“Shines on tool use, math, and instruction following.”

“The new AI HAT+ 2 is designed for local generative AI model inference on edge devices.”

“AMDのリサ・スーCEOが世界最大級の家電見本市「CES 2026」の基調講演を実施し、PC向けプロセッサの「Ryzen AI 400シリーズ」やAIデータセンター向けGPU「MI455X」などの製品を発表しました。”

“It’s built to power reliable on-device agentic applications: higher quality, lower latency, and broader modality support in the ~1B parameter class.”

“AI for everyone.”

“AMD announced the latest version of its AI-powered PC chips designed for a variety of tasks from gaming to content creation and multitasking.”

“Samsung aims to scale Galaxy AI to 800 million devices by 2026”

“HY-MT1.5 consists of 2 translation models, HY-MT1.5-1.8B and HY-MT1.5-7B, supports mutual translation across 33 languages with 5 ethnic and dialect variations”

“The paper explores integer (Int8) quantization and a resource-aware gait scheduling viewpoint to maximize RL reward under power constraints.”

“The article mentions the use of AI to refine the author's writing and integrate the latest product roadmap. It also references CEO Demis Hassabis's vision of accelerating scientific discovery.”

“The research focuses on Hierarchical and Dynamic Rank Adaptation for Mobile Vision Language Models.”

“The paper focuses on federated on-device autoencoder denoising.”

“”

“The article's context indicates a focus on on-device processing for HAR.”

“The article focuses on Transformer inference for Edge Devices.”

“The article's context revolves around the MiniConv library's capabilities.”

“”

“The episode discusses the persistent challenge of object hallucination in Vision-Language Models (VLMs).”

“The context mentions that the paper is from ArXiv.”

“Shrinking AI for your phone is no simple matter.”

“”

“Hung Bui details his team's work on SwiftBrush and SwiftEdit, which enable high-quality text-to-image generation and editing in a single inference step.”

“The article's origin is Hacker News, suggesting a developer-focused audience and potential for technical depth.”

“We start with “DiMA: Distilling Multi-modal Large Language Models for Autonomous Driving,” an end-to-end autonomous driving system that incorporates distilling large language models for structured scene understanding and safe planning motion in critical "long-tail" scenarios.”

“We’re introducing an efficient, on-device robotics model with general-purpose dexterity and fast task adaptation.”

“We’re open-sourcing Stable Audio Open Small in partnership with Arm, whose technology powers 99% of smartphones globally. Building on the industry-leading text-to-audio model Stable Audio Open, the new compact variant is smaller and faster, while preserving output quality and prompt adherence.”

“”

“We’ve partnered with Arm to bring generative audio to mobile devices, enabling high-quality sound effects and audio sample generation directly on-device with no internet connection required.”

“Siddhika introduces Qualcomm's AI Hub, a platform developed to simplify the process of testing and optimizing AI models across different devices.”

“The article likely details how developers can now leverage the Mistral 7B model within their applications using Core ML.”

“We explore efficient diffusion models for text-to-image generation, grounded reasoning in videos using language models, real-time on-device 360° image generation for video portrait relighting...”

“Show HN: Tiny LLMs – Browser-based private AI models for a wide array of tasks”

“No direct quote available from the provided text.”

“Qualcomm is working with Meta to run Llama-2 on mobile devices.”

“Qualcomm works with Meta to enable on-device AI applications using Llama 2”

“”

“The article doesn't contain any direct quotes.”

“An on-device deep neural network is being used.”

“Snips is a AI Voice Assistant platform 100% on-device and private”

“Android is planning to launch TensorFlow Lite for mobile machine learning.”

“This Week in Machine Learning & AI brings you the week’s most interesting and important stories from the world of machine learning and artificial intelligence.”

“This article discusses how Google optimizes its deep learning models for mobile devices.”