With traditional goals such as stability and homeownership feeling out of reach, young people are focused on the present By now, we all know the shape of financial nihilism: the throw-your-money-at-anything reaction to the dissolution of the slow, linear path to wealth; the prediction markets, meme stocks, cryptocurrencies and microluxuries growing in its place. We’re left in an economic no man’s land. But financial nihilism only describes where we put our money. What about how we earn it in the first place? The modern economy has long relied on our need for stable employment – to pay the mortgage, to keep our kids fed, or, for some, to chase prestige and power – to generate the work ethic that keeps the wheel of production turning. None of this is going well for young people. Our jobs ar…

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Hugo Santana Aug 31, 2026 Some of the fastest-growing products today have one thing in common, they’re open source. Tools like PostHog, Supabase, n8n, Postiz, or Resend have supercharged their growth by being extremely…

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Google announces Gemini 3.7 Flash, Jalapeño’s first results show industry-leading speed, A Drone Killed Three Ukrainians. It Was Guided Entirely by A.I.

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Attestation-gated Key Release for Confidential Computing Workloads In Confidential Computing, all the data in your machine is protected while in use; the CPU uses hardware isolation to protect the registers and caches,…

(Credit: NUR Photo via Getty Images) AI agents are supposed to make our lives easier, but the buzzy OpenClaw agent recently deleted the emails of a Meta employee without permission. "Nothing humbles you like telling you…

OpenAI supports California SB 1119, advancing strong, age-appropriate AI safeguards for teens while preserving opportunities to learn, create, and explore.

Polimill uses OpenAI GPT models and Codex to help municipalities search and use administrative knowledge while accelerating development.

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Exclusive: Patients identify errors in consultation transcripts that are missed by GPs, Healthwatch England finds AI technology that listens to and transcribes patients’ consultations with doctors can put them at risk by getting the names of drugs and illnesses wrong, an NHS watchdog has warned. In one case a woman was left badly shaken when the AI scribe’s summary of her conversation wrongly said she had demyelination – serious nerve damage that can lead to multiple sclerosis. Continue reading...

Andrew Bailey warns G20 members about risk of cyber-disruption spreading ‘across jurisdictions’ The Bank of England’s governor, Andrew Bailey, has joined the throng of figures warning about the global risks posed by the most advanced artificial intelligence technology. In a two-page letter sent to international finance ministers and central bank governors as part of his role as chair of the international Financial Stability Board (FSB), Bailey said “frontier” AI models were “showing increasingly sophisticated autonomy and problem-solving abilities, as well as threat capabilities”. Continue reading...

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Blog: Scaling Memory Safety: AI-Assisted Rewrites of C/C++ Dependencies to Rust

Release & CIRelease notes v2026.8.1 (AKA OpenClaw 2.0) Native app availability: The signed and notarized macOS 2026.8.1 downloads are available as ZIP and DMG. New iOS and Android app distributions follow separately. Fo…

ChatGPT and similar large language models can produce compelling, humanlike answers to an endless array of questions – from queries about the best Italian restaurant in town to explaining competing theories about the na…

The OpenClaw Foundation has released v2026.8.1, which the project calls OpenClaw 2.0: 933 contributors, 569 first-timers, and more than 16,000 pull requests, roughly half of every PR ever merged into the repo. Setup now reuses existing subscriptions, API keys and local models. The rebuilt Control UI cut test-harness startup from about 1.6 seconds to 575 ms. Shared cloud sessions add real multiplayer, though the docs are explicit that they are not a security boundary. The post OpenClaw Releases OpenClaw 2.0: Guided Model Setup, 575 ms Control UI Startup, and One Trust Boundary Per Gateway appeared first on MarkTechPost.

The Open Air European Museum | The Princeton ToryThe Princeton Tory The Leading Princeton Publication of Conservative Thought The Open Air European Museum William Neumann /May 27, 2026 Europe may still look like a thriv…

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Hugo Duminil-Copin, Professor at Université de Genève and IHES Disclaimer. The effects of AI are vast, and there is much to be said about its broader impact on society. One should also remind that mathematicians have a…

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ChatGPT Ads reaches $1 billion in annualized revenue run rate and expands globally, supporting broader access to AI through free and affordable options.

