PulseAugur / Brief
EN
LIVE 15:31:55

Brief

last 24h
[3/3] 222 sources

Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. TOOL · Mastodon — fosstodon.org English(EN) ·

    "Researchers from Carnegie Mellon University have developed a unique Wi-Fi configuration that allows them to estimate human movements through walls and dense ob

    Researchers at Carnegie Mellon University have created a novel Wi-Fi system capable of tracking human movement through walls and solid objects. This system utilizes inexpensive $30 Wi-Fi routers and receivers, eliminating the need for costly equipment like LiDAR or cameras. The development raises significant privacy concerns, as the widespread presence of home Wi-Fi routers could enable passive surveillance without consent. AI

    IMPACT This technology could enable new forms of surveillance and privacy invasion, impacting how individuals and organizations secure their spaces.

  2. RESEARCH · Wired — AI English(EN) · · [2 sources]

    I Gave My OpenClaw Agent a Physical Body

    An AI agent named OpenClaw was successfully integrated with a physical robot arm, enabling it to configure the arm, grasp objects, and even train another AI model for specific tasks. This development, utilizing an open-source robot arm and AI coding assistance, suggests a potential breakthrough in robotics by simplifying the control and training processes. Researchers are developing benchmarks like CaP-X to evaluate AI models' robotic capabilities, with Gemini showing promising results in multimodal understanding for physical world interactions. AI

    I Gave My OpenClaw Agent a Physical Body

    IMPACT Demonstrates AI's growing capability in physical robotics, potentially simplifying complex control and training tasks for broader adoption.

  3. SIGNIFICANT · Together AI blog (SW) ·

    Mamba-3

    Together AI has released Mamba-3, a new state space model (SSM) prioritizing inference efficiency over training speed. This model features a more expressive recurrence formula, complex-valued state tracking, and a multi-input, multi-output (MIMO) variant that enhances accuracy without sacrificing decoding speed. Mamba-3 SISO has demonstrated superior performance in prefill and decode latency compared to previous Mamba versions and even the Llama-3.2-1B Transformer model at the 1.5B parameter scale. The team has also open-sourced the model's kernels, developed collaboratively with researchers from Carnegie Mellon University, Princeton University, and Cartesia AI. AI

    IMPACT Sets a new benchmark for inference efficiency in state space models, potentially influencing future LLM architectures and deployment strategies.