Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Creator Playbook Mashable Voices Trending Now Say More Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List Switch Off In My Bag All Series Spend less for a great gaming monitor that'll immerse you in your favorite titles. Brittany is fueled by horror, rainbow-sugar-pixel-rushes, and video games. Until her dying breath she'll be wielding a BFG made entirely of killer drive and ambition. Check out her work at PfhorTheWin.com. Like a fabulous shooter once said, get psyched!

I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen Will plug-in solar plus batteries solve your power problem? It can't be just about harvesting solar power - it's also about storage, and time-shifting cheap, off-peak power to times when it's more expensive. Batteries let you store and timeshift power. They can store solar-generated power or cheap, off-peak power. To make this work, you need a TOU tariff with a decent peak/off-peak rate spread.

Will ran game benchmarks without OBS running, then with OBS on different setups to see what kind of effect it had on your game's framerates. Long story short: Across a variety of hardware setups with the hugely powerful RTX 5090, from mid-range to high-powered CPUs, you're going to see a predictable drop of about 10-15 percent while running an OBS stream, even without other tools.

Mashable Voices Look Up Trending Now Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Say More Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List Switch Off In My Bag VidCon with Mashable All Series Bring some oomph to your favorite TV shows and movies. Brittany is fueled by horror, rainbow-sugar-pixel-rushes, and video games. Until her dying breath she'll be wielding a BFG made entirely of killer drive and ambition. Check out her work at PfhorTheWin.com. Like a fabulous shooter once said, get psyched!

Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Say More Safety Net Creator Hub Versus Gift Ideas For Everyone On Your List Mashable Selects Switch Off Trending Now In My Bag VidCon with Mashable All Series Don't settle for flat TV audio when this deal exists. Brittany is fueled by horror, rainbow-sugar-pixel-rushes, and video games. Until her dying breath she'll be wielding a BFG made entirely of killer drive and ambition. Check out her work at PfhorTheWin.com. Like a fabulous shooter once said, get psyched!

Power companies are pushing aggressive time-based use pricing. Here's how a regular consumer can benefit. I like to keep my home at a cool and comfortable 68 degrees year-round. This preference would be fine if I lived near the Pacific Ocean, or in a small home, or in a newer home that's insulated with modern mineral wool instead of tissue paper and horsehair. I, however, live in a 2,000-plus-square-foot home built in 1906.

Out-of-distribution (OOD) 3D relighting requires novel view synthesis under unseen lighting conditions that differ significantly from the observed images. Existing relighting methods, which assume consistent light source distributions between training and testing, often degrade in OOD scenarios. We introduce MetaGS to tackle this challenge from two perspectives. First, we propose a meta-learning approach to train 3DGaussian splatting, which explicitly promotes learning generalizable Gaussian geometries and appearance attributes across diverse lighting conditions, even with biased training data. Second, we embed fundamental physical priors from the Blinn-Phong reflection model into Gaussian splatting, which enhances the decoupling of shading components and leads to more accurate 3D scene reconstruction. Results on both synthetic and real-world datasets demonstrate the effectiveness of MetaGS in challenging OOD relighting tasks, supporting efficient point-light relighting and generalizing well to unseen environment lighting maps.

We study how to fine-tune LLMs using user-edit deployment data consisting of a set of context, an agent's response, and user edits. This deployment data is naturally generated by users in applications such as LLMs-based writing assistants and coding agents. The natural origin of user edits makes it a desired source for adapting and personalizing of LLMs. In this setup, there emerges a unification of various feedback types namely preferences, supervised labels, and cost that are typically studied separately in the literature. In this paper, we initiate the theoretical investigation of learning from user edits.

The efficiency and quality of language model pretraining are largely determined by the way pretraining data are selected. In this paper, we introduce Group-MATES, an efficient group-level data selection approach to optimize the speed-quality frontier of language model pretraining. Specifically, Group-MATES parameterizes costly group-level selection with a relational data influence model. To train this model, we sample training trajectories of the language model and collect oracle data influences alongside. The relational data influence model approximates the oracle data influence by weighting individual influence with relationships among training data. To enable efficient selection with our relational data influence model, we within partition each the cluster dataset independently into small clusters . Experiments using relationship on DCLM weights 400M-4x, and 1B-1x, select data and 3B-1x show that Group-MATES achieves 3.5%-9.4%

Adafactor is an early memory-efficient optimization algorithm proposed as an alternative to Adam. By eliminating first-order momentum and employing a rank-1 matrix factorization to approximate the second-moment matrix, Adafactor achieves near-zero memory overhead compared to traditional gradient descent methods. Despite its practical suitability for large-scale training tasks where memory efficiency is critical, its theoretical convergence analysis remains unexplored, largely due to the challenges posed by its matrix factorization and update clipping mechanisms. In this work, we provide a convergence analysis of Adafactor for non-convex smooth optimization. We establish optimal convergence rates (up to logarithmic factors) for finding stationary points in both deterministic and stochastic settings, the latter under sub-Gaussian noise. Central to our analysis is viewing Adafactor as an approximation of Adam, and the use of a new proxy step-size to approximate the unique adaptive step-size induced by Adafactor's matrix factorization and update clipping, along with an induction argument to control the gradient magnitude. Our findings may theoretically suggest that involving rank-1 matrix approximation of the second-moment matrix in Adam does not fundamentally hinder the convergence.