Generative AI

Just published our comprehensive benchmark comparing 15 vector databases for production RAG systems. TL;DR: No single winner. Pgvector is surprisingly competitive for <10M vectors. Pinecone leads on managed ease. Qdrant has the best price-performance ratio. Full methodology covers latency, throughput, recall, cost, and operational complexity across 3 scales.

Excited to announce: I'm joining Anthropic as Head of AI Safety Research. After 8 years at DeepMind, this feels like the right moment to focus entirely on alignment. The problems are getting harder, but the community is getting stronger. Grateful for everyone who supported this journey. Let's build safe AI together. 🙏

The new FLUX model is genuinely impressive for real-time video understanding. I've been benchmarking it against our production pipeline at Tesla. Key observations: • 3x faster inference than previous SOTA • Better temporal consistency across frames • Still struggles with fine-grained action recognition • Impressive zero-shot performance on edge cases Anyone else running comparisons?

PSA for anyone building RAG systems: Your chunking strategy matters more than your embedding model. We tested 8 chunking approaches × 4 embedding models × 3 retrieval methods. Result: The best chunking strategy outperformed the best embedding model by 23% on retrieval quality. Semantic chunking with 15-20% overlap is the sweet spot.

Lessons from building real-time AI video generation at Runway: 1. Latency budgets are everything — users notice >100ms 2. Streaming architectures beat batch processing 10:1 3. Progressive rendering is key to perceived speed 4. GPU memory management is the real engineering challenge 5. The gap between demo and production is 6-12 months Shipping creative AI is wildly different from shipping chatbo...see more