Learning 117
- PWEB001: Full-Stack Engineering as a Public Evidence System
- FICC009: Rates Bond Quant - 我们自己的开源利率债量化项目
- ICML2026003: Quant Paper Map - Data / Signal / Risk / Portfolio / Market / Financial Agent
- ICML2026002: Oral Paper Map - Production Agent / Document Reasoning / Safety / RL / Data Market
- ICML2026001: Spotlight Paper Map - Agent / Quant / RAG / RL / Research OS 推荐阅读地图
- HKUDS053: OpenOPC - AI-Native Company / Self-Built Self-Run Self-Grown
- Futures x Crypto HFT: 期货投研项目与高频做市项目对比
- MarketMakerHFT001: Crypto Case Study for High-Frequency Market Making
- Futures001: 从期货 CTA 回测到 ML / GP 因子研究
- 专题001: HKUDS x LLMQuant 六项目对比 - LightRAG / Vibe-Trading / AI-Trader / NanoBot / Skills / Data MCP
- Quant Open Source Package001: Qlib - Microsoft AI Quant Research Platform 深度学习
- LightRAG Deepdown003: Tests / PR / Open Source Maintenance OS
- LightRAG Deepdown002: Python Core / File Pipeline / Storage / Retrieval / Why Light
- LightRAG Deepdown001: Coding OS - Python / TypeScript / TSX / HTML / CSS / JS / TOML / YAML / JSON
- LLMQuant x WorldQuant: 从因子投研流程到 AI Quant Research OS
- LLMQuant x HKUDS x FICC: AI 金融研究系统合作地图
- FICC008: FICC AI Agent Harness - 从 FICC001-FICC007 到可控研究 Agent 系统
- FICC007: FICC 系列总复盘 - 从三大资产到 AI Research OS
- FICC006: FICC x AI Research OS - RAG / Agent / Quant Workflow
- FICC005: Event-to-Signal Workflow - 从市场事件到研究假设、验证与复盘
- FICC004: Daily FICC Brief Generator - 把 FI / FX / Commodities 合成每日研究工作流
- FICC002: Currencies / FX - 汇率、远期、掉期、利差、套息与跨境资金流
- FICC003: Commodities - 能源、金属、农产品、期货曲线、库存与供需
- HKUDS052: HKUDS Quant 系列专题总结 - Vibe-Trading / AI-Trader / FutureShow / UrbanGPT 对比
- HKUDS051: HKUDS RAG 系列专题总结 - LightRAG / RAG-Anything / MiniRAG / VideoRAG 对比
- FICC001: Fixed Income / Rates / Credit - 债券、利率曲线、久期与信用利差
- FICC000: FICC 总地图 - Fixed Income / Currencies / Commodities 三大类
- CLOUDFLARE000: Cloudflare 网站部署与全栈应用总地图
- AI_CONF000: AI 顶会地图 - ML / NLP / CV / Agent / RAG / Data Mining / Robotics / Systems
- HKUDS050: Remaining Repo Map - 未完成项目总览、分类与后续学习路线
- PENGYI_HARNESS001: DeepSeek Coding Agent Harness - Claude Code vs Codex 的产品启示与工程预演
- X2STRATEGY000: X2Strategy 单独章节 - Paper-to-Strategy Compiler 与 Quant Research Harness
- PENGYI_HARNESS_MAP000: Harness 总览 - Agent / Research / Quant / Tool / Memory / Product 六类 Harness
- NEETCODE007: Coding Interview Execution OS
- NEETCODE006: Math & Geometry / Bit Manipulation
- NEETCODE005: Dynamic Programming / Greedy / Intervals
- NEETCODE004: Backtracking / Graphs / Advanced Graphs
- NEETCODE003: Trees / Heap / Tries
- NEETCODE002: Stack / Binary Search / Linked List
- NEETCODE001: Arrays & Hashing / Two Pointers / Sliding Window
- NEETCODE000: NeetCode 题型总地图 - DSA / Coding Interview / Agent Harness 训练系统
