PCWorld reports that the traditional one-size-fits-all laptop is giving way to purpose-built devices designed for specific tasks and user needs. The market appears to be splitting into two clear niches, with ultralight options like the Acer Swift Blade at just 1.76 pounds and high-performance AI workstations like the HP ZBook Ultra G1a. Nvidia RTX Spark laptops, the Asus ZenBook Duo, and Framework Laptop 13 suggest manufacturers are increasingly matching hardware to real-world productivity demands. Apple's paragon of portability, the 13-inch MacBook Air, weighs 2.7 pounds. At IFA, two PC makers announced 14-inch laptops that weigh .

When you purchase through links in our articles, we may earn a small commission. Save $182 on the Acemagic Retro X3, a compact Ryzen 7 mini PC with 1TB storage and Windows 11 Pro included. Mini PCs are great for saving space on a cramped desktop and for solid performance at affordable prices. The Acemagic Retro X3 is one of the best value mini PCs on the market, and more so now that it's on sale for just $517.50. That's a huge discount off its original $699.99 price and a strapping good deal considering the hardware packed inside--not to mention it's a brand new all-time low price by a significant margin.

The Lenovo ThinkPad P16s Gen 4 is a typical workstation affair. It’s fairly potent but big, bulky, and pricey. Next to consumer laptops, its performance doesn’t impress, and new Panther Lake hardware threatens it at every turn.

Ikea's New Designer Collection Is Home-Office Heaven From a twisty lamp to a bendy clock, the Swedish company's full 2026 PS Collection has dropped. It's a boon for those looking to refine their homes and workstations. Now that the hubbub of the cat-tested blow-up chair has calmed down, Ikea has taken the wraps off the rest of its latest PS Collection (which stands for Post Scriptum). Consisting of 44 pieces, including the viral inflatable PS 2026 Easy Chair, the furniture brand says the theme of the 2026 PS Collection is playful functionality. The new PS Collection (available in stores from May 14, then online from May 22) has not one but two options for accommodating impromptu sleepovers.

We study the problem of generating arbitrarily large environments to improve the throughput of multi-robot systems. Prior work proposes Quality Diversity (QD) algorithms as an effective method for optimizing the environments of automated warehouses. However, these approaches optimize only relatively small environments, falling short when it comes to replicating real-world warehouse sizes. The challenge arises from the exponential increase in the search space as the environment size increases.

When you purchase through links in our articles, we may earn a small commission. This machine exceeds expectations -- as long as you don't expect much AI. HP's ZBook 8 G1i is a capable professional workstation with fast performance, good thermals, and an unusually long warranty. The HP ZBook 8 G1i is a high-end workstation laptop designed for professional workloads: CAD, 3D modeling, and video editing . It's priced to match, too.

The rapid growth of e-commerce in recent years has significantly transformed people's shopping habits [1]. Consumers increasingly favor online shopping over in-person purchases, leading to a substantial impact on product logistics, which plays a crucial role in customer satisfaction. In addition to product quality and other factors, the timely delivery of orders has become a key determinant of customer satisfaction. Picking and replenishment tasks are responsible for 65% of operating costs [2]. In a conventional manual order picking system, often referred to as a picker-to-parts system, pickers dedicate 70% of their working time to searching for items and traveling within the facility [3, 4].

We study the problem of generating arbitrarily large environments to improve the throughput of multi-robot systems. Prior work proposes Quality Diversity (QD) algorithms as an effective method for optimizing the environments of automated warehouses. However, these approaches optimize only relatively small environments, falling short when it comes to replicating real-world warehouse sizes. The challenge arises from the exponential increase in the search space as the environment size increases.

We study optimizing a destination-to-chutes task mapping to improve throughput in Robotic Sorting Systems (RSS), where a team of robots sort packages on a sortation floor by transporting them from induct workstations to eject chutes based on their shipping destinations (e.g. Los Angeles or Pittsburgh). The destination-to-chutes task mapping is used to determine which chutes a robot can drop its package. Finding a high-quality task mapping is challenging because of the complexity of a real-world RSS. First, optimizing task mapping is interdependent with robot target assignment and path planning. Second, chutes will be CLOSED for a period of time once they receive sufficient packages to allow for downstream processing. Third, task mapping quality directly impacts the downstream processing, as scattered chutes for the same destination increase package handling time. In this paper, we first formally define task mappings and the problem of Task Mapping Optimization (TMO). We then present a simulator of RSS to evaluate task mappings. We then present a simple TMO method based on the Evolutionary Algorithm and Mixed Integer Linear Programming, demonstrating the advantage of our optimized task mappings over the greedily generated ones in various RSS setups with different map sizes, numbers of chutes, and destinations. Finally, we use Quality Diversity algorithms to analyze the throughput of a diverse set of task mappings. Our code is available online at https://github.com/lunjohnzhang/tmo_public.