbox developed a multi-strategy approach to drive overhead back down, combining compaction, garbage collection, and efficient redundancy to achieve better storage efficiency and control at exabyte scale.

Dropbox's engineering team optimized Dash's relevance judge using DSPy, a framework for systematically optimizing prompts against a measurable objective. To adapt their existing judge for a lower-cost model, they defined a clear objective (minimizing disagreement with human relevance judgments while ensuring usable outputs) and used DSPy's GEPA optimizer to generate structured feedback for each example where the model disagreed with humans. This prompted a repeatable optimization loop, resulting in a more reliable and cheaper judge for production use.