- MLRL005: Quant / Agent / LLM 里的 ML-RL 统一视角
- MLRL004: RLHF / Agent Training
- MLRL003: Reinforcement Learning 基础
- MLRL002: Transformer 架构
- MLRL001: PyTorch 架构与训练循环
- MLRL000: Machine Learning / Reinforcement Learning / PyTorch / Transformer 总地图
- LLMQUANT008: LLMQuant 学习总览与 000-007 项目作用清单
- HKUDS049: UrbanGPT 作为 Spatio-Temporal LLM、Urban Forecasting Foundation Model 与 Quant OS 时空预测层
- HKUDS048: MGP 作为 Governed Agent Memory Protocol、Policy/Audit Layer 与 Research OS Memory Governance Layer
- HKUDS047: SepLLM 作为 Long-Context Compression、KV Cache Efficiency 与 Research OS Memory Compression Layer
- HKUDS046: LightReasoner 作为 Reasoning Efficiency、Expert-Amateur Teaching 与 Research OS Skill Distillation Layer
- HKUDS045: CatchMe 作为 Personal Digital Footprint、Agent Memory Layer 与 Research OS Context Engine
- HKUDS044: ViMax 作为 Agentic Video Generation、AI Creative Studio 与 Research OS Multimodal Production Layer
- HKUDS043: HKUDS 学习总览与 000-042 项目作用清单
- HKUDS042: Agent Product Phase Review 作为 Pengyi Research OS Agent Product Stack 阶段复盘
- HKUDS041: Auto-Deep-Research / DeepResearch-Eval Revisited 作为 Deep Research Product Loop 与 AI Scientist Evaluation Layer
- HKUDS040: VideoAgent 作为 Agentic Video Workflow、Meeting Intelligence 与 Multimodal Production OS
- HKUDS039: UpSkill Revisited 作为 Agent Skill Growth Layer 与 Research OS 复利系统
- HKUDS038: MoChat 作为 Agent-Native IM、Networking Wingman 与 AI Organization Interface
- HKUDS037: OpenPhone 作为 AI Phone Agent、现实 App 操作入口与 Mobile Research Agent
- HKUDS036: Litewrite 作为 AI Research Writing Workspace 与 Vibe Writing Product
- HKUDS035: FastAgent 作为 DeepResearch + Computer Use 的高速 Agent Execution Engine
- HKUDS034: ClawWork 作为 AI Coworker 与 Economic Accountability Layer
- HKUDS033: ClawTeam 作为 Agent Swarm Intelligence 与 AI Organization Layer
- HKUDS032: StudyMap4 - Agent Product / Workspace 系列路线图
- HKUDS031: KGRec 作为 Knowledge Graph Self-Supervised Rationalization 与 KG-Grounded Recommendation Layer
- HKUDS030: AutoCF 作为 Automated Self-Supervised Collaborative Filtering 与 Recommendation Backbone Layer
- HKUDS029: XRec 作为 Explainable Recommendation 与 Collaborative-Signal-to-Language Layer
- HKUDS028: RecLM 作为 Recommendation Instruction Tuning 与 Profile-Augmented Ranking Layer
- HKUDS027: HiGPT 作为 Heterogeneous Graph Language Model 与 Structured Multimodal Layer
- HKUDS026: GraphGPT 作为 Graph Instruction Tuning 与 Graph-Language Alignment Layer
- HKUDS025: OpenGraph 作为 Open Graph Foundation Model 与 Zero-Shot Graph Generalization Layer
- HKUDS024: GraphAgent 作为 Agentic Graph Language Assistant 与 Graph Reasoning Layer
- HKUDS023: OpenSpace 作为 Self-Evolving Agent Workspace 与 Skill Economy Layer
- HKUDS022: FastCode 作为 Code Intelligence Acceleration 与 Repo-Level Research Engineering Layer
