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Release: asyncinject 0.7 I built this utility library to support an asyncio dependency injection pattern a few years ago. I was using it with Datasette and Claude Fable 5 spotted some bugs in the dependency which it then fixed for me. It’s a very proactive model! Tags: async, projects, python, claude-mythos

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AI data centers are straining local grids. Here’s why hyperscalers should follow a Bring Your Own Power mandate.

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BitDive generates regression tests from real Java runtime traces using its own small local AI model. Instead of sending code, SQL queries, HTTP payloads, and business data to cloud LLMs, BitDive runs locally on the developer’s machine. The model does not rely on prompts or token-based APIs — it converts captured runtime behavior directly into unit and integration tests, keeping data private and eliminating token costs.

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The decision comes as India emerges as the world’s largest GCC market.

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arXiv:2606.11911v1 Announce Type: new Abstract: Persistence diagrams are common representations in topological data analysis, but they do not naturally live in a vector space, and the statistical tools developed for comparing them have largely evolved separately from those used for downstream prediction. We introduce STRAND (Survival Topological Representation ANalysis of Diagrams), which treats (collections of) PDs as survival data: each topological feature with persistence value $p = d – b$ is a fully observed time-to-event, and the persistence […]

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arXiv:2606.11865v1 Announce Type: new Abstract: Conformal Bayes combines Bayesian posterior predictives with conformal calibration to produce prediction sets that are both statistically valid and geometrically efficient. We study conformal Bayes under label shift from a unified perspective, identifying two complementary approaches that restore nominal target-domain coverage through importance-weighted conformal calibration but operate through independent mechanisms. emph{Post-hoc calibration} tilts the posterior predictive toward the target domain and corrects the conformal threshold via an importance-weighted quantile, leaving the parameter posterior […]

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arXiv:2606.11738v1 Announce Type: new Abstract: We study online estimation for high-dimensional generalized linear models with streaming data. First, for the non-distributed setting, we propose a gradient-enhanced surrogate loss that approximates the cumulative loss using only historical summaries, which modifies and improves upon the existing renewable estimation approach for the same model in the high-dimensional setting, and removes the batch-number constraint in previous studies. We then extend the method to distributed streaming data under the master-client architecture, where batches […]

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arXiv:2606.11570v1 Announce Type: new Abstract: We propose a spectral-based, unsupervised representation learning framework to derive low-dimensional embeddings for clinical concepts and patients in rare disease cohorts from electronic health records, where data are high-dimensional but sample sizes are limited. To overcome this challenge, we incorporate a knowledge matrix extracted from a broader population that shares a partially overlapping subspace with the rare-disease cohort. Our method departs from existing approaches by relaxing restrictive one-to-one signal-alignment assumptions between the latent […]

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