Stop everything you’re doing because the newly revamped Dreamina AI Web just turned my browser into a full-blown tech keynote studio! The entire Canvas and workspace received a massive overhaul, dropping an impressive library of viral Workflows and master-class Camera Skills designed for cinematic ads and blockbuster visuals. Look at how it gave a standard, everyday grocery store banana the full Cupertino flagship treatment—essentially launching the "Banana Pro Max." We’re talking dramatic golden rim lighting piercing through moody shadows, buttery-smooth slow-mo macro sweeps showing off the peel’s organic texture, and hyper-crisp hero angles that look straight out of a million-dollar Apple launch event. The fact that a single piece of fruit can be made to look this sleek, premium, and legendary in seconds is genuinely surreal!

We’ve just released the preview version of our latest paper: Infinite-Parameter LLMs — Generating and Adapting Weights from Live Data. In it, we demonstrate that large language models can learn up to 1,000x faster through Bayesian Self-learning transformers (BAST) than SOTA training algorithms engineered by human researchers. This breakthrough marks a critical shift in AI research, from cost-intensive, static-weight AI to energy-efficient, dynamic-weight AI. For a decade, building more advanced models has been achieved by training bigger ones on more data. This approach is now hitting a ceiling with the supply of pretraining text projected to run out in the next two to five years. Meanwhile, the fast adoption of AI agents is producing an unprecedented amount of continuously-growing data that models can learn from. None of it is captured, locked in individual sessions and lost as soon as the agent completes the task. Bringing self-learning AI agents to users captures the value of that data. We validated that BAST LLMs overcome the fundamental limitations of memory-based learning, such as Retrieval-Augmented Generation (RAG), in both cost and performance, across long-context conversations. Without underlying architectural innovation like BAST, RAG, larger context windows, and agent scaffolding suffer a drop in performance and escalating input token cost due to the static weight of LLMs. This research underscores a fundamental premise of our work at Boltzbit to achieve General Learning Intelligence and democratise model ownership. Scaling up static-weight model size or layering on more workarounds cannot address the spiraling cost of training AI systems. The viable path is a new AI architecture that adapts its own weights. Preview version of the paper: arxiv.org/pdf/2609.18842

Meet PixVerse R2, our new real-time world model. Explore living worlds. Control and edit them with prompts. Shape the story. Meet characters that remember and respond.