- HKUDS021: VideoRAG 作为 Extreme Long-Context Video Memory 与 Multimodal Knowledge Ingestion Layer
- HKUDS020: FutureShow 作为 Forecasting Agent Benchmark 与 Quant Judgment Layer
- HKUDS019: Paper2Slides 作为 Research-to-Presentation Artifact Generation Layer
- HKUDS018: MiniRAG 作为 Lightweight Graph RAG 与 On-Device Knowledge Layer
- HKUDS017: AnyTool 作为 Universal Tool-Use Layer 与 Capability Routing Layer
- HKUDS00000: StudyMap3 - HKUDS020 之后的八条后续路线
- HKUDS016: UpSkill 作为 Failure-to-Skill Distillation 与 Agent Self-Improvement Layer
- HKUDS015: OpenHarness 作为 Agent Harness Runtime 与 Personal Agent Infrastructure Layer
- HKUDS014: DeepTutor 作为 Agent-Native Personalized Tutoring 与 AI Scientist Self-Training Layer
- HKUDS013: DeepResearch-Eval 作为 Report-Centric Evaluation 与 Factuality Checking Layer
- HKUDS012: Auto-Deep-Research 作为 Open Deep Research Product 与 AutoAgent Application Layer
- HKUDS011: DeepInnovator 作为 Scientific Idea Foundation Model 与 Research Innovation Training Layer
- HKUDS010: AI-Researcher 作为 Autonomous Scientific Discovery 与 Research Agent Benchmark Layer
- HKUDS009: DeepCode 作为 Paper2Code 与 Agentic Coding Implementation Layer
- HKUDS0000: 中场 Map - 四大主线、已做 Repo 与下一阶段路线
- LLMQUANT007: ecosystem coverage matrix 第一阶段总复盘
- LLMQUANT006: finance knowledge layer 作为金融知识底座
- LLMQUANT005: awesome-trading-agents 作为交易 Agent 生态雷达
- LLMQUANT004: Magents 作为多策略回测与仿真层
- LLMQUANT003: QuantMind 作为金融知识结构化层
- LLMQUANT002: skills 作为金融 workflow 路由层
- HKUDS008: AutoAgent 作为 Self-Developing Agent Factory 与 Zero-Code Workflow Creation Layer
- HKUDS007: RAG-Anything 作为 Multimodal Document Ingestion 与 All-in-One RAG Layer
- HKUDS006: AgentSpace 作为 Organizational Agent Workspace 与 Digital Employee Operating Layer
- HKUDS005: AI-Trader 作为 Agent-Native Live Trading Platform Layer
- HKUDS004: CLI-Anything 作为 Agent-Native Software Action Layer
- HKUDS003: nanobot 作为 Personal Agent Shell 与 Always-On Research Workspace
- HKUDS002: Vibe-Trading 作为 Agentic Quant Research Workflow
- HKUDS001: LightRAG 作为知识图谱 RAG 与 Research Memory 基建
- HKUDS000: PENGYI_HKUDS_STUDYMAP
- HKUDS Quant and Trading Projects: AI Trader Learning Log
- HKUDS, LLMQuant, and X2Strategy: Toward a Personal Research and Quant Production OS
- HKUDS vs LLMQuant: Two Project Universes for My Research OS
- yuandong000: Study Map for Yuandong Tian's Projects
- 把田渊栋访谈当下饭视频看
- QuantMind and X2Strategy: From Financial Knowledge to Executable Strategies
- LLMQUANT001: data-mcp 作为数据工具层
- LLMQUANT000: PENGYI_LLMQUANT_STUDYMAP
- HKUDS and LLMQuant: Project Similarities for a Quant Research OS
- LLMQuant 学习地图: 从项目阅读到个人 Research OS
- RA and PhD Research Path: Building a Long-Term Research Bridge