# yolo

Published articles for yolo.

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## Your agent's permission model stops at your home folder

DevFeed: [Your agent's permission model stops at your home folder](<https://devfeed.tech/articles/your-agent-s-permission-model-stops-at-your-home-folder-15991.md>)

Original publisher: [Read original article](<https://workos.com/blog/agent-permissions-blast-radius>)

Author: WorkOS

Published: 2026-09-02T00:00:00Z

Content type: opinion

Language: en

Sources: [WorkOS Blog](<https://devfeed.tech/sources/workos-blog.md>)

Topics: [AI-assisted coding](<https://devfeed.tech/topics/ai-assisted-coding.md>), [coding](<https://devfeed.tech/topics/coding.md>), [API](<https://devfeed.tech/topics/api.md>), [Command-line interface](<https://devfeed.tech/topics/cli.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agents](<https://devfeed.tech/tags/agents.md>), [api](<https://devfeed.tech/tags/api.md>), [coding-agents](<https://devfeed.tech/tags/coding-agents.md>), [developer](<https://devfeed.tech/tags/developer.md>), [permission](<https://devfeed.tech/tags/permission.md>), [yolo](<https://devfeed.tech/tags/yolo.md>)

### AI overview

This opinion article argues that coding-agent permission systems primarily protect local files and shell actions, while providing little business-aware control over API operations such as CRM changes. It examines approval fatigue, automatic approval modes, and the need for permission models that understand business impact.

### Source excerpt

Coding agents were built to guard a filesystem. The blast radius that matters now is in your CRM.

## PereStruct: Modular Pipeline and Dataset for Parsing Historical Newspapers

DevFeed: [PereStruct: Modular Pipeline and Dataset for Parsing Historical Newspapers](<https://devfeed.tech/articles/vlm-perestruct-24889.md>)

Original publisher: [Read original article](<https://habr.com/ru/companies/yandex/articles/1076770/>)

Author: makSShan (Яндекс, Yandex Cloud & Yandex Infrastructure)

Published: 2026-09-01T07:05:21Z

Content type: tutorial

Language: ru

Sources: [Яндекс - Как мы делаем Яндекс / Статьи](<https://devfeed.tech/sources/source.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [яндекс](<https://devfeed.tech/topics/tag-4004cf5948d3.md>), [vlm](<https://devfeed.tech/topics/vlm.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-studio](<https://devfeed.tech/tags/ai-studio.md>), [bleu](<https://devfeed.tech/tags/bleu.md>), [computervision](<https://devfeed.tech/tags/computervision.md>), [ocr](<https://devfeed.tech/tags/ocr.md>), [perestruct](<https://devfeed.tech/tags/perestruct.md>), [rouge](<https://devfeed.tech/tags/rouge.md>), [tag-4004cf5948d3](<https://devfeed.tech/tags/tag-4004cf5948d3.md>), [tag-65c8d6d9e736](<https://devfeed.tech/tags/tag-65c8d6d9e736.md>), [tag-9bf5e01ce62e](<https://devfeed.tech/tags/tag-9bf5e01ce62e.md>), [tag-d27a0708d400](<https://devfeed.tech/tags/tag-d27a0708d400.md>), [vision](<https://devfeed.tech/tags/vision.md>), [vlm](<https://devfeed.tech/tags/vlm.md>), [yandex-ai-studio](<https://devfeed.tech/tags/yandex-ai-studio.md>), [yolo](<https://devfeed.tech/tags/yolo.md>)

### AI overview

The article presents PereStruct, a modular pipeline for reconstructing articles from historical newspaper scans. It combines YOLO-based layout detection, Yandex Vision OCR, Yandex AI Studio models for error correction, and a semantic model for assembling article blocks; the authors also publish code, an annotated dataset, and a benchmark.

### Source excerpt

Попробуйте открыть скан советской газеты и прочитать одну статью от начала до конца. Человек быстро замечает крупный заголовок, продолжение в соседней колонке и подпись под фотографией. Для алгоритма перед ним -- это выцветшая страница с десятками тесно расположенных прямоугольников, нестандартными шрифтами и неоднозначным порядком чтения. Даже если OCR правильно распознаёт почти все слова, на выходе ещё не получится документ. Нужно понять, какие фрагменты относятся к одной статье, где её начало, в каком порядке соединить блоки и что не следует включать в основной текст. Мы разработали PereStruct -- модульный пайплайн для разбора исторических газет. Он объединяет детектор вёрстки на базе YOLO, Yandex Vision OCR, коррекцию ошибок с помощью моделей Yandex AI Studio и отдельную модель семантической сборки статей. Вместе с кодом мы публикуем размеченный датасет и бенчмарк, чтобы другие команды могли изучать подход и ставить эксперименты на исторических документах. Читать далее

## Blue Proton Initiative: four 17-year-olds are building AI-powered livestock monitoring with Arduino

DevFeed: [Blue Proton Initiative: four 17-year-olds are building AI-powered livestock monitoring with Arduino](<https://devfeed.tech/articles/blue-proton-initiative-four-17-year-olds-are-building-ai-powered-livestock-monitoring-with-arduino-13646.md>)

Original publisher: [Read original article](<https://blog.arduino.cc/2026/08/27/blue-proton-initiative-four-17-year-olds-are-building-ai-powered-livestock-monitoring-with-arduino/>)

Author: Arduino Team

Published: 2026-08-27T13:18:53Z

Content type: article

Language: en

Sources: [Arduino Blog](<https://devfeed.tech/sources/arduino-blog.md>)

Topics: [Arduino](<https://devfeed.tech/topics/arduino.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Computer vision](<https://devfeed.tech/topics/computer-vision.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [object-detection](<https://devfeed.tech/topics/object-detection.md>), [C](<https://devfeed.tech/topics/c.md>), [datasets](<https://devfeed.tech/topics/datasets.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-powered-livestock-monitoring](<https://devfeed.tech/tags/ai-powered-livestock-monitoring.md>), [arduino](<https://devfeed.tech/tags/arduino.md>), [computer-vision](<https://devfeed.tech/tags/computer-vision.md>), [data](<https://devfeed.tech/tags/data.md>), [livestock-monitoring](<https://devfeed.tech/tags/livestock-monitoring.md>), [model-training](<https://devfeed.tech/tags/model-training.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [neural-networks](<https://devfeed.tech/tags/neural-networks.md>), [uno-q](<https://devfeed.tech/tags/uno-q.md>), [yolo](<https://devfeed.tech/tags/yolo.md>)

### AI overview

An Arduino Blog article profiles four 17-year-olds in Italy developing an AI-powered livestock monitoring system with the UNO Q. The project uses computer vision, custom-trained neural networks, and diverse image data to identify animals, count them, and detect possible health problems in real time.

### Source excerpt

Pietro Maria Piazza, Alessandro Nesci, Davide Santucci, and Matteo Angiolillo are not waiting to finish school before starting to build something real. Based in Forlì, Italy, the four friends behind Blue Proton Initiative strive to develop an AI-powered livestock monitoring system designed to help farmers identify individual animals and detect early signs of health problems [...] The post Blue Proton Initiative: four 17-year-olds are building AI-powered livestock monitoring with Arduino appeared first on Arduino Blog.