Hollywood is racing to test AI across development, VFX, and marketing—promising speed and scale while risking homogenization and opaque decision-making. Here’s what the experimentation phase means for studios, marketers, and creators.
Jonathan Nolan on AI: Why AI in film production Is in a Frothy Moment Read Post »
As context windows jump to 128K–256K+ tokens, attention’s quadratic costs strain memory and interconnects. Here’s how JAX/XLA teams can combine sequence parallelism, sharding, and checkpointing to keep utilization high and step times in check.
Scaling Long-Context Model Training in JAX and XLA: A Practical Playbook for Engineers Read Post »
Automated traffic has overtaken human visits on the web, driven by a surge in AI scrapers and retrieval bots. Publishers are deploying new defenses—and business models—to protect content and revenue.
The Impact of AI Crawlers on Web Traffic: What Publishers Need to Know Now Read Post »
A practical guide to turning complex PDFs into structured, searchable data for retrieval-augmented generation—preserving tables, charts, and layout while scaling with NVIDIA NIM.
A modular approach that splits future-state prediction from action selection is showing that it can match or beat behavior cloning with far fewer demonstrations—especially when data is narrow or well-covered.
A practical guide to align data sourcing, generation, and governance so your distillation workflows stay within license and privacy boundaries while preserving business value.
How to Build License-Compliant Synthetic Data Pipelines for AI Model Distillation Read Post »
