AutoIndex with @Sam25491761 is a recent Weaviate Podcast episode I keep coming back to! 🤔 AI designing our schemas and preprocessing pipelines feels like an obvious direction the puck is heading. To be fair, I think this is already how most people work with Claude Code and their databases. But it mostly happens at a shallow, text-to-schema layer, maybe with some sample data. I think the more interesting idea is database refactoring. 🏗️ Firstly, I think you'll expect your database to keep a record of its workload, the same way it keeps your data: queries, transactions, metadata updates, or all of the above. 🔬 Weaviate has recently shipped dashboards in our Cloud Console for query latency, ingest throughput, average batch size, and object count. I am super excited to see this continue to evolve. 💚🚀 Secondly, Branching databases are an interesting opportunity for the next level of turning that workload log into new database designs. 🌿 An AutoIndex agent could fork the database, try a new schema or preprocessing program, replay the workload, and merge the winner. 🏆 I loved learning about BranchBench at VLDB and diving into what branching databases actually entail at the storage engine layer. Neon branches the write-ahead log and gives each branch its own compute. This makes reads fast, but branching slow. Contrastively, Dolt branches a content-addressed hash tree, so a branch is just a pointer, but reads degrade as branches pile up. ⚖️ Weaviate has a related history with native multi-tenancy. I'm very interested to see if this topic picks up steam and to dive further into what branching looks like for Weaviate. Especially branches that share or merge index structures! This could also get really complicated with heterogeneous embedding models per collection... If you haven't seen the episode yet, I hope this inspires your interest in it! Would love to know what you think! youtube.com/watch?v=mAj92SoE…