digitado

Vercel powers more than 4 million websites and processes 30 billion requests every week. That scale makes it an attractive target. On April 19, 2026, a ShinyHunters-affiliated threat actor claimed a breach of Vercel’s internal systems not by attacking Vercel’s platform directly, but by stealing OAuth credentials from a third-party AI tool connected to Vercel’s Google Workspace. The attacker is now demanding a $2 million ransom and has listed the alleged data on BreachForums. This article breaks down […]

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Data scarcity remains a fundamental bottleneck in applying deep learning to wireless communication problems, particularly in scenarios where collecting labeled Radio Frequency (RF) data is expensive, time-consuming, or operationally constrained. This paper proposes LLM-AUG, a data augmentation framework that leverages in-context learning in large language models (LLMs) to generate synthetic training samples directly in a learned embedding space. Unlike conventional generative approaches that require training task-specific models, LLM-AUG performs data generation through structured prompting, enabling rapid adaptation in […]

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Hello everyone! I’ve been independently researching & developing small-but-powerful vision-language models (VLMs) and noticed a gap in visual datasets – none were teaching my model to simply ground text in imagery, but trying to get it to reason about the text or about the scene itself. This lead me down a 2 week side-side-project to create SGOCR, an open source dataset pipeline for generating spatially-grounded, OCR-focused VQA tuples with tons of rich metadata to support diverse VLM training […]

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Reinforcement learning (RL) has become a prevalent paradigm for training tool calling agents, which typically requires online interactive environments. Existing approaches either rely on training data with ground truth annotations or require advanced commercial language models (LMs) to synthesize environments that keep fixed once created. In this work, we propose TRUSTEE, a data-free method training tool calling agents with dynamic environments fully simulated by free open-source LMs that can be as small as 8B, including task generation, user […]

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TIL: SQL functions in Google Sheets to fetch data from Datasette I put together some notes on patterns for fetching data from a Datasette instance directly into Google Sheets – using the importdata() function, a “named function” that wraps it or a Google Apps Script if you need to send an API token in an HTTP header (not supported by importdata().) Here’s an example sheet demonstrating all three methods. Tags: spreadsheets, datasette, google

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Predictions from machine learning algorithms can vary across random seeds, inducing instability in downstream debiased machine learning estimators. We formalize random seed stability via a concentration condition and prove that subbagging guarantees stability for any bounded-outcome regression algorithm. We introduce a new cross-fitting procedure, adaptive cross-bagging, which simultaneously eliminates seed dependence from both nuisance estimation and sample splitting in debiased machine learning. Numerical experiments confirm that the method achieves the targeted level of stability whereas alternatives do not. […]

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