Introduction When I started exploring Retrieval-Augmented Generation (RAG) systems for incident analysis, I realized that jumping straight into paid APIs like Claude or OpenAI wasn’t practical for learning and experimentation. Instead, I wanted to build something completely local, free to run, and powerful enough to handle real production scenarios. This article documents my journey building a fully functional RAG system that analyzes production incidents by learning from past issues — without spending a dime on API calls. Everything runs on […]

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arXiv:2603.06716v1 Announce Type: new Abstract: Eating is a daily challenge for over 60 million adults with essential tremors and other mobility limitations. For these users, traditional utensils like forks or spoons are difficult to manipulate — resulting in accidental spills and restricting the types of food that can be consumed. Prior work has developed rigid, hand-held utensils that often fail to secure food, as well as soft, shape-changing utensils made strictly for robot-assisted feeding. To assist a broader […]

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Agentic Engineering Patterns > LLMs are restricted by their context limit – how many tokens they can fit in their working memory at any given time. These values have not increased much over the past two years even as the LLMs themselves have seen dramatic improvements in their abilities – they generally top out at around 1,000,000, and benchmarks frequently report better quality results below 200,000. Carefully managing the context such that it fits within those limits is […]

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Hiring talented people with the right attitude and right skills is not enough, they also need the right environment to thrive—are leaders in the organization only focused on business deliverables or do they also invest in their team’s learning and growth? Do they encourage people to speak up and share their disagreements or do they shut down voices that differ with their own perspective? Are people in the team clear on their priorities and aligned with a long-term […]

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Second-order optimizers can significantly accelerate large-scale training, yet their naive federated variants are often unstable or even diverge on non-IID data. We show that a key culprit is emph{preconditioner drift}: client-side second-order training induces heterogeneous emph{curvature-defined geometries} (i.e., preconditioner coordinate systems), and server-side model averaging updates computed under incompatible metrics, corrupting the global descent direction. To address this geometric mismatch, we propose texttt{FedPAC}, a emph{preconditioner alignment and correction} framework for reliable federated second-order optimization. texttt{FedPAC} explicitly decouples parameter […]

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arXiv:2406.14399v3 Announce Type: replace-cross Abstract: The development of Time-Series Forecasting (TSF) models is often constrained by the lack of comprehensive datasets, especially in Global Station Weather Forecasting (GSWF), where existing datasets are small, temporally short, and spatially sparse. To address this, we introduce WEATHER-5K, a large-scale observational weather dataset that better reflects real-world conditions, supporting improved model training and evaluation. While recent TSF methods perform well on benchmarks, they lag behind operational Numerical Weather Prediction systems in capturing […]

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arXiv:2601.20992v1 Announce Type: new Abstract: We propose several improvements to the speech recognition evaluation. First, we propose a string alignment algorithm that supports both multi-reference labeling, arbitrary-length insertions and better word alignment. This is especially useful for non-Latin languages, those with rich word formation, to label cluttered or longform speech. Secondly, we collect a novel test set DiverseSpeech-Ru of longform in-the-wild Russian speech with careful multi-reference labeling. We also perform multi-reference relabeling of popular Russian tests set and […]

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arXiv:2602.08723v1 Announce Type: cross Abstract: Training data reconstruction from KKT conditions has shown striking empirical success, yet it remains unclear when the resulting KKT equations have unique solutions and, even in identifiable regimes, how to reliably recover solutions by optimization. This work hereby focuses on these two complementary questions: identifiability and optimization. On the identifiability side, we discuss the sufficient conditions for KKT system of two-layer networks with polynomial activations to uniquely determine the training data, providing a […]

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