Tootfinder

Workers at Google, TikTok, Adobe, Dropbox, CrowdStrike, and other tech firms recount how managers used AI to justify firing them, speed up their work, and more (Brian Merchant/Blood in the Machine)
https://www.bloodinthemachine.com/p/how-ai-is-killing-jo…

AI Killed My Job: Tech workers
Tech workers at TikTok, Google, and across the industry share stories about how AI is changing, ruining, or replacing their jobs.

AI-Driven Cytomorphology Image Synthesis for Medical Diagnostics
Jan Carreras Boada, Rao Muhammad Umer, Carsten Marr
https://arxiv.org/abs/2507.05063 https…

AI-Driven Cytomorphology Image Synthesis for Medical Diagnostics
Biomedical datasets often contain a large sample imbalance and are subject to strict privacy constraints, which together hinder the development of accurate machine learning models. One potential solution is to generate synthetic images, as this can improve data availability while preserving patient privacy. However, it remains difficult to generate synthetic images of sufficient quality for training robust classifiers. In this work, we focus on the classification of single white blood cells, a …

«I do understand: you want permission. There’s a machine in the corner wrapped in human skin that makes things out of shit and blood to look like whatever you want (as long as you don’t look too closely). […] You want to know you can use it sometimes without me thinking less of you. You don’t need me to believe it’s useful, you just want me to be polite about it. But I am a hater, and I will not be polite. The machine is disgusting and we should break it.»
/cc @…
https://anthonymoser.github.io/writing/ai/haterdom/2025/08/26/i-am-an-ai-hater.html

Deep Learning for Blood-Brain Barrier Permeability Prediction
Zihan Yang, Haipeng Gong
https://arxiv.org/abs/2507.18557 https://arxiv.org/pdf/2507.18557

Deep Learning for Blood-Brain Barrier Permeability Prediction
Predicting whether a molecule can cross the blood-brain barrier (BBB) is a key step in early-stage neuropharmaceutical development, directly influencing both research efficiency and success rates in drug discovery. Traditional empirical methods based on physicochemical properties are prone to systematic misjudgements due to their reliance on static rules. Early machine learning models, although data-driven, often suffer from limited capacity, poor generalization, and insufficient interpretabili…

Reliable Noninvasive Glucose Sensing via CNN-Based Spectroscopy
El Arbi Belfarsi, Henry Flores, Maria Valero
https://arxiv.org/abs/2506.13819 https://

Reliable Noninvasive Glucose Sensing via CNN-Based Spectroscopy
In this study, we present a dual-modal AI framework based on short-wave infrared (SWIR) spectroscopy. The first modality employs a multi-wavelength SWIR imaging system coupled with convolutional neural networks (CNNs) to capture spatial features linked to glucose absorption. The second modality uses a compact photodiode voltage sensor and machine learning regressors (e.g., random forest) on normalized optical signals. Both approaches were evaluated on synthetic blood phantoms and skin-mimicking…