Technology
Artificial intelligence, the tools built on it, and the innovation shaping what comes next.
Hackers can use 9 of the most popular AI tools to assemble massive botnets
Researchers have developed a new pull-based prompt injection attack called HalluSquatting, which can exploit nine popular AI coding tools to create massive botnets and perform large-scale DDoS attacks. This method leverages an LLM's tendency to hallucinate resource identifiers, allowing attackers to infect numerous devices indiscriminately by registering and seeding predicted identifiers with malicious instructions. This marks a significant advancement in prompt injection attacks, enabling widespread compromise with minimal effort.
Where Will SpaceX Be in 100 Years?
SpaceX aims to commercialize its Starship megarocket, which is crucial for its long-term goals. The company plans to launch orbital data centers and enhance its Starlink internet service to support the AI economy. Ultimately, SpaceX envisions establishing a presence on the Moon and other planets.
Auto-FL-Research: Agentic Search for Federated Learning Algorithms
Federated learning (FL) research involves numerous algorithmic choices that are challenging to explore manually. This work introduces Auto-FL-Research (AFR), an agent-based workflow designed to automate the search for optimal FL algorithmic recipes. AFR evaluates candidate training algorithms across various healthcare and grouped-client datasets, demonstrating gains in several tasks while also identifying seed-sensitive and search-selected failure cases.
The MMM Data Model -- A Normative Specification for Knowledge Interoperability in a Decentralisable Knowledge Commons
The MMM data model is introduced as a new approach to knowledge documentation, designed to overcome the limitations of traditional document-centric information systems. It combines normative constraints with free-text labels to facilitate interoperability across disciplines and applications without requiring semantic convergence. A reference implementation and pilot deployment data demonstrate its implementability and early usability.
Google’s AI buildout drove 37% increase in electricity use in 2025
Google's electricity consumption surged by 37% in 2025, the largest increase in its history, driven by the expansion of AI data centers. Despite this, the company claims to have reduced operational carbon emissions by purchasing significant amounts of clean energy. Google's total electricity usage has grown over 250% since 2019, with its data centers consuming more electricity than entire countries.
Recursive Self-Evolving Agents via Held-Out Selection
Researchers introduce RSEA, a Recursive Self-Evolving Agent that improves LLM agents by rewriting its strategy, skills, and playbook from its own trajectories. RSEA commits changes only if they do not regress on a held-out split, ensuring monotone-safe self-evolution. Across four benchmarks, RSEA demonstrates strong performance, particularly on ALFWorld, and highlights the importance of guarded context evolution.
When Does Personality Composition Matter for Multi-Agent LLM Teams?
Research explores how personality prompting in large language models (LLMs) affects multi-agent team performance across different tasks. It finds that the impact of personality traits, such as agreeableness, is highly dependent on the task structure. While low agreeableness has little effect on structured coding tasks, it significantly degrades performance in open-ended collaboration and competitive bargaining scenarios.
Anthropic's latest hiring spree reveals where it's building AI data centers next
Anthropic is expanding its AI compute capacity in the Asia-Pacific region, particularly in Australia and Japan, to meet the surging demand for its products. The company is actively hiring for numerous roles focused on developing and managing AI data centers in these countries. This expansion aims to address infrastructure strain caused by unprecedented consumer growth and leverage regions with abundant renewable energy potential and stable political environments.
AI-Model Network: Concept, Current State and Future
The rapid development of large language models (LMs) faces challenges in high training costs and deployment complexities, leading to a need for effective interaction and collaboration among diverse models. This paper proposes the concept of an "AI-Model Network" (AI-ModelNet), inspired by the Internet, to enable interconnection, capability sharing, and collaborative reasoning among AI models. The research outlines the vision, architecture, and feasibility of AI-ModelNet through a prototype system and application cases.