digitado

Most communities fail because they’re not designed as a distribution growth engine. Instead, they’re built for engagement—likes, comments, and activity that look promising but don’t translate into measurable business impact. A distribution growth engine is a system where your community drives acquisition, conversion, and retention—turning engaged users into a scalable source of low-cost, high-trust growth. I’ve seen this play out repeatedly across startups and growth teams. Companies invest heavily in building communities, but without connecting them to distribution, […]

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Deep learning training is non-deterministic: identical code with different random seeds produces models that agree on aggregate metrics but disagree on individual predictions, with per-class AUC swings exceeding 20 percentage points on rare clinical classes. We present a framework for verified bit-identical training that eliminates three sources of randomness: weight initialization (via structured orthogonal basis functions), batch ordering (via golden ratio scheduling), and non-deterministic GPU operations (via architecture selection and custom autograd). The pipeline produces MD5-verified identical trained […]

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Most real estate investment trusts still run leasing and dividends through spreadsheets and human teams. They think the problem is slow paperwork or high fees. It is not. The real gap is blockchain underuse. REIT operators use less than 5% of what blockchain can do for trust and instant settlement. That is not a property management problem. It is a trust and latency problem — and no traditional process upgrade fixes an on-chain settlement problem. Here is the […]

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This tutorial shows you how to build a real-time medical transcription system that captures patient conversations, separates speakers, and automatically generates SOAP notes. You’ll create a streaming application that processes audio as it happens, giving healthcare providers instant transcription with proper clinical documentation format. You’ll use AssemblyAI’s Python SDK for real-time speech-to-text with multichannel speaker separation, combined with OpenAI’s GPT-4 API to transform raw transcripts into structured clinical notes. The implementation includes microphone audio capture, streaming transcription processing, […]

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Doginal Dogs Went From Free Claim to High-Value Hold Doginal Dogs is being talked about as one of the strangest success stories in NFTs for a simple reason: the collection started as a free mint and still managed to turn early holders into serious winners. According to the project’s own telling, minters paid nothing to claim. The founder covered the gas costs, and users were able to mint directly on Dogecoin through inscription technology. What looked like a […]

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When building a design system for the Android app at inDrive, we implemented a reusable TextArea component using androidx.compose.foundation.text.BasicTextField. One of the basic requirements was straightforward: limit the maximum number of characters a user can enter. Jetpack Compose seems to already support this through InputTransformation. However, after exploring the implementation more closely, we discovered a subtle behavior that can lead to a completely unusable TextField in certain scenarios. This article explores: how Jetpack Compose TextField max length works […]

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arXiv:2603.24771v2 Announce Type: replace-cross Abstract: Missing data is a ubiquitous challenge in data analysis, often leading to biased and inaccurate results. Traditional imputation methods usually assume that the missingness mechanism is missing-at-random (MAR), where the missingness is independent of the missing values themselves. This assumption is frequently violated in real-world scenarios, prompted by recent advances in imputation methods using deep learning to address this challenge. However, these methods neglect the crucial issue of nonparametric identifiability in missing-not-at-random (MNAR) […]

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arXiv:2603.24763v2 Announce Type: replace-cross Abstract: Conditional independence is central to modern statistics, but beyond special parametric families it rarely admits an exact covariance characterization. We introduce the binary expansion group intersection network (BEGIN), a distribution-free graphical representation for multivariate binary data and bit-encoded multinomial variables. For arbitrary binary random vectors and bit representations of multinomial variables, we prove that conditional independence is equivalent to a sparse linear representation of conditional expectations, to a block factorization of the corresponding […]

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arXiv:2601.16195v2 Announce Type: replace-cross Abstract: Machine-learned interatomic potentials (MLIPs) are increasingly used to replace computationally demanding electronic-structure calculations to model matter at the atomic scale. The most commonly used model architectures are constrained to fulfill a number of physical laws exactly, from geometric symmetries to energy conservation. Evidence is mounting that relaxing some of these constraints can be beneficial to the efficiency and (somewhat surprisingly) accuracy of MLIPs, even though care should be taken to avoid qualitative failures […]

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arXiv:2510.03587v2 Announce Type: replace-cross Abstract: Bayesian inference for doubly-intractable pairwise exponential graphical models typically involves variations of the exchange algorithm or approximate Markov chain Monte Carlo (MCMC) samplers. However, existing methods for both classes of algorithms require either perfect samplers or sequential samplers for complex models, which are often either not available, or suffer from poor mixing, especially in high dimensions. We develop a method that does not require perfect or sequential sampling, and can be applied to […]

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