MLOps & Deployment

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?

We just raised our Series B! NeuralScale is building the infrastructure layer that will power the next generation of AI applications. Think of us as 'Vercel for AI inference' — but with 10x better latency and 3x lower cost than existing solutions. Hiring across the board: infra engineers, ML engineers, and product managers. DM me or check the jobs page.

Our latest paper on emergent world models in large language models just got accepted at ICML 2026! 🎉 Key finding: When you train LLMs on enough diverse data, they develop internal representations that look remarkably like simplified physics engines. This has huge implications for how we think about grounding and embodied AI.

Just finished interviewing 50+ AI engineers about their tech stack. Consensus stack for 2026: • Models: GPT-4o + Claude 3.5 Sonnet + Llama 4 (open) • Frameworks: LangChain or LlamaIndex • Vector DB: Pgvector (startups) / Pinecone (enterprise) • Orchestration: Temporal or Inngest • Monitoring: W&B + LangSmith • Deployment: Replicate, Modal, or self-hosted The stack is maturing fast.