Today in Generative Media
Customers say no to AI; Mixed judgment in Anthropic copyright case; 1984 with LLMs
News and Opinion
‘It destroys the purpose of humanity’: Customers are saying no to AI (Washington Post)
Judge Alsup grants partial summary judgment to Anthropic, ruling training copies were fair use. But Judge rules no fair use in pirated copies of books used to build a central library. They are infringing. (ChatGPT Is Eating the World)
Elon Musk says xAI will retrain Grok: 'Far too much garbage' (Business Insider)
1984, but with LLM’s (Gary Marcus on Substack)
Mastodon updates its terms to prohibit AI model training (TechCrunch)
Thanks to social media, consumers have more power than ever. Just wait until generative AI becomes commonplace (Fast Company)
What happens when AI comes for our fonts? (The Verge)
Software
🤔👀🎬🖥️ Kimi-VL-A3B-Thinking-2506: A Quick Navigation (HuggingFace)
Claude Code for VSCode (Anthropic)
Graph-Code: A Multi-Language Graph-Based RAG System (GitHub) An accurate Retrieval-Augmented Generation (RAG) system that analyzes multi-language codebases using Tree-sitter, builds comprehensive knowledge graphs, and enables natural language querying of codebase structure and relationships.
Research
GenMOJO: Robust Multi-Object 4D Generation for In-the-wild Videos (project page)
A Feed-Forward 4D Foundation Model for Text-Driven Universal Mesh Animation (project page)
UniRelight: Learning Joint Decomposition and Synthesis for Video Relighting (nvidia.com)
WildCAT3D: Appearance-Aware Multi-View Diffusion in the Wild (project page)
ContentV: Efficient Training of Video Generation Models with Limited Compute (project page)
Align Your Flow: Scaling Continuous-Time Flow Map Distillation (project page)
Metropolis-Hastings Sampling for 3D Gaussian Reconstruction (project page)
UltraZoom: Generating Gigapixel Images from Regular Photos (project page)
PosterCraft: ✨⭐ Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework (project page)
Lumina: Real-Time Mobile Neural Rendering by Exploiting Computational Redundancy (arXiv)