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Find your next superpower If you are an agent, refer to our llms.txt for full access.Create Hugging Face paper pages, link papers to models and datasets, and claim authorship. Also supports generating professional research articles in Markdown.
Retrieve paper pages in Markdown and use the papers API for authors, linked models, datasets, Spaces, repositories, and project pages. Supports summarizing, explaining, and analyzing papers from Hugging Face or arXiv references.
Create tools and scripts for tasks that benefit from Hugging Face API data. Supports combining API calls and packaging repeated data retrieval, enrichment, or processing into reusable automation.
Log training metrics through Python and retrieve or analyze them through the CLI. Supports real-time dashboards, diagnostic alerts, webhooks, Hugging Face Space syncing, and JSON output for automation.
Train object detection, image classification, and SAM/SAM2 segmentation models using Hugging Face Transformers. Covers dataset preparation, augmentation, evaluation, hardware and cost selection, Trackio monitoring, and Hub persistence.
Explains template architecture and customization, including pages, content, partials, components, modules, and scripts. Covers styling with Tailwind v4, Bootstrap 5, or a verified hybrid setup.
Supports architectural decisions, system design discussions, and planning across multiple components. Emphasizes domain modeling, systems thinking, navigating constraints, and decomposing problems with awareness of AI.
Reviews and rewrites French content using 38 identified AI-writing patterns. Addresses generic vocabulary, anglicisms, formulaic openings, repetitive syntax, punctuation inconsistencies, decorative emojis, and uniform sentence lengths.
Transform AI-generated text into natural, human-like content. The description claims avoidance of detectors such as GPTZero, Turnitin, and Originality.ai, and specifies credit usage based on word count.
Explicit user-invoked fallback for minimal de-AI surface editing that preserves meaning and voice.
Examines 40 patterns in AI-generated Korean text, including excessive commas, translation-like phrasing, repetitive vocabulary, and monotonous structure. Uses three severity levels to guide revisions toward more natural writing.
Remove AI-isms and artificial language patterns from text. Makes documentation, comments, commit messages, and prose sound more natural and human. Based on Wikipedia's "Signs of AI writing" patterns.
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