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Python - 100天从新手到大师
Embed GitHub issues, PRs, and code snippets directly in Obsidian
Dify is an open-source LLM app development platform. Dify's intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features and more, letting yo…
Instantly share an Obsidian note with the full theme exactly like you see in your vault. Data is shared encrypted by default, and only you and the person you send it to have the key.
⚡ TabPFN: Foundation Model for Tabular Data ⚡
Batch effects removal method based on deep autoencoder and adversarial learning
《开源大模型食用指南》针对中国宝宝量身打造的基于Linux环境快速微调（全参数/Lora）、部署国内外开源大模型（LLM）/多模态大模型（MLLM）教程
Python code to convert Obsidian notes to proper markdown and optionally to create an html site too.
A fast yet powerful Python Markdown parser with renderers and plugins.
🔧 A clean, elegant but advanced blog theme for Hugo 一个简洁、优雅且高效的 Hugo 主题
Process Obsidian notes to publish them with Hugo. Supports transformation of Obsidian wiki links into Hugo shortcodes for internal linking.
A hackable markdown, Typst, latex, html(inline) & YAML previewer for Neovim
Plugin to improve viewing Markdown files in Neovim
Integrate Git version control with automatic commit-and-sync and other advanced features in Obsidian.md
Code and sample data for the MSK-CHORD manuscript (public version)
Autocomplete your obsidian notes with AI, including ChatGPT, through a copilot-like interface.
Obsidian tars plugin that supports text generation based on tag suggestions, using services like DeepSeek, Claude, OpenAI, OpenRouter, SiliconFlow, Gemini, Ollama, Kimi, Doubao, Qwen, Zhipu, QianFa…
Sync notes between local and cloud with smart conflict: S3 (Amazon S3/Cloudflare R2/Backblaze B2/...), Dropbox, webdav (NextCloud/InfiniCLOUD/Synology/...), OneDrive, Google Drive (GDrive), Box, pC…
E2M converts various file types (doc, docx, epub, html, htm, url, pdf, ppt, pptx, mp3, m4a) into Markdown. It’s easy to install, with dedicated parsers and converters, supporting custom configs. E2…
Generating biologically informed neural networks