Codex Dynamic Workflows is a Codex CLI skill by DannyMac180 that turns large, ambiguous tasks into supervised AI-agent work. Instead of executing directly, the skill forces planning, delegation, and verification before anything risky happens.
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Open-source, model-agnostic platform for building and running autonomous coding agents. Operated by All Hands AI; CEO Robert Brennan, Chief Scientist Graham Neubig (also CMU LTI). Cloud is hosted at `app.all-hands.dev`; core repo is MIT-licensed and community-maintained under the `OpenHands` GitHub
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Portent is an open specification for work and personal knowledge bases. It defines a small, opinionated vocabulary of note types, relationships, and lifecycle states so that knowledge bases become "easy for humans and agents to understand." Released under the MIT license by Luca Rossi (founder of Re
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If you sync your Obsidian vault through iCloud Drive, startup can be painfully slow because iCloud lazily downloads file contents only when something tries to read them. Obsidian scanning thousands of notes on launch triggers that download cascade.
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Out of the box, Obsidian does not include AI features. It fully relies on external AI applications and AI community plugins to provide AI support.
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A tool for detecting and removing watermarks added by AI image generators (like DALL-E, Midjourney, Stable Diffusion) to images.
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The Cost Of The Grain That Feeds Half The World Just Posted Biggest Monthly Surge Since 2008
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www.zerohedge.com
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2 days ago
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eng
The Cost Of The Grain That Feeds Half The World Just Posted Biggest Monthly Surge Since 2008 Asian rice prices logged their biggest monthly gain in nearly two decades in May, as a Gulf energy shock collides with an expected El Niño event later this year . The spike adds to the mounting risks of a broader food price shock that could emerge as soon as six months from now. Any time rice prices spike, it is a major concern because the gr....
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Potential Offshore Strike In Norway Could Add Fresh Uncertainty To Global Energy Markets As Wage Talks Collapse
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www.zerohedge.com
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2 days ago
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eng
Potential Offshore Strike In Norway Could Add Fresh Uncertainty To Global Energy Markets As Wage Talks Collapse By Michael Kern of OilPrice.com A potential strike over wages could threaten smooth operations offshore Norway, Western Europe's top oil and gas producer, at a time when the world is scrambling for oil and gas supply amid the Middle East crisis. Almost 8% of oil and gas workers offshore Norway could go on a strike....
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How Contagious Is Ebola? More than 200 people are suspected to have died in Ebola outbreaks in the Democratic Republic of the Congo and Uganda , according to the latest figures published by the Centers for Disease Control and Prevention on May 29. The vast majority of these are in the DRC. With no vaccine available for this strain, the World Health Organization declared a public health emergency of international concern on May 1....
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Britain's Nuclear Renaissance Faces Mounting Cost Pressures Authored by Felicity Bradstock via OilPrice.com, Sizewell C and Hinkley Point C are expected to play a major role in expanding Britain’s nuclear generation capacity and reducing dependence on fossil fuels. Both projects have faced concerns over delays and rising costs, with Hinkley Point C’s estimated price nearly doubling from its original forecast. The U.K.....
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I love this update, the pure passion of teenage love is so wonderful to behold. Hopefully there will...
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arXiv:2606.00128v1 Announce Type: new Abstract: Each year the American Statistical Association (ASA) hosts the Annual Data Challenge Expo, which tasks participants with analyzing a given dataset and presenting their work at the Joint Statistical Meeting (JSM). The 2025 Data Challenge Expo tasked participants with analyzing over 35 years of commercial flight data from the United States Bureau of Transportation Statistics (BTS). These data p....
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arXiv:2606.00157v1 Announce Type: new Abstract: We consider establishing the interpretability theory of deep learning through constructing a corresponding relationship between the renormalization group (RG) method in statistical physics and the training process of deep neural networks (DNNs). We have proved the constructed relationship using the one-dimensional Ising model as the input data. In this paper we generalize our results to the c....
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arXiv:2606.00181v1 Announce Type: new Abstract: We introduce a novel regression framework designed to model non-linear responses situated on a sphere $\mathbb{S}$ of finite or infinite dimension. Unlike traditional tangent-space regressions, which lift responses to a tangent space $T_o \mathbb{S}$ and thereby violate intrinsic spherical distances, our proposed method employs an intrinsic approach. We model the conditional mean through an i....
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On Asymptotic Outlier Rejection in Bayesian Mixed Poisson Regression Models Under Extreme Target and Covariate Values
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arxiv.org
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2 days ago
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eng
arXiv:2606.00231v1 Announce Type: new Abstract: Bayesian models are claimed to be fully robust against outliers if, asymptotically, observations infinitely far from the other data do not influence the posterior. Early works in robust Bayesian inference concentrated on continuous distributions and i.i.d. observations. Robustness results were then extended to linear regression in the presence of infinite residuals, either through an outlying....
