Hi team, I'm really interested in spiceDB and wanted to share a few ideas from my perspective as an AI/ML engineer working on scalable backend systems. First off, I've been impressed by authzed's scalability—handling up to a million qps is no small feat. that said, I've noticed some performance inconsistencies and latency spikes during high-traffic periods or after feature rollouts. This might be a great opportunity to explore ML-driven anomaly detection across metrics like latency, throughput, and error rates. It could help proactively surface issues before they affect users. On a similar note, predictive analytics could be valuable for forecasting capacity needs and fine-tuning resource allocation—especially as systems scale. I also see potential around schema design and migration. while the existing CI/CD support for schema validation is solid, complex policies can still introduce subtle bugs or lead to unintended access patterns. for teams unfamiliar with zanzibar-style models, this can be a real hurdle. It might be worth exploring AI-powered tooling that can analyze application models and suggest optimized spiceDB schema definitions, or even assist with migrating legacy auth systems—automating some of the heavy lifting and reducing error-prone manual effort. I’ve got a few other ideas as well and would be happy to chat or brainstorm further if that’s helpful. Thanks for all the great work you’re doing—big fan of the project.