YouTuber "Schlep built a huge following tracking down alleged child predators on Roblox before being kicked off. The platform is facing multiple lawsuits over child safety. Last month, Kentucky attorney general Russell Coleman announced the details of yet another lawsuit against Roblox over suspected pedophiles lurking on the hugely popular gaming platform. While doing so, Coleman singled out the work of one self-described "predator hunter" who claims to have helped identify alleged abusers mixing with young gamers. "Roblox is even trying to silence those who raised these security risks," Coleman said. "The famous case of one of their developers, Schlep, immediately comes to mind." Schlep is in fact Michael, a 22-year-old Texan who has spent the last two years working with a group of other Roblox players to track down and identify people purportedly seeking to groom young children on the platform--predators like the one Schlep says allegedly groomed him a decade ago, which he says led ...

The personal information of users of the video game chat platform has been compromised. The personal information of users of the video game chat platform has been compromised. Video game chat platform tells users that driver's licences and passports were among the forms of data accessed via a third-party customer service provider Video game chat platform Discord has suffered a data breach, informing users that their personal information - including identity documents of those required to prove their age - were compromised. The company stated last week that an unauthorised party had compromised one of Discord's third-party customer service providers, leading to the access of "a limited number of users" who had been in contact with the customer service or trust and safety teams. The data compromised may have included usernames, email, billing information, the last four digits of credit card numbers, IP addresses and messages with customer support.

Generative AI, especially through large language models (LLMs), is transforming how technical knowledge can be accessed, reused, and extended. PETSc, a widely used numerical library for high-performance scientific computing, has accumulated a rich but fragmented knowledge base over its three decades of development, spanning source code, documentation, mailing lists, GitLab issues, Discord conversations, technical papers, and more. Much of this knowledge remains informal and inaccessible to users and new developers. To activate and utilize this knowledge base more effectively, the PETSc team has begun building an LLM-powered system that combines PETSc content with custom LLM tools -- including retrieval-augmented generation (RAG), reranking algorithms, and chatbots -- to assist users, support developers, and propose updates to formal documentation. This paper presents initial experiences designing and evaluating these tools, focusing on system architecture, using RAG and reranking for PETSc-specific information, evaluation methodologies for various LLMs and embedding models, and user interface design. Leveraging the Argonne Leadership Computing Facility resources, we analyze how LLM responses can enhance the development and use of numerical software, with an initial focus on scalable Krylov solvers. Our goal is to establish an extensible framework for knowledge-centered AI in scientific software, enabling scalable support, enriched documentation, and enhanced workflows for research and development. We conclude by outlining directions for expanding this system into a robust, evolving platform that advances software ecosystems to accelerate scientific discovery.

In July, Siyan, a UK-based Discord user, logged on one morning and found himself unable to access some of his text chats marked NSFW. The channel, a popup informed him, was now age-restricted. The United Kingdom had enacted its far reaching child safety laws, which includes an age requirement system to verify users are over 18. Discord's updates required users to verify their age, either by government ID or a face scan. Siyan (who requested to only be referred to by his screen name for privacy reasons) describes himself as "painfully over the age of needing to fake an ID." He didn't want to take a photo of his ID.

Roblox is rolling out new features aimed at making the platform safer for minors, including a revamped friend system, privacy tools, and age verification services users submit by recording a video selfie. In Roblox's old friend system, players have no distinction between people they know casually or online versus someone they consider a close friend. The platform's new tiered system introduces Connections and Trusted Connections specifically for people that players know and trust. To access Trusted Connections and its benefits, users first need to complete an age verification, which requires them to submit a video selfie. Once they've submitted their video, the company says it's run against an AI-driven "diverse dataset" to get an age estimation.

On a crisp Saturday in March, beneath a canopy of pixelated cherry blossoms, two avatars stood in front of a digital altar crafted from shimmering quartz blocks and flickering redstone torches. They were surrounded by a sprawling Minecraft village, complete with custom-coded NPCs reciting lore about the couple's decade-long digital courtship. Nearby, pixelated foxes darted between guests--each one logged in from across the world, dressed in custom skins as forest druids and rogue mages. After the vows (typed and read aloud on Discord), guests dispersed for side quests, scavenger hunts, and an enchanted maze culminating in a virtual fireworks show. This wasn't a rehearsal for an in-person wedding--this was the wedding.

NPCs in traditional games are often limited by static dialogue trees and a single platform for interaction. To overcome these constraints, this study presents a prototype system that enables large language model (LLM)-powered NPCs to communicate with players both in the game en vironment (Unity) and on a social platform (Discord). Dialogue logs are stored in a cloud database (LeanCloud), allowing the system to synchronize memory between platforms and keep conversa tions coherent. Our initial experiments show that cross-platform interaction is technically feasible and suggest a solid foundation for future developments such as emotional modeling and persistent memory support.

Roblox, Discord, OpenAI and Google are launching a nonprofit organization called ROOST, or Robust Open Online Safety Tools, which hopes "to build scalable, interoperable safety infrastructure suited for the AI era." The organization plans on providing free, open-source safety tools to public and private organizations to use on their own platforms, with a special focus on child safety to start. The press release announcing ROOST specifically calls out plans to offer "tools to detect, review, and report child sexual abuse material (CSAM)." Partner companies are providing funding for these tools, and the technical expertise to build them, too. The operating theory of ROOST is that access to generative AI is rapidly changing the online landscape, making the need for "reliable and accessible safety infrastructure" all the more urgent.

Human motion, inherently continuous and dynamic, presents significant challenges for generative models. Despite their dominance, discrete quantization methods, such as VQ-VAEs, suffer from inherent limitations, including restricted expressiveness and frame-wise noise artifacts. Continuous approaches, while producing smoother and more natural motions, often falter due to high-dimensional complexity and limited training data. To resolve this "discord" between discrete and continuous representations, we introduce DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow Decoding, a novel method that decodes discrete motion tokens into continuous motion through rectified flow. By employing an iterative refinement process in the continuous space, DisCoRD captures fine-grained dynamics and ensures smoother and more natural motions. Compatible with any discrete-based framework, our method enhances naturalness without compromising faithfulness to the conditioning signals. Extensive evaluations demonstrate that DisCoRD achieves state-of-the-art performance, with FID of 0.032 on HumanML3D and 0.169 on KIT-ML. These results solidify DisCoRD as a robust solution for bridging the divide between discrete efficiency and continuous realism. Our project page is available at: https://whwjdqls.github.io/discord.github.io/.

Major technology companies, including Google, Apple, and Discord, have been enabling people to quickly sign up to harmful "undress" websites, which use AI to remove clothes from real photos to make victims appear to be "nude" without their consent. More than a dozen of these deepfake websites have been using login buttons from the tech companies for months. A WIRED analysis found 16 of the biggest so-called undress and "nudify" websites using the sign-in infrastructure from Google, Apple, Discord, Twitter, Patreon, and Line. This approach allows people to easily create accounts on the deepfake websites--offering them a veneer of credibility--before they pay for credits and generate images. While bots and websites that create nonconsensual intimate images of women and girls have existed for years, the number has increased with the introduction of generative AI.