Tootfinder

R doesnt need to be a hard and scientific tool 📈. You can use it to make art 🎨: #rstats

GitHub - cutterkom/generativeart: Create Generative Art with R
Create Generative Art with R. Contribute to cutterkom/generativeart development by creating an account on GitHub.

⚡ Food Vendors Ditch Noisy Generators as City Explores Cleaner Power Options
#nyc

Food Vendors Ditch Noisy Generators as City Explores Cleaner Power Options
From halal carts to hot dog stands, sellers have long relied on noisy, polluting fossil fuel generators. Enter battery-powered alternatives — but the transition isn’t cheap or simple.

Understand Before You Generate: Self-Guided Training for Autoregressive Image Generation
Xiaoyu Yue, Zidong Wang, Yuqing Wang, Wenlong Zhang, Xihui Liu, Wanli Ouyang, Lei Bai, Luping Zhou
https://arxiv.org/abs/2509.15185

Understand Before You Generate: Self-Guided Training for Autoregressive Image Generation
Recent studies have demonstrated the importance of high-quality visual representations in image generation and have highlighted the limitations of generative models in image understanding. As a generative paradigm originally designed for natural language, autoregressive models face similar challenges. In this work, we present the first systematic investigation into the mechanisms of applying the next-token prediction paradigm to the visual domain. We identify three key properties that hinder th…

I don’t understand why we must spend billions of dollars and waste so much water for AI.
I just created an “AI-generated image” from the original using a few dabs of the retouch tool in the Photos app.
Might have taken a sip of water afterwards but that was it.
(Is there a word/phrase for taking a regular image and making it look like it was AI generated? There should be.)

Garrett, Parsons among 'best in this generation' https://www.espn.com/nfl/story/_/id/46315350/garrett-sees-parsons-very-best-generation

Calibrated Generative AI as Meta-Reviewer: A Systemic Functional Linguistics Discourse Analysis of Reviews of Peer Reviews
Gabriela C. Zapata, Bill Cope, Mary Kalantzis, Duane Searsmith
https://arxiv.org/abs/2509.15035

Calibrated Generative AI as Meta-Reviewer: A Systemic Functional Linguistics Discourse Analysis of Reviews of Peer Reviews
This study investigates the use of generative AI to support formative assessment through machine generated reviews of peer reviews in graduate online courses in a public university in the United States. Drawing on Systemic Functional Linguistics and Appraisal Theory, we analyzed 120 metareviews to explore how generative AI feedback constructs meaning across ideational, interpersonal, and textual dimensions. The findings suggest that generative AI can approximate key rhetorical and relational fe…

Micah Parsons' HS coach reveals mindset that makes Cowboys star generational https://www.si.com/nfl/cowboys/news/micah-parsons-high-school-coach-reveals-mindset-that-makes-dallas-cowboys-star-generational-ta…

GenKOL: Modular Generative AI Framework For Scalable Virtual KOL Generation
Tan-Hiep To, Duy-Khang Nguyen, Tam V. Nguyen, Minh-Triet Tran, Trung-Nghia Le
https://arxiv.org/abs/2509.14927

GenKOL: Modular Generative AI Framework For Scalable Virtual KOL Generation
Key Opinion Leader (KOL) play a crucial role in modern marketing by shaping consumer perceptions and enhancing brand credibility. However, collaborating with human KOLs often involves high costs and logistical challenges. To address this, we present GenKOL, an interactive system that empowers marketing professionals to efficiently generate high-quality virtual KOL images using generative AI. GenKOL enables users to dynamically compose promotional visuals through an intuitive interface that inte…

Breaking the SFT Plateau: Multimodal Structured Reinforcement Learning for Chart-to-Code Generation
Lei Chen, Xuanle Zhao, Zhixiong Zeng, Jing Huang, Liming Zheng, Yufeng Zhong, Lin Ma
https://arxiv.org/abs/2508.13587

Breaking the SFT Plateau: Multimodal Structured Reinforcement Learning for Chart-to-Code Generation
While reinforcement learning (RL) has proven highly effective for general reasoning in vision-language models, its application to tasks requiring in-depth understanding of information-rich images and generation of structured outputs remains underexplored. Chart-to-code generation exemplifies this challenge, demanding complex reasoning over visual charts to generate structured code. Supervised fine-tuning (SFT) alone is often insufficient, highlighting the need for effective RL strategies that a…