AI companies teaming up with some of the big retail stores is becoming a new trend. For those who still haven’t got why, the reason is pretty obvious: making users shop more smartly. However, the news of Google teaming up with Walmart isn’t just another partnership at all. Both companies are looking to revolutionize how people shop, and this time, it’s not just another AI experiment slapped onto an app. Soon, Gemini will make Walmart shopping easier than […]

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arXiv:2602.17726v1 Announce Type: new Abstract: In January 2026, torrential rains killed 200-300 people across Southern Africa, exposing a critical reality: 60% of the continent lacks effective early warning systems due to infrastructure costs. Traditional radar stations exceed USD 1 million each, leaving Africa with an 18x coverage deficit compared to the US and EU. We present a production-grade architecture for deploying NVIDIA Earth-2 AI weather models at USD 1,430-1,730/month for national-scale deployment – enabling coverage at 2,000-4,545x lower […]

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arXiv:2601.10861v1 Announce Type: new Abstract: The transition to electric vehicles (EVs) depends heavily on the reliability of charging infrastructure, yet approximately 1 in 5 drivers report being unable to charge during station visits due to inoperable equipment. While regulatory efforts such as the National Electric Vehicle Infrastructure (NEVI) program have established uptime requirements, these metrics are often simplistic, delayed, and fail to provide the diagnostic granularity needed by Charging Site Operators (CSOs). Despite their pivotal role in maintaining […]

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arXiv:2602.06154v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models scale large language models efficiently by sparsely activating experts, but once an expert is selected, it is executed fully. Hence, the trade-off between accuracy and computation in an MoE model typically exhibits large discontinuities. We propose Mixture of Slimmable Experts (MoSE), an MoE architecture in which each expert has a nested, slimmable structure that can be executed at variable widths. This enables conditional computation not only over which experts are […]

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The previous post talked about doing perfect shuffles: divide a deck in half, and alternately let one card from each half fall. It matters which half lets a card fall first. If the top half’s bottom card falls first, this is called an in-shuffle. If the bottom half’s bottom card falls first, it’s called an out-shuffle. With an out-shuffle, the top and bottom cards don’t move. Presumably it’s called an out-shuffle because the outside cards remain in place. […]

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arXiv:2603.06901v1 Announce Type: new Abstract: As machine learning (ML) systems increasingly shape access to credit, jobs, and other opportunities, the fairness of algorithmic decisions has become a central concern. Yet it remains unclear when enforcing fairness constraints in these systems genuinely improves outcomes for affected groups or instead leads to “leveling down,” making one or both groups worse off. We address this question in a unified, population-level (Bayes) framework for binary classification under prevalent group fairness notions. Our […]

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I’ve heard variations of this so many times: “What is this even for?”, “Why do we need it?”, “This isn’t my job — I’m not here to sit in tasks.” I’ve heard it both directly from developers and from project managers who constantly complained about their stubborn teams. And every time, it seemed to me that the real problem wasn’t Jira itself (it’s just a tool, even if not the most user-friendly one), but rather that PMs didn’t […]

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It’s been a long time sine I’ve written a blog post, and I miss it a lot! Today, I’ll be talking about an experiment I did 2 months ago when I was trying to optimize Blazor.Diagrams: JS Interop Batching. Context I’ve been working on my Diagramming Library for Blazor for quite some time now, and it basically takes a model (Diagram) that contains multiple nodes, ports and links (which are also models), renders it and makes it editable […]

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