Say More Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Switch Off Creator Playbook Mashable Voices Trending Now Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List In My Bag All Series OpenClaw 2.0 is here: A Crowdsourced update to the AI agent is now live A major new version for the open source AI assistant platform OpenClaw is here. A new version of OpenClaw, the open-source AI agent, is available now. OpenClaw 2.0 was released over the weekend, promising a simpler setup and a new browser app. On its website, OpenClaw calls it by far the largest update in the history of OpenClaw. The OpenClaw Foundation says that OpenClaw 2.0 was completely crowdsourced with 933 contributors.

PCWorld reports that Microsoft's Windows 11 optional update KB5120981 cleans up Windows Search by removing cluttered Bing and MSN results, prioritizing more relevant local results instead. The update also improves typo handling, file categorization, and introduces gradual features like taskbar repositioning, making it a meaningful quality-of-life improvement. However, users should be aware of potential cursor and wallpaper bugs post-installation, so waiting may be wise if stability is a priority. A few days ago, Microsoft rolled out KB5120998, an optional update for Windows 11 that introduces several new features and improvements ( like the taskbar being repositionable again). Another noteworthy improvement is that the built-in Windows Search has been tidied up, meaning you'll no longer see results from Bing, Copilot, and MSN mixed in. Other search-related changes include: web searches no longer prioritizing ads and product suggestions, instead now always showing the most relevant result first; more clarity as to whether a search result is an app, a setting, a file, a web page, or a Microsoft Store recommendation; and the ability to cleanly handle typos and incomplete terms. Since KB5120998 is an optional update, you'll have to install it manually if you want to take advantage of its benefits. Otherwise, you can wait for it to release as part of September's big update.

There was a time--a more innocent age--when you could read an op-ed in a major newspaper and confidently say that a human had written it. Hold on to the memory of that for as long as you can. This week, after the billionaire investor Stanley Druckenmiller published an editorial in criticizing Treasury Secretary Scott Bessent, his onetime protégé, readers began to notice some familiar tics of AI writing. According to Pangram, the AI-detection tool, the op-ed was 100 percent AI-generated. That in itself was not so shocking. AI writing has regularly snuck into mainstream outlets, and especially newspaper op-ed pages, despite policies against it.

We argue that the machine learning value chain is structurally unsustainable due to an economic data processing inequality: each state in the data cycle from inputs to model weights to synthetic outputs refines technical signal but strips economic equity from data generators. We show, by analyzing seventy-three public data deals, that the majority of value accrues to aggregators, with documented creator royalties rounding to zero and widespread opacity of deal terms. This is not just an economic welfare concern: as data and its derivatives become economic assets, the feedback loop that sustains current learning algorithms is at risk. We identify three structural faults--missing provenance, asymmetric bargaining power, and nondynamic pricing--as the operational machinery of this inequality. In our analysis, we trace these problems along the machine learning value chain and propose an Equitable Data-Value Exchange (EDVEX) Framework to enable a minimal market that benefits all participants. Finally, we outline research directions where our community can make concrete contributions to data deals and contextualize our position with related and orthogonal viewpoints.

The meteoric rise of Artificial Intelligence (AI), with its rapidly expanding market capitalization, presents both transformative opportunities and critical challenges. Chief among these is the urgent need for a new, unified paradigm for trustworthy evaluation, as current benchmarks increasingly reveal critical vulnerabilities. Issues like data contamination and selective reporting by model developers fuel hype, while inadequate data quality control can lead to biased evaluations that, even if unintentionally, may favor specific approaches. As a flood of participants enters the AI space, this "Wild West" of assessment makes distinguishing genuine progress from exaggerated claims exceptionally difficult. Such ambiguity blurs scientific signals and erodes public confidence, much as unchecked claims would destabilize financial markets reliant on credible oversight from agencies like Moody's. In high-stakes human examinations (e.g., SAT, GRE), substantial effort is devoted to ensuring fairness and credibility; why settle for less in evaluating AI, especially given its profound societal impact? This position paper argues that a laissezfaire approach is untenable. For true and sustainable AI advancement, we call for a paradigm shift to a unified, live, and quality-controlled benchmarking framework--robust by construction rather than reliant on courtesy or goodwill.

Flood of AI'garbage' is pushing open-source developers to the limit A viral cartoon about open-source software shows a teetering pile of boxes labelled "all modern digital infrastructure" and one tiny box right at the bottom, propping up the whole lot: "a project some random person in Nebraska has been thanklessly maintaining since 2003". That's the reality of open source: every website, application and operating system relies on it. Modern society couldn't function without it, and yet it's written by volunteers in their spare time. But the growing burden caused by a flood of AI-generated code is causing many to burn out and leave the community altogether, threatening the future of open-source software. 'Flashes of brilliance and frustration': I let an AI agent run my day AI models are making it easier and easier to generate code to build new features, fix bugs or create entire new projects at the click of a button.

Most of the medical observational studies estimate the causal treatment effects using electronic health records (EHR), where a patient's covariates and outcomes are both observed longitudinally. However, previous methods focus only on adjusting for the covariates while neglecting the temporal structure in the outcomes. To bridge the gap, this paper develops a new method, SyncTwin, that learns a patient-specific time-constant representation from the pre-treatment observations. SyncTwin issues counterfactual prediction of a target patient by constructing a synthetic twin that closely matches the target in representation. The reliability of the estimated treatment effect can be assessed by comparing the observed and synthetic pre-treatment outcomes. The medical experts can interpret the estimate by examining the most important contributing individuals to the synthetic twin. In the real-data experiment, SyncTwin successfully reproduced the findings of a randomized controlled clinical trial using observational data, which demonstrates its usability in the complex real-world EHR.

Online social platforms increasingly rely on crowd-sourced systems to label misleading content at scale, but these systems must both aggregate users' evaluations and decide whose evaluations to trust. To address the latter, many platforms audit users by rewarding agreement with the final aggregate outcome, a design we term consensus-based auditing. We analyze the consequences of this design in X's Community Notes, which in September 2022 adopted consensus-based auditing that ties users' eligibility for participation to agreement with the eventual platform outcome. We find evidence of strategic conformity: minority contributors' evaluations drift toward the majority and their participation share falls on controversial topics, where independent signals matter most. We formalize this mechanism in a behavioral model in which contributors trade off private beliefs against anticipated penalties for disagreement. Motivated by these findings, we propose a two-stage auditing and aggregation algorithm that weights contributors by the stability of their past residuals rather than by agreement with the majority. The method first accounts for differences across content and contributors, and then measures how predictable each contributor's evaluations are relative to the latent-factor model. Contributors whose evaluations are consistently informative receive greater influence in aggregation, even when they disagree with the prevailing consensus. In the Community Notes data, this approach improves out-of-sample predictive performance while avoiding penalization of disagreement.

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