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Python tool for converting files and office documents to Markdown.
別用愛發電！這是一套將遊戲設計從藝術升維為科學的公式化實戰手冊，專為終結平庸設計和戰略變現而打造。 STOP Working for Love! This is the formulaic playbook that elevates game design from art to science, built for strategic monetization and ending cr…
OpenStock is an open-source alternative to expensive market platforms. Track real-time prices, set personalized alerts, and explore detailed company insights — built openly, for everyone, forever f…
The contents of /mnt/skills in Claude's code interpreter environment
Source for https://fullstackdeeplearning.com
Tongyi Deep Research, the Leading Open-source Deep Research Agent
⚡ Python-free Rust inference server — OpenAI-API compatible. GGUF + SafeTensors, hot model swap, auto-discovery, single binary. FREE now, FREE forever.
AI-powered CLI that translates natural language into safe, reviewable ffmpeg commands.
gpt-oss-120b and gpt-oss-20b are two open-weight language models by OpenAI
A curated list of insanely awesome libraries, packages and resources for systematic trading. Crypto, Stock, Futures, Options, CFDs, FX, and more | 量化交易 | 量化投资
VectorBT: Find your trading edge, using the fastest engine for backtesting, algorithmic trading, and research.
🔎 📈 🐍 💰 Backtest trading strategies in Python.
stock股票.获取股票数据,计算股票指标,筹码分布,识别股票形态,综合选股,选股策略,股票验证回测,股票自动交易,支持PC及移动设备。
Python Backtesting library for trading strategies
The pytest framework makes it easy to write small tests, yet scales to support complex functional testing
The 100 line AI agent that solves GitHub issues or helps you in your command line. Radically simple, no huge configs, no giant monorepo—but scores >74% on SWE-bench verified!
《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。
Working Directory for UoM Dissertation.
VIP cheatsheet for Stanford's CME 295 Transformers and Large Language Models
A survey and paper list of current Diffusion Model for Time Series and SpatioTemporal Data with awesome resources (paper, application, review, survey, etc.).
Official Repo for "TheoremExplainAgent: Towards Video-based Multimodal Explanations for LLM Theorem Understanding" [ACL 2025 oral]