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arXiv:2606.00233v1 Announce Type: new Abstract: Density estimation is often presented as a choice among parametric summaries, finite mixtures, and nonparametric smoothers. This review argues for a complementary view: a data set can be studied through a path of densities indexed by smoothing scale, diffusion time, model complexity, density level, or noise level. We call this perspective density evolution. Under this lens, Gaussian kernel de....
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Out-of-Distribution generalization of quantile regression with heavy tailed inputs: an SVM approach
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arxiv.org
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2 days ago
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eng
arXiv:2606.00265v1 Announce Type: new Abstract: We study quantile regression in an extrapolation regime where the covariate takes unusually large values. Under regular variation assumptions, extreme observations can be effectively characterized through their angular components, enabling learning strategies that focus on the angle of the most extreme observations. This approach is formalized through the minimization of an asymptotic conditi....
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arXiv:2606.00296v1 Announce Type: new Abstract: Neural operators are often reported to exhibit zero-shot super-resolution, a phenomenon in which a model trained on coarse grids produces accurate predictions on finer testing grids without additional retraining. Despite strong empirical evidence, the theoretical foundations of this phenomenon remain unclear. In this work, we provide a systematic theoretical study of zero-shot super-resolutio..
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arXiv:2606.00302v1 Announce Type: new Abstract: Despite being ubiquitous in science, clustering remains a technique whose results are not quantitatively scrutinized via a framework. We present an analysis called evaluating replicability via iterative clustering assignments (ERICA) that is applied to a dataset to determine whether clusters are identified in a replicable manner. The pipeline computes a statistic that describes whether struct..
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Cluster Analysis with Resampling for Validation and Exploration (CARVE)
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arxiv.org
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2 days ago
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eng
arXiv:2606.00327v1 Announce Type: new Abstract: Clustering is widely used across the sciences as the foundation for downstream data-driven scientific discoveries. However, clustering results are highly sensitive to the choice of algorithm, preprocessing, and the number of clusters $k$, producing scientific claims that are often not reproducible. The current state of the art for validating clustering solutions consists of clustering validat....
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arXiv:2606.00343v1 Announce Type: new Abstract: Motivated by the analysis of the behaviour of extremes from multivariate heavy-tailed distributions, we introduce a novel notion of statistical depth, referred to as Polar Depth. The polar depth function is naturally expressed in polar coordinates, as is the limiting distribution of a regularly varying random variable, beyond asymptotically large thresholds, once its marginals have been appro....
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arXiv:2606.00346v1 Announce Type: new Abstract: Phenomena such as epidemiological processes, hydrologic systems, social platforms, utility services, and supply chains can be represented as topological networks. A central question about these networks concerns connectivity and the permeability of edges. Dyadic regression and related approaches have been proposed to identify network features associated with pairwise node-level differences. I....
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A Distribution-Free Framework for Rewrite-Based Human-text Detection via Knockoff Filtering
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arxiv.org
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2 days ago
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eng
arXiv:2606.00402v1 Announce Type: new Abstract: We propose a distribution-free statistical framework that converts arbitrary rewrite-based detectors into detectors with finite-sample FDR guarantees without retraining. Our key observation is that rewrite-based detection implicitly constructs knockoff samples, enabling LLM-generated text detection to be formulated as a multiple hypothesis testing problem with knockoff structure. This perspec..
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Riemannian Stochastic Optimization for Sufficient Dimension Reduction
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arxiv.org
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2 days ago
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eng
arXiv:2606.00413v1 Announce Type: new Abstract: Sufficient dimension reduction (SDR) makes high-dimensional regression tractable by projecting the covariates onto a low-dimensional subspace that preserves the conditional mean of the response. Existing gradient-based estimators either operate in the ambient space and suffer from the curse of dimensionality, or localize in the reduced space at a per-outer-iteration cost at least quadratic in....
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arXiv:2606.00419v1 Announce Type: new Abstract: Uncertainty quantification (UQ) is critical for the deployment of machine learning predictors in real-world scenarios where the data distribution may shift over time (i.e., data may not be exchangeable). Online conformal prediction (OCP) methods address this issue at the expense of either (i) group-wise error control or (ii) learning-rate independent implementation. Group-conditional coverage....
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arXiv:2606.00425v1 Announce Type: new Abstract: Moment conditions are widely used to identify parameters in models where the full likelihood is either unknown or intentionally left unspecified. Empirical likelihood methods address this problem by assigning probability weights to the observed data so that the sample moment conditions hold exactly. Building on this idea, we propose a nonparametric Bayesian framework based on exponentially ti....
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