The Asus ROG Flow Z13 may be the best gaming tablet ever made, with the best integrated GPU in history. I've never found a "gaming tablet" that left a strong impression on me, and the 2025 Asus ROG Flow Z13 hasn't changed that. What I was really looking forward to was the debut of the AMD Ryzen AI Max (Strix Halo)–a unique processor that combines a powerful CPU with an NPU, a killer integrated GPU, and a massive cache, all for what I hoped would be a bacchanal of gaming and AI applications. I discovered that it could, but the competition is still better. Asus has a history of applying innovative solutions to gaming on the go. AMD should certainly celebrate what's been accomplished here, I just don't think it works. Yes, the Flow is perhaps the lightest gaming solution outside of a handheld, with a chip inside that has the best integrated graphics ever made. But it all boils down to a good product that falls short of greatness and for a price that should deliver much more. We've seen the Z13 before. In 2025, AMD convinced Asus that its powerful Ryzen AI Max, relying on integrated graphics alone, could measure up. Asus sells the ROG Flow in three different configurations.

If you've purchased a laptop or tablet with an AMD Ryzen chip inside, there's a performance tweak you absolutely need to know about. Savvy gamers know instinctively that you can boost your game's frame rate by lowering the resolution or the visual quality, or by making an adjustment to the Windows power-performance slider. But the Ryzen AI Max is a new kind of device: a killer mobile processor that can run modern games at elevated frame rates, and serve as an AI powerhouse. A simple adjustment of the Ryzen AI Max's unified frame buffer, or available graphics memory. While it's a simple fix, in my tests, it made an enormous difference: up to a 60 percent performance boost in some cases.

Recent work in speech-to-speech translation (S2ST) has focused primarily on offline settings, where the full input utterance is available before any output is given. This, however, is not reasonable in many real-world scenarios. In latency-sensitive applications, rather than waiting for the full utterance, translations should be spoken as soon as the information in the input is present. In this work, we introduce a system for simultaneous S2ST targeting real-world use cases. Our system supports translation from 57 languages to English with tunable parameters for dynamically adjusting the latency of the output -- including four policies for determining when to speak an output sequence. We show that these policies achieve offline-level accuracy with minimal increases in latency over a Greedy (wait-$k$) baseline. We open-source our evaluation code and interactive test script to aid future SimulS2ST research and application development.

This paper addresses the dire need for a platform that efficiently provides a framework for running reinforcement learning (RL) experiments. We propose the CaiRL Environment Toolkit as an efficient, compatible, and more sustainable alternative for training learning agents and propose methods to develop more efficient environment simulations. There is an increasing focus on developing sustainable artificial intelligence. However, little effort has been made to improve the efficiency of running environment simulations. The most popular development toolkit for reinforcement learning, OpenAI Gym, is built using Python, a powerful but slow programming language. We propose a toolkit written in C++ with the same flexibility level but works orders of magnitude faster to make up for Python's inefficiency. This would drastically cut climate emissions. CaiRL also presents the first reinforcement learning toolkit with a built-in JVM and Flash support for running legacy flash games for reinforcement learning research. We demonstrate the effectiveness of CaiRL in the classic control benchmark, comparing the execution speed to OpenAI Gym. Furthermore, we illustrate that CaiRL can act as a drop-in replacement for OpenAI Gym to leverage significantly faster training speeds because of the reduced environment computation time.