There is a distinct lifecycle when developers build with LLMs. Phase one is pure awe: you ask a model to write a Python script or debug an async function, and it spits out working code in seconds. Phase two is ambition: you try to move past simple chat windows and build an autonomous AI agent to handle complex, multi-step workflows. Then you hit phase three: the data brick wall. :::info You realize that while an LLM can reason […]

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arXiv:2601.03397v1 Announce Type: new Abstract: We present PIVONet (Physically-Informed Variational ODE Neural Network), a unified framework that integrates Neural Ordinary Differential Equations (Neuro-ODEs) with Continuous Normalizing Flows (CNFs) for stochastic fluid simulation and visualization. First, we demonstrate that a physically informed model, parameterized by CNF parameters {theta}, can be trained offline to yield an efficient surrogate simulator for a specific fluid system, eliminating the need to simulate the full dynamics explicitly. Second, by introducing a variational model with […]

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Let me tell you about a code review I sat through two years into my engineering career. I had written a solution that worked. I had written a solution that worked out. It changed into readable, it turned into fast, and it solved the exact trouble we had. My senior engineer looked at it and said a few words I still think about: “That’s not best practice.” No explanation. No alternative. Just the verdict. I rewrote it the […]

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Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]

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arXiv:2603.20236v1 Announce Type: new Abstract: Recent advances in unimanual manipulation policies have achieved remarkable success across diverse robotic tasks through abundant training data and well-established model architectures. However, extending these capabilities to bimanual manipulation remains challenging due to the lack of bimanual demonstration data and the complexity of coordinating dual-arm actions. Existing approaches either rely on extensive bimanual datasets or fail to effectively leverage pre-trained unimanual policies. To address this limitation, we propose textbf{EnergyAction}, a novel framework that […]

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In high-stakes environments, artificial intelligence has already become a necessity in safety systems. In particular, healthcare has quickly adopted AI-powered systems into its clinical workflows, giving rise to innovative tools such as medical speech recognition. These tools enable live speech documentation during patient visits, reducing the administrative burden on medical staff and supporting faster, more accurate decision-making. The real value is clear: AI delivers the most value when it is a critical part of infrastructure, not merely a […]

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Email marketing only works if messages reach the inbox. Deliverability testing identifies spam triggers, broken links, and authentication issues. Tools like MailGenius help marketers optimize campaigns, improve engagement, protect sender reputation, and maximize ROI by ensuring emails are seen, not lost to spam or promotions tabs.

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Plaud is trying to make a mark in a crowded market full of AI-powered meeting notetakers.

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We present THEIA, a modular neural architecture that learns complete Kleene three-valued logic (K3) end-to-end without any external symbolic solver, and investigate what architectural prior enables compositional generalization under uncertainty. THEIA processes four mathematical domains (arithmetic, order, set membership, propositional logic) through dedicated engines that converge in a final logic module. Trained on a 2M-sample dataset with input space ~3.4 x 10^13, it achieves 12/12 Kleene K3 rule coverage across 5 seeds in 7.93 +/- 1.40 minutes (6.5x […]

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