Lists (2)
Stars
[WIP] Resources for AI engineers. Also contains supporting materials for the book AI Engineering (Chip Huyen, 2025)
Implement a reasoning LLM in PyTorch from scratch, step by step
Serenity-inspired Agent Skill for supply-chain bottleneck stock research
Open Data Platform for analysts, quants and AI agents.
Portfolio analytics for quants, written in Python
📈 Download filings from the SEC EDGAR database using Python
A historical copy of CNN's Fear and Greed Dataset
AI market map for scanning 5,000+ U.S.-listed stocks
FinceptTerminal is a modern finance application offering advanced market analytics, investment research, and economic data tools, designed for interactive exploration and data-driven decision-makin…
An Awesome List of 500+ AI Market Maps from 2026 and 2025.
Collection of step-by-step playbooks for setting up AI/ML workloads on NVIDIA DGX Spark devices with Blackwell architecture.
Download market data from Yahoo! Finance's API
AKShare is an elegant and simple financial data interface library for Python, built for human beings! 开源财经数据接口库
基于多智能体LLM的中文金融交易框架 - TradingAgents中文增强版
TradingAgents: Multi-Agents LLM Financial Trading Framework
Kronos: A Foundation Model for the Language of Financial Markets
A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.
Build an email assistant with human-in-the-loop and memory
An agent-managed museum exhibit, built in Rust with Gajae-Code / LazyCodex — developed and maintained with no human intervention.
Claude Code is an agentic coding tool that lives in your terminal, understands your codebase, and helps you code faster by executing routine tasks, explaining complex code, and handling git workflo…
Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞
Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models
Search-R1: An Efficient, Scalable RL Training Framework for Reasoning & Search Engine Calling interleaved LLM based on veRL
Materials for EACL2024 tutorial: Transformer-specific Interpretability