January 2026

It took Bernardo Quintero more than three decades to track down the ghost in the machine—the anonymous architect of a computer virus that inadvertently transformed the Spanish city of Málaga into a global cybersecurity capital. For Quintero, the founder of VirusTotal, the journey began not in a Google boardroom, but in a university computer lab in 1992. A benign piece of malware, known simply as the “Málaga Virus,” had swept through the Polytechnic School’s systems. When a professor […]

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I did not start my career thinking deeply about regulation. Like most tech-focused product managers, I cared about velocity, user experience, system reliability and scale. That instinct served me well—until I began leading platforms that moved money, made credit decisions and carried long-term financial consequences for customers. Regulation did not show up as paperwork, it showed up as questions the system would eventually be forced to answer. Regulation Is Delayed Interrogation I learned quickly that in banking, regulation […]

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If you’ve worked with Ethereum and then tried Solana, you probably felt confused by how different everything looks. The account model especially feels strange. But here’s the thing: it’s not just different for the sake of being different. Ethereum and Solana made completely opposite choices about how to organize data, and these choices matter a lot. Let’s break down what makes Solana’s account model unique and why it works the way it does. The Big Picture: Two Different […]

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Your chatbot confidently told a customer your return policy is 90 days. It’s 30.It later described features your product does not even have. That is the gap between a great demo and a real production system. Language models sound sure even when they are wrong, and in production that gets expensive fast. This is why serious AI teams use RAG. Not because it is trendy, but because it keeps models grounded in real information. What most people miss is […]

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I recently started doing software development work to expand and integrate AI systems. Here are some of the biggest surprises I found along the way. AI Developers Love Calling Everything “Fine Tuning” When studying for the AI-102 exam, I thought fine tuning strictly referred to additional training of a model to include additional data (which can be fed into the system in different ways). When actually working on an AI project, what I learned was developers (and architects) sprinkle […]

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arXiv:2511.11966v2 Announce Type: replace-cross Abstract: We study the problem of entropy calibration, which asks whether a language model’s entropy over generations matches its log loss on human text. Past work found that models are miscalibrated, with entropy per step increasing as generations grow longer, due to error accumulation. To calibrate the model and improve text quality, it has become standard practice to truncate the distribution, but this approach reduces output diversity, which we would like to avoid. Therefore, […]

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arXiv:2511.10859v2 Announce Type: replace-cross Abstract: One of the major bottlenecks for deploying popular first-order differentially private (DP) machine learning algorithms (e.g., DP-SGD) lies in their high computation and memory cost, despite the existence of optimized implementations. Zeroth-order methods have promise in mitigating the overhead, as they leverage function evaluations to approximate the gradients, hence significantly easier to privatize. While recent works have explored zeroth-order approaches in both private and non-private settings, they still suffer from relatively low utilities […]

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arXiv:2506.18124v3 Announce Type: replace-cross Abstract: Multiobject tracking (MOT) is an important task in applications including autonomous driving, ocean sciences, and aerospace surveillance. Traditional MOT methods are model-based and combine sequential Bayesian estimation with data association and an object birth model. More recent methods are fully data-driven and rely on the training of neural networks. Both approaches offer distinct advantages in specific settings. In particular, model-based methods are generally applicable across a wide range of scenarios, whereas data-driven MOT […]

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arXiv:2506.11010v2 Announce Type: replace-cross Abstract: This manuscript provides a systemic and data-centric view of what we term essential data science, as a natural ecosystem with challenges and missions stemming from the fusion of data universe with its multiple combinations of the 5D complexities (data structure, domain, cardinality, causality, and ethics) with the phases of the data life cycle. Data agents perform tasks driven by specific goals. The data scientist is an abstract entity that comes from the logical […]

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