AI Radar

I'm learning inference engineering, so I tried to run a 22B video model (LTX-2.5, unquantized, with audio) on my laptop's integrated GPU

I'm trying to get into inference engineering, and I wanted a project that would force me to learn the hard parts instead of just calling a library. So I picked something that shouldn't fit: **LTX-2.5 (22B), in bf16 with no quantization**, on an **Intel Core Ultra 5 iGPU with 15.6 GB of shared RAM**. No discrete GPU. The model is about 66 GB on disk, so I never load it. The weights stay on NVMe and get streamed **layer by layer**: aligned reads into a small pinned buffer, a prefetch thread loading layer n+1 while layer n computes, and the weights swapped in and out with hooks. It uses the…

Update on my Asian Character LoRA for Krea 2

In case people missing my previous post, right now I have around 72 lora now You can see the preview and download at my huggingface https://huggingface.co/spaces/fidel1234/Krea2_Asian_Character_Showcase (I will keep update my huggingface until I get bored) It's trained on gravure idol photobook, so try to use various gravure idol theme shoot. I tried my best to capture their proportion. That's it, have a nice weekend. submitted by /u/Fideltampan12345 [link] [comments]

What 30,000 Hours of Ego-centric Video Does Not Teach

World models offer a promising alternative to physics-based simulators, yet remain far from practical deployment. We ask how far scaling ego-centric human video takes them, using a dataset of 30,000 hours spanning over 1,000 scene types and 14,000 contributors. Rather than relying on opaque downstream metrics, we directly evaluate agent and object-interaction fidelity on a challenging out-of-distribution benchmark. Increasing training data by 100x improves both, but unevenly: the agent is modeled well, while object fidelity remains far lower and improves slowly. We show that the agent gains…

Dead Space Creator Glen Schofield on AI's Limits in Game Development

"A creative idea can save more money than AI can right now."

MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvement

Reinforcement learning (RL) is the central training paradigm for advancing large foundation models towards self-improvement. This report introduces the MiMo-V2.6 series, an omni-modal family that pushes the frontier of model intelligence by scaling RL compute. Prior to RL, we conduct mid-training on a broad multimodal corpus to provide ample exploration space, and build a solid infrastructure on the pretrained hybrid-SWA architecture to support subsequent scale-up. We scale RL compute along three dimensions: (1) larger batches and higher throughput, with an asynchronous training that consumes…

Gemini 4 Argon: our next era of frontier intelligence

Claude Haiku 5.5

OpenAI annualised revenues $20B less than previously signalled