arXiv:2608.28090v1 Announce Type: new Abstract: Humanoid robots with quasi-direct-drive actuators continuously generate joint torque while standing, whereas seated humans delegate weight support to chairs during desk work. As a first step toward seated loco-manipulation, we study omnidirectional seated locomotion on a passive mobile chair, requiring unfixed pelvis-seat contact and intermittent foot-floor propulsion of the robot-chair system. We extend a standard standing velocity-tracking environment with a passive-chair model, seated-state rewards, critic-only chair observations, and task-tailored contact settings. The policy is learned without motion-imitation rewards; its actor uses only proprioception and velocity commands, without contact sensing or chair states. In random-command ev…

arXiv:2608.28075v1 Announce Type: new Abstract: Manipulation in partially observable environments requires planning under incomplete scene information. In such settings, an initially valid plan may execute successfully yet remain insufficient for task completion. Existing foundation-model-guided task and motion planning (TAMP) systems can generate useful long-horizon task decompositions, subgoals, or constraints, but they often assume having access to a fully specified scene state or invoke model-level replanning after a subgoal, refinement, or execution attempt fails. We present ROBUST TAMP, a modular LLM/VLM-guided planning framework for reactive TAMP where unseen task-relevant and non-target objects may become visible during execution. The framework restricts the foundation-model plann…

arXiv:2608.27793v1 Announce Type: new Abstract: Autonomous navigation in underwater cave environments is essential for search-and-rescue operations, scientific exploration, and emergency egress. Traditional navigation systems commonly depend on dense visual features for localization and mapping. In underwater caves, however, visual degradation can undermine feature-based localization, sonar-based mapping may yield overly conservative obstacle representations, and communication constraints preclude real-time human guidance. To address these limitations, we propose an autonomous underwater cave navigation framework that leverages a vision-language model (VLM) with Chain-of-Thought (CoT) reasoning to infer navigable directions from environmental cues, including light intensity gradients, pas…

arXiv:2608.27726v1 Announce Type: new Abstract: Multi-agent motion planning for high-degree-of-freedom robotics manipulators in shared workspaces remains a fundamental yet challenging problem. Centralized planners often suffer from poor scalability, while decentralized approaches face robustness and safety concerns. Game-theoretic formulations offer a promising approach for modeling agent interactions, potentially overcoming these limitations. However, their application to articulated multi-arm systems remains limited. This paper presents an iterative Linear Quadratic (LQ) game framework for multi-manipulator motion planning, where each manipulator is modeled as an independent agent optimizing its own objective while interacting with other agents based on shared global states and collisio…

arXiv:2608.27685v1 Announce Type: new Abstract: Simultaneously optimizing the trajectories of multiple agents is a challenging problem plagued by nonlinearity, nonconvexity, and the curse of dimensionality. A collection of interacting aerial vehicles or self-driving cars in an intersection are examples of complex multi-agent systems that remain difficult to solve without many simplifying assumptions. The presence of communication latency between agents further increases the difficulty. In this paper, we analyze Distributed Model-Based Diffusion: a sampling-based Model-Predictive Control method suitable for highly nonlinear, nonconvex, nonsmooth, multi-agent systems. We prove contraction and robustness to latency for multi-agent, nonconvex problems, showing applicability to real-world cons…

arXiv:2608.27628v1 Announce Type: new Abstract: Subarctic regions have the potential to see increased deployment of autonomous robots in applications including forestry, mining, and environmental monitoring. In these conditions, an autonomous system's reliance on GNSS or cloud computing is precarious due to dense tree canopies and atmospheric attenuation, necessitating onboard sensing and data processing. However, established exteroceptive modalities, including cameras, lidars, and radars, are typically evaluated in structured urban settings or in environments that lack significant seasonal variations. To address this, we present a field report on a year-long deployment of a mobile robot in a subarctic boreal forest. We evaluate 64 km of data using nine odometry, localization, and mapping…

arXiv:2608.27609v1 Announce Type: new Abstract: Vision-language-action models (VLAs) have shown strong promise for general-purpose robotic manipulation by mapping language instructions and vision observations directly to actions. However, most VLAs primarily condition action prediction on current observations and lack an explicit mechanism for reasoning over future task dynamics, which is particularly important for fine-grained, contact-rich manipulation. We present PHR-VLA, a framework that enables planning-horizon reasoning in VLAs through privileged latent representations of future dynamics. PHR-VLA introduces a lightweight auxiliary future head that, during training, aligns the VLA's internal representations with latent dynamics extracted from future observations. Evaluation results d…