SShortSingh.
TopTechnologyAI & MLStartupsProgrammingScienceBusinessFinanceCrypto & Web3World
Back to feed
Programming

Quest 2.10 PTC adds theater mode and simpler launcher, pushing headsets toward daily use

0
·2 views

Meta's Horizon OS 2.10 PTC update is rolling out to Quest headsets with a new theater environment and a streamlined Navigator launcher. The article argues these changes signal that headsets are becoming comfortable media devices that people wear for longer periods. It then explores using that wearing time for vision training, noting that adherence has been the main obstacle for traditional methods like eye patches. The author profiles Amblyotube, a Quest app that uses dominant-eye blur, AI-based edge sharpening, and tracking-based alignment, delivered through videos users already watch. The piece stresses that the app is a supplementary training tool rather than a medical device, recommends 30 to 40 minutes daily for ages 13 and up, and advises consulting an eye specialist for vision symptoms.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

Assassin's Creed Shadows Runs on Ubisoft's Anvil Engine, Not Unreal or Snowdrop

Ubisoft built Assassin's Creed Shadows on an upgraded version of its in-house Anvil engine rather than Unreal Engine 5 or Snowdrop. The game is set in 16th-century Japan and follows shinobi Naoe and samurai Yasuke, launching in March 2025 on multiple platforms. It reportedly reached 5 million players by July 2025 and became the best-selling game in the U.S. that March. Anvil's lineage, dating to the first Assassin's Creed in 2007, was tuned for systemic gameplay and large open worlds, which Ubisoft says makes it easier to share across its studios. Shadows also introduces dynamic lighting that affects enemy vision, a seasonal world system, and runs only on ninth-generation consoles.

0 comments Read more at DEV Community
0
ProgrammingDEV Community ·

osFoundry Guide: Making Claude Answer From Its Knowledge Base With Article Citations

This guide explains how to connect Claude, either via claude.ai or Claude Code, to an osFoundry knowledge base so its answers cite the articles behind each claim. Each citation includes the article ID and last-updated time. Claude can also query osFoundry for changes since a prior answer, flagging answers that rest on articles since rewritten. With permission, Claude can edit an article, but only from the exact version it read. Setup involves creating a connection in osFoundry, adding the server URL, authenticating, and attaching a project.

0 comments Read more at DEV Community
0
ProgrammingDEV Community ·

Embodied AI Future: Robots Set to Merge Perception, World Models and Lifelong Learning

A STATERA Library series chapter outlines how next-generation embodied AI will combine advanced AI, robotics, world models, multimodal perception, autonomous decision-making and lifelong learning into one physical system. Unlike earlier robots that relied on fixed rules and controlled settings, these systems are expected to learn and adapt through continuous interaction with the real world. The piece argues that intelligence is inseparable from physical experience, with perception and action forming a continuous loop of observation, action and learning. It highlights large-scale world models that let robots predict outcomes before acting, along with vision-language-action models that can translate instructions directly into behavior. The material is presented in Korean as part of Volume 06 of the series on AMR AI and embodied intelligence.

0 comments Read more at DEV Community
0
ProgrammingDEV Community ·

Researchers Outline Path From Task-Specific Robots to General Embodied Intelligence

A STATERA Library chapter examines artificial general intelligence (AGI) as a central concept for the future of robotics. It contrasts today's robots, which are built for narrow tasks in controlled settings, with systems that could understand, reason, learn, and adapt across unpredictable real-world environments. The discussion highlights key enablers such as foundation models, world models, multimodal AI, reinforcement learning, and embodied AI. It also identifies challenges including generalization, common sense, long-term planning, memory, and lifelong learning. The chapter is part of a multi-volume series on AMR AI and embodied intelligence.

0 comments Read more at DEV Community