The all-in-one SaaS platform for independent restaurants, processing 1.5 million orders a month across nine CEE markets, is now targeting Portugal, Spain, Italy, France, Germany, and the Netherlands. Choice, the Prague-founded restaurant management platform that bundles ordering, payments, reservations, and marketplace integrations into a single subscription, has closed a $7.1 million Series A. The round […]
This story continues at The Next Web
Hints and answers to today’s Hurdle all in one place.
Here’s the answer for “Wordle” #1732 on March 17 as well as a few hints, tips, and clues to help you solve it yourself.
The NYT Strands hints and answers you need to make the most of your puzzling experience.
Connections is a New York Times word game that’s all about finding the “common threads between words.” How to solve the puzzle.
The New York Times’ latest game, Pips, brings domino fun to your desktop. How to play Pips as well as hints in case you get stuck.
YouTube creator DirectorFeng is back with an ASMR-focused video highlighting the storage upgrade on a new Apple device. In this newest episode, DirectorFeng adeptly upgrades a 256GB MacBook Neo to 1TB, following an earlier attempt to enhance the iPhone 17 Pro Max to 2TB, which encountered NAND compatibility problems.
The video offers an engaging teardown experience, where DirectorFeng carefully removes the logic board to reach the internal parts. This process includes desoldering the existing 256GB NAND chip, cleaning the area, and then soldering in a new 1TB substitute. After putting the device back together, DirectorFeng powers on the MacBook Neo and carries out the required Device Firmware Update (DFU) steps with help from another Mac, ultimately showcasing the successful upgrade in the System Settings.
Curiously, DirectorFeng is not the first to attempt this modification. Commenters noted that another creator, Yang Changshun, had previously executed the same upgrade during a live stream, although that video is presently unavailable on YouTube.
For those looking into similar upgrades or components, exploring related products on Amazon may be beneficial.
We’re examining laptops that surpass home consoles like the PlayStation 5 and act as the ultimate platform for gamers.
Opting for the most expensive, high-performance laptop alone isn’t the wisest approach. Buyers should consider TGP, or total graphics power, in addition to thermal management. It’s impressive to have an Nvidia RTX 5090 24GB GPU, but if the cooling system fails to operate effectively, users won’t fully benefit from it. Key features to look for include vapor chambers, liquid metal thermal paste, and dedicated fans for both intake and expelling hot air. Also, do not compromise on anything less than a Mini-LED or OLED display, as no one desires motion blur or inaccurate color representation while using a high-performance laptop. Let’s dive in.
MSI Titan HX AI is quite remarkable. Although it’s built to function as a portable command center, it consumes 270W of total power through its sleek and slender chassis. This power supports an Intel Core Ultra 9 285HX processor and an Nvidia RTX 5090 24GB graphics card, which easily handles 4K resolutions through its 18-inch 4K Mini-LED display. This screen is among the brightest available, reaching 1,000 nits, leading to HDR performance that is truly commendable. Furthermore, this display can achieve up to 120 FPS due to its 120Hz refresh rate.
Up to 96GB of DD5 6400 RAM can be installed in the MSI Titan HX AI across two slots, so if that doesn’t showcase how overpowered this laptop is, not much else will.
Participate in our audio survey to potentially grant a Mashable Readers’ Choice Award to the headphones/earbuds, speakers, and soundbars you use.
Apple’s researchers have created a groundbreaking AI model that can reconstruct a 3D object from just one image while preserving consistent reflections, highlights, and various visual effects from multiple viewing perspectives. This progress is outlined in their paper titled “LiTo: Surface Light Field Tokenization.”
### A Bit of Context
The notion of latent space in machine learning has surged in popularity in recent years, especially with the advent of transformer-based AI models. Latent space pertains to the practice of compressing information into numerical forms and arranging these figures within a multi-dimensional framework, facilitating efficient distance and probability calculations.
For example, by altering mathematical representations of words, one can uncover relationships among them, such as transforming “king” into “queen” by adding and subtracting other representations.
This technique of encoding information as mathematical forms allows for quicker and less computationally demanding evaluations, relevant to a variety of data forms, including images.
### LiTo: Surface Light Field Tokenization
In their study, Apple introduces an innovative 3D latent representation that encompasses both object geometry and view-dependent appearance. This method allows for capturing how light interacts with an object from various angles, reflecting realistic phenomena such as specular highlights and Fresnel reflections.
The researchers point out that earlier techniques primarily concentrated on either 3D geometry reconstruction or view-independent appearance, restricting their capacity to illustrate realistic view-dependent effects. By employing RGB-depth images to sample a surface light field, their model consolidates subsamples into a concise set of latent vectors, enabling a cohesive representation of geometry and appearance.
### Training LiTo
To train the model, the researchers utilized thousands of objects rendered from 150 distinct angles and three different lighting conditions. Instead of employing all the data at once, the system randomly chose small subsets to form a latent representation. The decoder was then trained to recreate the entire object and its appearance from these subsets.
The training methodology involved understanding how the object’s geometry and appearance fluctuate with varying viewing angles. Ultimately, a separate model was developed to take a single image and forecast its corresponding latent representation, allowing the decoder to recreate the complete 3D object.
### Comparison with Other Models
Apple’s research provides comparisons between their LiTo model and another model known as TRELLIS. The project page encompasses interactive side-by-side comparisons, highlighting the proficiency of LiTo in reconstructing 3D objects from single images.
For more information and to check out the interactive comparisons, you can visit the [project page](https://apple.github.io/ml-lito/index.html#recon-comparison) and view the full study [here](https://arxiv.org/abs/2603.11047).
