Prediction Market Philosophers Got What They Wanted. Getting the future right is now big business. But at a festival in the Bay Area, forecasters worry that sports markets could take the whole industry down. On June 11, Kalshi released a buzzy ad featuring noted New York Knicks fan Timothée Chalamet. It was a zeitgeist-capturing moment for prediction markets, akin to the 2022 Super Bowl, when seemingly every commercial featured a celebrity shilling crypto.

Leaked files show the invite-only network grades members by their money and fame, shaping who's in, who's out, and who pays. Dialog, the private network cofounded by Peter Thiel, grades its event attendees on a hidden scale, ranking them by wealth and fame, tracking their relationships, and using algorithms to help decide who they should meet, who they should sit with, and who no longer belongs, WIRED has learned. The records are part of a trove of internal data received by WIRED from a confidential source, containing the personal information of nearly 200 prominent people scheduled to attend the group's annual retreat this summer. The data includes home addresses, private phone numbers and email accounts, dates of birth, photos, and emergency contacts, as well as food allergies and the political leanings volunteered by some members. The records are distinct from a list of people affiliated with Dialog that was left exposed on the organization's website and has been circulating online since earlier this week--a looser directory that appears to include nonmembers, such as Maryland governor Wes Moore, a former event speaker, and other outside guests who passed through Dialog's orbit, in some cases years ago.

Evie is a longtime favorite of far-right. At its very first live event, the strength of the publication's politics was in the pretense that it doesn't have any. Just after 8:00 pm on Sunday night, Evie Magazine's first live event was finally getting started. The women's magazine, which was founded in 2019 and once described itself as a " conservative Cosmo," welcomed eager fans to celebrate the publication, generally, and its new issue, specifically, during New York Fashion Week at the Standard Hotel's Boom in Chelsea. Guests lined up outside, hugging fur coats around formal dresses, as hosts scanned a list for their names. One blonde woman begged for access to the VIP section; an event planner ran downstairs to tell her coworkers that someone's hair had caught on fire.

No Phone, No Social Safety Net: Welcome to the'Offline Club' Across Europe's largest cities, people are gathering for semi-silent, offline hangouts, in search of an experience that isn't mediated through their smartphones. On cue, the room fell silent. A man seated to my left at a long wooden table began to scratch at a piece of paper with a coloring pencil. To my right, another guy picked up a book. Across the way, someone buried themselves in a puzzle.

They're starting to dominate entire sectors of AI and robotics. I decided to go to CES kind of at the last minute. Over the holiday break, contacts from China kept messaging me about their travel plans. After the umpteenth "See you in Vegas?" As a China tech writer based in the US, I have one week a year when my entire beat seems to come to me--no 20-hour flights required. CES, the Consumer Electronics Show, is the world's biggest tech show, where companies launch new gadgets and announce new developments, and it happens every January.

At New Zealand's Kawaiican cybersecurity convention, organizers hacked together a way for attendees to track CO levels throughout the venue--even before they arrived. Hacker conferences--like all conventions--are notorious for giving attendees a parting gift of mystery illness. To combat "con crud," New Zealand's premier hacker conference, Kawaiicon, quietly launched a real-time, room-by-room carbon dioxide monitoring system for attendees. To get the system up and running, event organizers installed DIY CO monitors throughout the Michael Fowler Centre venue before conference doors opened on November 6. Attendees were able to check a public online dashboard for clean air readings for session rooms, kids' areas, the front desk, and more, all before even showing up. It's ALMOST like we are all nerds in a risk-based industry, the organizers wrote on the convention's website.

Generative AI assistants offer significant potential to enhance productivity, streamline information access, and improve user experience in enterprise contexts. In this work, we present Summit Concierge, a domain-specific AI assistant developed for Adobe Summit. The assistant handles a wide range of event-related queries and operates under real-world constraints such as data sparsity, quality assurance, and rapid deployment. To address these challenges, we adopt a human-in-the-loop development workflow that combines prompt engineering, retrieval grounding, and lightweight human validation. We describe the system architecture, development process, and real-world deployment outcomes. Our experience shows that agile, feedback-driven development enables scalable and reliable AI assistants, even in cold-start scenarios.

Large Language Models (LLMs) have shown strong reasoning capabilities, with models like OpenAI's O-series and DeepSeek R1 excelling at tasks such as mathematics, coding, logic, and puzzles through Reinforcement Learning with Verifiable Rewards (RLVR). However, their ability to solve more complex optimization problems - particularly NP-hard tasks - remains underexplored. To bridge this gap, we propose NP-ENGINE, the first comprehensive framework for training and evaluating LLMs on NP-hard problems. NP-ENGINE covers 10 tasks across five domains, each equipped with (i) a controllable instance generator, (ii) a rule-based verifier, and (iii) a heuristic solver that provides approximate optimal solutions as ground truth. This generator-verifier-heuristic pipeline enables scalable and verifiable RLVR training under hierarchical difficulties. We also introduce NP-BENCH, a benchmark derived from NP-ENGINE-DATA, specifically designed to evaluate LLMs' ability to tackle NP-hard level reasoning problems, focusing not only on feasibility but also on solution quality. Additionally, we present QWEN2.5-7B-NP, a model trained via zero-RLVR with curriculum learning on Qwen2.5-7B-Instruct, which significantly outperforms GPT-4o on NP-BENCH and achieves SOTA performance with the same model size. Beyond in-domain tasks, we demonstrate that RLVR training on NP-ENGINE-DATA enables strong out-of-domain (OOD) generalization to reasoning tasks (logic, puzzles, math, and knowledge), as well as non-reasoning tasks such as instruction following. We also observe a scaling trend: increasing task diversity improves OOD generalization. These findings suggest that task-rich RLVR training is a promising direction for advancing LLM's reasoning ability, revealing new insights into the scaling laws of RLVR.

On July 21 at 9 am PT, Anthropic retired Claude 3 Sonnet, a lightweight model known for being quick and cost-effective. On Saturday, in a large warehouse in San Francisco's SOMA district, more than 200 people gathered to mourn its passing. The star-studded funeral was put on by a group of Claude fanatics and Gen Z founders, one of whom told me he dropped out of college after learning about artificial general intelligence. Attendees included Amanda Askell, an Anthropic researcher who has jokingly called herself the "Fairy Claudemother," staffers from Anthropic and OpenAI, and high-profile X posters including the writer Noah Smith. The warehouse was dimly lit, with a tentacle from a shoggoth (a fictional H.P. Lovecraft creature that's become a popular metaphor for AI models) hanging from the ceiling.

Online services often require users to agree to lengthy and obscure Terms of Service (ToS), leading to information asymmetry and legal risks. This paper proposes TOSense-a Chrome extension that allows users to ask questions about ToS in natural language and get concise answers in real time. The system combines (i) a crawler "tos-crawl" that automatically extracts ToS content, and (ii) a lightweight large language model pipeline: MiniLM for semantic retrieval and BART-encoder for answer relevance verification. To avoid expensive manual annotation, we present a novel Question Answering Evaluation Pipeline (QEP) that generates synthetic questions and verifies the correctness of answers using clustered topic matching. Experiments on five major platforms, Apple, Google, X (formerly Twitter), Microsoft, and Netflix, show the effectiveness of TOSense (with up to 44.5% accuracy) across varying number of topic clusters. During the demonstration, we will showcase TOSense in action. Attendees will be able to experience seamless extraction, interactive question answering, and instant indexing of new sites.