Working extensively with AWS credentials in Kubernetes this quarter revealed how often credential precedence causes configuration issues. While the AWS SDK’s credential chain is well-designed, understanding the priority order is crucial for production deployments. Here’s what I’ve learned. The Problem Nobody Talks About A recent incident illustrated this well: We configured IRSA for a microservice, validated it in staging, and deployed to production successfully. Two weeks later, an audit revealed the service was using broader IAM permissions than […]

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Objective: The objective of this study is to develop a machine learning (ML)-based framework for early risk stratification of clinical trials (CTs) according to their likelihood of exhibiting a high rate of dosing errors, using information available prior to trial initiation. Materials and Methods: We constructed a dataset from ClinicalTrials.gov comprising 42,112 CTs. Structured, semi-structured trial data, and unstructured protocol-related free-text data were extracted. CTs were assigned binary labels indicating elevated dosing error rate, derived from adverse event […]

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Key Takeaways Laravel Prompts provides a beautiful, user-friendly interface for command-line applications with zero dependencies The package offers multiple input types including text, password, select, multiselect, confirm, search, and progress bars Laravel 12 includes Prompts natively, making CLI interactions more intuitive and visually appealing Prompts automatically handles validation, error messages, and keyboard navigation Perfect for creating installation wizards, configuration tools, and interactive artisan commands Index Introduction to Laravel Prompts Understanding Laravel Prompts Components Statistics Available Prompt Types Practical […]

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arXiv:2603.08907v1 Announce Type: cross Abstract: We present a comprehensive ablation of nine finite-sample bound families for selective prediction with risk control, combining concentration inequalities (Hoeffding, Empirical Bernstein, Clopper-Pearson, Wasserstein DRO, CVaR) with multiple-testing corrections (union bound, Learn Then Test fixed-sequence) and betting-based confidence sequences (WSR). Our main theoretical contribution is Transfer-Informed Betting (TIB), which warm-starts the WSR wealth process using a source domain’s risk profile, achieving tighter bounds in data-scarce settings with a formal dominance guarantee. We prove […]

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We propose Bayesian optimal sequential prediction as a new principle for understanding in-context learning (ICL). Unlike interpretations framing Transformers as performing implicit gradient descent, we formalize ICL as meta-learning over latent sequence tasks. For tasks governed by Linear Gaussian State Space Models (LG-SSMs), we prove a meta-trained selective SSM asymptotically implements the Bayes-optimal predictor, converging to the posterior predictive mean. We further establish a statistical separation from gradient descent, constructing tasks with temporally correlated noise where the optimal […]

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Intellectual property on the blockchain is supposed to be the future. Programmable licenses, instant provenance verification, transparent royalty splits – the pitch basically writes itself, but the developer tooling tells a different story entirely. Try registering an IP asset on Story Protocol today. You will format JSON metadata by hand. You will debug cryptic SDK error messages. You will burn testnet gas on failed transactions because nothing validated your inputs before hitting the chain. The whole process takes 15–30 minutes […]

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arXiv:2601.07061v4 Announce Type: replace Abstract: We introduce the setting of continuous index learning, in which a function of many variables varies only along a small number of directions at each point. For efficient estimation, it is beneficial for a learning algorithm to adapt, near each point $x$, to the subspace that captures the local variability of the function $f$. We pose this task as kernel adaptation along a manifold with noise, and introduce Local EGOP learning, a recursive […]

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Predicting extreme events in high-dimensional chaotic dynamical systems remains a fundamental challenge, as such events are rare, intermittent, and arise from transient dynamical mechanisms that are difficult to infer from limited observations. Accordingly, real-time forecasting calls for precursors that encode the mechanisms driving extremes, rather than relying solely on statistical associations. We propose a fully data-driven framework for long-lead prediction of extreme events that constructs interpretable, mechanism-aware precursors by explicitly tracking transient instabilities preceding event onset. The approach […]

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