As Penpa Tsering won re-election as the Sikyong (executive head) of the Central Tibetan Administration for another five-year term, Tenzin Jigmey* argues that one his central focuses should be to work towards “transforming our children into world-class entrepreneurs and innovators” for which purpose he suggests ways to give strategic priority to attaining expertise in Science

Get the scoop on the most recent ranking from the Tiobe programming language index, learn a no-fuss way to distribute DIY tooling across Python projects, and take a peek at ComfyUI: interactive, node-based tools for generative AI workloads in Python. Python faces new challenges from old rivals, but is it a blip or something more?

Netzilo announced expanded capabilities of Netzilo AI Edge, delivering comprehensive visibility, agent sandboxing, and advanced governance for enterprises deploying OpenClaw AI agents. As organizations accelerate adoption of OpenClaw — a next-generation autonomous AI agent capable of performing complex tasks with minimal supervision — security teams face new challenges in visibility, agent integrity, and data protection.

References Rejeb, A. et al. The Internet of Things (IoT) in healthcare: Taking stock and moving forward. Internet Things. 22, 100721 (2023). Google Scholar  Mustafa, N. & Hussain, K. Threat modeling for healthcare industry security: Safeguarding IoT devices and ensuring business continuity (2024). Li, C., Wang, J., Wang, S. & Zhang, Y. A review of

Single-cell RNA sequencing data consistently reveal robust statistical structures, prompting the creation of complex foundation models like TranscriptFormer to generate gene expression embeddings. Huan Souza and Pankaj Mehta, both from the Department of Physics and the Faculty of Computing and Data Science at Boston University, demonstrate that comparable performance can be achieved using significantly less

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If you want an even better AI model, there could be reason to celebrate. Google, on Thursday, announced the release of Gemini 3.1 Pro, characterizing the model's arrival as "a step forward in core reasoning." Measured by the release cadence of machine learning models, Gemini 3.1 Pro is hard on the heels of recent model

The search space for protein engineering grows exponentially with complexity. A protein of just 100 amino acids has 20^100 possible variants-more combinations than atoms in the observable universe. Traditional engineering methods might test hundreds of variants but limit exploration to narrow regions of the sequence space. Recent machine learning approaches enable broader searches through computational