digitado

If you order groceries in India and they arrive in 10 minutes, there’s a dark store within 2–3 kilometres of your house. These are small, windowless warehouses — no walk-in customers, no signage, just pickers and riders. Blinkit, Zepto, and Swiggy Instamart collectively operate thousands of them across the country. I wanted the coordinates of every single one. Why? Partly curiosity about the infrastructure density of India’s Q-Commerce boom, partly a practical engineering challenge I wanted to document. […]

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Backtests often look far better on paper than they do in live markets. The usual reason is not that the idea was completely wrong. It is that the strategy was tuned too closely to the past, then judged on the same data it was trained on. That is exactly the problem we will address in this guide. We are going to build a complete walk-forward backtesting pipeline in Python using AAPL data from 2010 onward. The pipeline will […]

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The indie hacker hierarchy is bullshit, and I fell for it anyway. I bought another domain this week. I don’t actually need it. I already have a business that works, one that pays me every month, serves a real audience, and has been running for almost a decade. Wtf. But I bought it anyway because brainstorming ideas with Claude and browsing Namecheap at night kinda feels like building. I tweeted about this recently and the gist was that […]

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Automotive engineering development increasingly relies on heterogeneous 3D data, including finite element (FE) models, body-in-white (BiW) representations, CAD geometry, and CFD meshes. At the same time, engineering teams face growing pressure to shorten development cycles, improve performance and accelerate innovation. Although artificial intelligence (AI) is increasingly explored in this domain, many current methods remain task-specific, difficult to interpret, and hard to reuse across development stages. This paper presents a practical graph learning framework for 3D engineering AI, in […]

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arXiv:2604.06337v1 Announce Type: new Abstract: This paper first demonstrates that applying standard incremental nonlinear dynamic inversion (INDI) with incremental control allocation (ICA) to input nonaffine systems relies on an untenable linear approximation of the actuator model. It then shows that avoiding this issue, while retaining the static control allocation paradigm, generally requires solving a nonlinear programming (NLP) problem. To address the associated online computational challenges, the paper subsequently presents a supervised learning-based approach. Numerical experiments on an example […]

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arXiv:2604.06336v1 Announce Type: new Abstract: Graph Transformers have recently attracted attention for molecular property prediction by combining the inductive biases of graph neural networks (GNNs) with the global receptive field of Transformers. However, many existing hybrid architectures remain GNN-dominated, causing the resulting representations to remain heavily shaped by local message passing. Moreover, most existing methods operate at only a single structural granularity, limiting their ability to capture molecular patterns that span multiple molecular scales. We introduce BiScale-GTR, a […]

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arXiv:2604.06335v1 Announce Type: new Abstract: We develop a unified framework to characterize the power of higher-level algorithms for the constraint satisfaction problem (CSP), such as $k$-consistency, the Sherali-Adams LP hierarchy, and the affine IP hierarchy. As a result, solvability of a fixed-template CSP or, more generally, a Promise CSP by a given level is shown to depend only on the polymorphism minion of the template. Similarly, we obtain a minion-theoretic description of $k$-consistency reductions between Promise CSPs. We […]

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arXiv:2604.06333v1 Announce Type: new Abstract: Drifting models generate high-quality samples in a single forward pass by transporting generated samples toward the data distribution using a vector valued drift field. We investigate whether this procedure is equivalent to optimizing a scalar loss and find that, in general, it is not: drift fields are not conservative – they cannot be written as the gradient of any scalar potential. We identify the position-dependent normalization as the source of non-conservatism. The Gaussian […]

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arXiv:2604.06332v1 Announce Type: new Abstract: Autonomous highway driving, especially for long-haul heavy trucks, requires detecting objects at long ranges beyond 500 meters to satisfy braking distance requirements at high speeds. At long distances, vehicles and other critical objects occupy only a few pixels in high-resolution images, causing state-of-the-art object detectors to fail. This challenge is compounded by the limited effective range of commercially available LiDAR sensors, which fall short of ultra-long range thresholds because of quadratic loss of […]

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