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Relationships often fail from lack of structure, not lack of care. Here’s how behavioral design can make emotional effort more consistent.

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Search is shifting from link-based results to AI-generated answers. Modern AI search engines analyze intent, retrieve information through semantic retrieval, and generate responses using multiple sources. This article explains how AI search algorithms evaluate authority, credibility, and content depth-and why writers, developers, and marketers must rethink SEO strategies to remain visible in the age of generative search.

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Marketing teams were segmenting audiences based on who people are rather than what they do. Customer acquisition costs across digital channels have jumped 40 to 60 percent between 2023 and 2025.

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A real engineering build log on creating a cross-OS AI automation system that can operate desktop software reliably outside demo environments.

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Learn how to build a simple AI-assisted text-to-PDF converter using HTML, CSS, JavaScript, and jsPDF in the browser.

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Back in my student years, I had a book by a rather famous Dane about a rather famous programming language. And although I harbored genuine warm feelings for C++, I couldn’t get through even half of that book—the reading was that dreary. Well, it didn’t stop me from continuing to believe in the destiny fate had in store for me and C++. So, in the summer of 2011, I went to interview for a C++ developer. This is […]

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arXiv:2602.16537v2 Announce Type: replace-cross Abstract: We study online conformal prediction for non-stationary data streams subject to unknown distribution drift. While most prior work studied this problem under adversarial settings and/or assessed performance in terms of gaps of time-averaged marginal coverage, we instead evaluate performance through training-conditional cumulative regret. We specifically focus on independently generated data with two types of distribution shift: abrupt change points and smooth drift. When non-conformity score functions are pretrained on an independent dataset, we […]

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arXiv:2601.23236v2 Announce Type: replace-cross Abstract: We propose a variational framework that interprets transformer layers as iterations of an optimization algorithm acting on token embeddings. In this view, self-attention implements a gradient step of an interaction energy, while MLP layers correspond to gradient updates of a potential energy. Standard GPT-style transformers emerge as vanilla gradient descent on the resulting composite objective, implemented via Lie–Trotter splitting between these two energy functionals. This perspective enables principled architectural design using classical optimization […]

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arXiv:2512.17805v2 Announce Type: replace-cross Abstract: We develop a minimax theory for operator learning, where the goal is to estimate an unknown operator between separable Hilbert spaces from finitely many noisy input-output samples. For uniformly bounded Lipschitz operators, we prove information-theoretic lower bounds together with matching or near-matching upper bounds, covering both fixed and random designs under Hilbert-valued Gaussian noise and Gaussian white noise errors. The rates are controlled by the spectrum of the covariance operator of the measure […]

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arXiv:2512.12988v2 Announce Type: replace-cross Abstract: Mixture models are widely used in modeling heterogeneous data populations. A standard approach of mixture modeling assumes that the mixture component takes a parametric kernel form. In many applications, making parametric assumptions on the latent subpopulation distributions may be unrealistic, which motivates the need for nonparametric modeling of the mixture components themselves. In this paper, we study finite mixtures with nonparametric mixture components, using a Bayesian nonparametric modeling approach. In particular, it is […]

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