A comic app built with Flutter, supporting multiple comic sources.Pica Comic A comic app with multiple sources built with flutter. How to use Clone the repository git clone https://github.com/wgh136/PicaComic Install flutter: https://docs.flutter.dev/get-started/install Build Application: https://docs.flutter.dev/deployment Introduction Built-in Comic Source Currently, Pica Comic has 5 built-in comic sources: picacg e-hentai/exhentai jmcomic hitomi 绅士漫画 nhentai Custom Comic Source You…
m3u8[m3u8-downloader] 视频在线提取工具 流媒体下载 、视频下载 、 m3u8下载 、 B站视频下载 桌面客户端 windows mac MediaGo 快速开始 • 官网 • 文档 • Discussions Intro 本项目支持 m3u8 视频在线提取工具 流媒体下载 m3u8 下载。 ✅ 无需抓包： 使用软件自带浏览器可以轻松嗅探网页中的视频资源，通过嗅探到的资源列表选择自己想要下载的资源，简单快速。 📱 移动播放： 可以轻松无缝的在 PC 和移动设备之前切换，下载完成后即可使用手机观看视频。 ⚡️ 批量下载： 支持同时下载多个视频和直播资源，高速带宽不闲置。 Quickstart 运行代码需要 node 和 pnpm，node 需要在官网下载安装，pnpm 可以通过npm i -g pnpm安装。 运行代码 # 代码下载 git clone https://github.com/caorushizi/mediago.git # 安装依赖 pn…
The Self-hosted AI Starter Kit is an open-source template that quickly sets up a local AI environment. Curated by n8n, it provides essential tools for creating secure, self-hosted AI workflows.Self-hosted AI starter kit Self-hosted AI Starter Kit is an open, docker compose template that quickly bootstraps a fully featured Local AI and Low Code development environment. Curated by https://github.com/n8n-io, it combines the self-hosted n8n platform with a curated list of compatible AI products an…
دورهی مقدمهای بر یادگیری ماشین، برای دانشجویانMachine Learning Course Computer Engineering Department, Sharif University of Technology You can find Slides, Jupyter Notebooks and Exercises of "Introduction to machine learning" course of Fall 2024 (1403) Semester. It's currently under construction and will be updated during the semster. The complete set of course material of previous semesters are located in "Previous Semsters".
A modern, fast and flexible .NET testing frameworkTUnit A modern, flexible and fast testing framework for .NET 8 and up. With Native AOT and Trimmed Single File application support included! Documentation See here: https://thomhurst.github.io/TUnit/ IDE TUnit is built on top of the newer Microsoft.Testing.Platform, as opposed to the older VSTest platform. Because the infrastructure behind the scenes is new and different, you may need to enable some settings. This should just be a one time …
SVG icons for popular brands Simple Icons Over 3100 Free SVG icons for popular brands. See them all on one page at SimpleIcons.org. Contributions, corrections & requests can be made on GitHub. Usage ImportantWe ask that all users read our legal disclaimer before using icons from Simple Icons. General Usage Icons can be downloaded as SVGs directly from our website - simply click the download button of the icon you want, and the download will start automatically. CDN Usage Icons …
Qwen2.5 is the large language model series developed by Qwen team, Alibaba Cloud.Qwen2.5 🤗 Hugging Face | 🤖 ModelScope | 📑 Paper | 📑 Blog ｜ 📖 Documentation 🖥️ Demo | 💬 WeChat (微信) | 🫨 Discord Visit our Hugging Face or ModelScope organization (click links above), search checkpoints with names starting with Qwen2.5- or visit the Qwen2.5 collection, and you will find all you need! Enjoy! To learn more about Qwen2.5, feel free to read our documentatio…
Optimized implementation for color-icon-matrix barcodesINTRODUCTION | ABOUT | CFC | LIBCIMBAR DETAILS | PERFORMANCE | TODO libcimbar: Color Icon Matrix Barcodes Behold: an experimental barcode format for air-gapped data transfer. It can sustain speeds of 850 kilobits/s (~106 KB/s) using just a computer monitor and a smartphone camera! Explain? The encoder outputs an animated barcode to a computer or smartphone screen: Encoder web app: https://cimbar.org While the decoder is a cell phone ap…
NumPy & SciPy for GPU CuPy : NumPy & SciPy for GPU Website | Install | Tutorial | Examples | Documentation | API Reference | Forum CuPy is a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python. CuPy acts as a drop-in replacement to run existing NumPy/SciPy code on NVIDIA CUDA or AMD ROCm platforms. >>> import cupy as cp >>> x = cp.arange(6).reshape(2, 3).astype('f') >>> x array([[ 0., 1., 2.], [ 3., 4., 5.]], dty…