AI Careers & Industry

Hot take: Most AI startups are over-engineering their ML pipelines and under-engineering their data pipelines. Your model is only as good as your data. Spend 80% of your time on data quality, not architecture. I've seen this pattern at 3 companies now. The ones that win focus relentlessly on data curation.

We just open-sourced our inference optimization toolkit that reduced our serving costs by 73% while maintaining 99.9% accuracy parity. Key techniques: • Speculative decoding with draft models • KV cache compression (4-bit quantization) • Dynamic batching with priority queues • Prefix caching for repeated prompts Repo link in comments. Happy to answer questions about production deployment.

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.

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?

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

The EU AI Act is now being enforced. Here's what every AI team needs to know: 🟢 Low risk: Most AI applications — no requirements 🟡 Limited risk: Chatbots — transparency obligations 🔴 High risk: Healthcare, hiring, credit — full compliance 🚫 Unacceptable: Social scoring, real-time biometric — banned The compliance deadline for high-risk systems is 6 months away. Start now.