January 2026

Documentation culture is about behavioral traits that technical writers practice every day based on set of beliefs and organizational values. Introduction A documentation culture reflects the behavioral traits practiced by everyone guided by shared beliefs and organizational values where writing is treated as a core part of their job role. Organization with strong documentation culture embeds writing into their cultural DNA whereby knowledge is captured, documented and more importantly leveraged. The defining characteristic of such a culture is […]

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The Invisible Wall in Your Codebase Imagine a new senior engineer joins your team. They are brilliant, experienced, and eager to push code. But for the first three weeks, their most common contribution is a question: “Hey, does anyone know why we used a custom hook here instead of a library?” or “Where is the doc explaining the database schema?” This is the Unsearchable Knowledge Problem. In most engineering organizations, knowledge exists in fragmented silos that don’t talk […]

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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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Physics-informed extreme learning machines (PIELMs) typically impose boundary and initial conditions through penalty terms, yielding only approximate satisfaction that is sensitive to user-specified weights and can propagate errors into the interior solution. This work introduces Null-Space Projected PIELM (NP-PIELM), achieving exact constraint enforcement through algebraic projection in coefficient space. The method exploits the geometric structure of the admissible coefficient manifold, recognizing that it admits a decomposition through the null space of the boundary operator. By characterizing this manifold […]

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Physics-informed extreme learning machines (PIELMs) typically impose boundary and initial conditions through penalty terms, yielding only approximate satisfaction that is sensitive to user-specified weights and can propagate errors into the interior solution. This work introduces Null-Space Projected PIELM (NP-PIELM), achieving exact constraint enforcement through algebraic projection in coefficient space. The method exploits the geometric structure of the admissible coefficient manifold, recognizing that it admits a decomposition through the null space of the boundary operator. By characterizing this manifold […]

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We invite you to read an article on how C++ is used in modern game development and why the industry is still not ready to move away from it. The author explores how C++ works at different levels of game engines and how performance requirements, legacy code, and platform constraints make an impact on the industry. We published and translated this article with the copyright holder’s permission. The author is Sergey Kushnirenko. I intended to write a follow-up […]

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arXiv:2512.12325v2 Announce Type: replace-cross Abstract: We prove that a classic sub-Gaussian mixture proposed by Robbins in a stochastic setting actually satisfies a path-wise (deterministic) regret bound. For every path in a natural “Ville event” $E_alpha$, this regret till time $T$ is bounded by $ln^2(1/alpha)/V_T + ln (1/alpha) + ln ln V_T$ up to universal constants, where $V_T$ is a nonnegative, nondecreasing, cumulative variance process. (The bound reduces to $ln(1/alpha) + ln ln V_T$ if $V_T geq ln(1/alpha)$.) If […]

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arXiv:2512.07074v2 Announce Type: replace-cross Abstract: Statistically correcting measured cross sections for detector effects is an important step across many applications. In particle physics, this inverse problem is known as unfolding. In cases with complex instruments, the distortions they introduce are often known only implicitly through simulations of the detector. Modern machine learning has enabled efficient simulation-based approaches for unfolding high-dimensional data. Among these, one of the first methods successfully deployed on experimental data is the OmniFold algorithm, a […]

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arXiv:2510.23463v3 Announce Type: replace-cross Abstract: Federated Learning (FL) is a distributed learning paradigm that preserves privacy by eliminating the need to exchange raw data during training. In its prototypical edge instantiation with underlying wireless transmissions enabled by analog over-the-air computing (AirComp), referred to as emph{over-the-air FL (AirFL)}, the inherent channel noise plays a unique role of emph{frenemy} in the sense that it degrades training due to noisy global aggregation while providing a natural source of randomness for privacy-preserving […]

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