Over the last two years, something subtle has shifted in software engineering, and most of us have felt it even if we can’t name it. Teams report being dramatically more productive. Code is written faster, prototypes appear sooner, and the distance from idea to working system has collapsed. And yet, the software we rely on every day doesn’t feel proportionally better. In many cases, it feels harder to understand, harder to change, and more fragile than before. The […]

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Hey folks, My team and I are diving into RL training setups and want to chat with folks who have hands-on experience. Could share your process for picking an environment (e.g., Gym, custom sims) and getting it up and running? What pain points have you hit—like scaling, reward shaping, or integration issues—and what fixes made life easier? DMs open or reply below—happy to hop on a quick call! Thanks! submitted by /u/Popular_Piglet_1443 [link] [comments]

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Could you run an entire business from a markdown file? We’re about to find out. For five thousand years, the business ledger was a bulwark against the universe’s inevitable slide toward sludge. The merchants of Uruk pressed reeds into damp clay to count sheep; the bankers of Renaissance Genoa perfected double-entry bookkeeping to track the flow of florins. These records were static. They were memory made physical, a way to freeze the turbulent currents of commerce into legible […]

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Modern software development is experiencing rapid changes because AI now handles coding tasks that used to depend on human developers. The traditional development process depends on developers who write code while using basic automation tools to assist them. In contrast, the current software development model has evolved into a system that allows intelligent machines to create software without human assistance. Google Antigravity introduces a new type of system that uses autonomous AI systems, letting AI agents execute the […]

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arXiv:2603.06646v1 Announce Type: new Abstract: This paper presents a trust aware federated learning (FL) framework for interpreting bone healing stages using spectral features derived from frequency response data. The primary objective is to address the challenge posed by either unreliable or adversarial participants in distributed medical sensing environments. The framework employs a multi-layer perceptron model trained across simulated clients using the Flower FL framework. The proposed approach integrates an Adaptive Trust Score Scaling and Filtering (ATSSSF) mechanism with […]

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Recently, Automatic Speech Recognition (ASR) systems (e.g., Whisper) have achieved remarkable accuracy improvements but remain highly sensitive to real-world unseen data (data with large distribution shifts), including noisy environments and diverse accents. To address this issue, test-time adaptation (TTA) has shown great potential in improving the model adaptability at inference time without ground-truth labels, and existing TTA methods often rely on pseudo-labeling or entropy minimization. However, by treating model confidence as a learning signal, these methods may reinforce […]

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I just sent the January edition of my sponsors-only monthly newsletter. If you are a sponsor (or if you start a sponsorship now) you can access it here. In the newsletter for January: LLM predictions for 2026 Coding agents get even more attention Clawdbot/Moltbot/OpenClaw went very viral Kakapo breeding season is off to a really strong start New options for sandboxes Web browsers are the “hello world” of coding agent swarms Sam Altman addressed the Jevons paradox for […]

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arXiv:2602.03991v1 Announce Type: new Abstract: Given a fixed positive integer $k$ and a simple undirected graph $G = (V, E)$, the {em $k^-$-path partition} problem, denoted by $k$PP for short, aims to find a minimum collection $cal{P}$ of vertex-disjoint paths in $G$ such that each path in $cal{P}$ has at most $k$ vertices and each vertex of $G$ appears in one path in $cal{P}$. In this paper, we present a $frac {k+4}5$-approximation algorithm for $k$PP when $kin{9,10}$ and […]

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arXiv:2601.04608v1 Announce Type: cross Abstract: We study U.S. Treasury yield curve forecasting under distributional uncertainty and recast forecasting as an operations research and managerial decision problem. Rather than minimizing average forecast error, the forecaster selects a decision rule that minimizes worst case expected loss over an ambiguity set of forecast error distributions. To this end, we propose a distributionally robust ensemble forecasting framework that integrates parametric factor models with high dimensional nonparametric machine learning models through adaptive forecast […]

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Background: Pediatric dental disease remains one of the most prevalent and inequitable chronic health conditions worldwide. Although strong epidemiological evidence links oral health outcomes to socio-economic and demographic determinants, most artificial intelligence (AI) applications in dentistry rely on image-based diagnosis and black-box prediction models, limiting transparency and ethical applicability in pediatric populations. Objective: This study aimed to develop and evaluate an explainable machine learning framework for pediatric dental risk stratification that prioritizes interpretability, calibration, and ethical deployment over […]

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