According to Ai (@ai_darpa), a user recently demonstrated an impressive AI-driven morphing transition using WAN22 and LoRA, highlighting the rapid evolution of generative visual effects technology (source: twitter.com/ai_darpa/status/1992947057267720395). This development illustrates the growing potential for AI models like WAN22 and LoRA to automate and enhance complex video transitions, which can significantly reduce production time and costs for digital content creators. The demonstration underscores practical applications in marketing, entertainment, and advertising, where high-quality, AI-generated morphing effects can create more dynamic and engaging visual content, opening up new business opportunities in content creation and post-production services.

According to @AndrewYNg, DeepLearning.AI has launched a new course titled 'Fine-tuning and Reinforcement Learning for LLMs: Intro to Post-training,' taught by @realSharonZhou, VP of AI at AMD (source: Andrew Ng, Twitter, Oct 28, 2025). The course addresses a critical industry need: post-training techniques that transform base LLMs from generic text predictors into reliable, instruction-following assistants. Through five modules, participants learn hands-on methods such as supervised fine-tuning, reward modeling, RLHF, PPO, GRPO, and efficient training with LoRA. Real-world use cases demonstrate how post-training elevates demo models to production-ready systems, improving reliability and user alignment. The curriculum also covers synthetic data generation, LLM pipeline management, and evaluation design. The availability of these advanced techniques, previously restricted to leading AI labs, now empowers startups and enterprises to create robust AI solutions, expanding practical and commercial opportunities in the generative AI space (source: Andrew Ng, Twitter, Oct 28, 2025).