235 个
面向空间 LLM 与地理空间 AI Agent 的生产工程模式——验证优先架构、地理空间 RAG、prompt-to-spatial-SQL、tool routing 与评估。Production engineering patterns for spatial LLMs and geospatial AI agents — validation-first architecture, geospatial RAG, prompt-to-spatial-SQL, tool routing, and evaluation.
本地优先求职工作站——抓取职位、筛选分流，通过自有 Codex CLI 定制简历与求职信，导出 LaTeX PDF，无需服务器端模型密钥。Local-first job-search workstation — fetch roles, triage, tailor CVs and cover letters through your own Codex CLI, export LaTeX PDFs. No server-held model keys.
AI-Native 钢琴学习 RAG 助手——在线且免费（GitHub Pages -> Google Cloud Run -> Neon pgvector -> Groq）：具备引用、护栏与内置可观测性（延迟/token/成本）的摄取与查询流水线（rewrite -> 混合检索/RRF -> rerank -> LLM），数据持久化至可检索数据库。Node.js 实现，不依赖付费 API。AI-Native Piano Learning RAG Assistant - live & free (GitHub Pages -> Google Cloud Run -> Neon pgvector -> Groq): ingestion + query pipeline (rewrite -> hybrid search/RRF -> rerank -> LLM) with citations, guardrails, and built-in observability (latency/tokens/cost) persisted to a searchable DB. Node.js, no paid APIs.
AI 驱动的生物医学文献综述平台：从 PubMed 检索论文，使用 FAISS 进行语义搜索，并基于 Gemini LLM 生成循证文献综述🧬 AI-powered biomedical literature review platform that retrieves PubMed papers, performs semantic search using FAISS, and generates evidence-based literature reviews using Gemini LLM.
论文一：NL2SQL 歧义检测——M1.5 分类法、确定性基线、人工金标评估与可复现研究制品Paper 1: NL2SQL Ambiguity Detection — M1.5 taxonomy, deterministic baseline, human-gold evaluation, reproducible research artifact
Nexus Memory AI Plugin：面向 2026 持久化上下文的 OpenClaw 与 Hermes MCP 集成。Nexus Memory AI Plugin: OpenClaw & Hermes MCP Integration for Persistent Context 2026
基于 CrewAI、PostgreSQL 与科学 API 集成的多 Agent PRISMA 系统综述编排器Multi-agent PRISMA systematic review orchestrator with CrewAI, PostgreSQL, and scientific API integrations
CampusIQ 是由 AI 驱动的学术知识管理与问答系统，借助 RAG、语义搜索、OCR、图像理解以及基于 LLM 的回答生成，实现教育内容的智能检索与分析。CampusIQ is an AI-powered academic knowledge management and question-answering system that enables intelligent retrieval and analysis of educational content using RAG, semantic search, OCR, image understanding, and LLM-based response generation.
一个生产级 RAG 系统，能自主决定检索方式——从本地向量库、Web 搜索或两者结合，以引用、多源综合的方式回答研究问题A production-grade RAG system that autonomously decides how to retrieve information — from a local vector store, web search, or both to answer research questions with cited, multi-source synthesis.
由 AI 驱动的交易研究与市场情报平台，使用智能 AI agent、SQL、RAG、市场数据以及外部工具来分析研究问题并生成结构化的市场洞察。An AI-powered trading research and market intelligence platform that uses an intelligent AI agent, SQL, RAG, market data, and external tools to analyze research questions and generate structured market insights.
Rust Graph Tracker：为 LLM 编程 Agent 提供数值与日期来源追踪，支持表达式求值与推导验证Rust Graph Tracker: numeric and date provenance tracking with expression evaluation and derivation verification for LLM coding agents
受 MemGPT 启发、支持 OpenAI API 的长期记忆 Agent，具备记忆生命周期控制与评估能力。MemGPT-inspired long-term memory agent with OpenAI API support, memory lifecycle controls, and evaluation.
针对任意 GitHub 仓库的自然语言问答 —— 构建 Neo4j 依赖图与 Qdrant 向量索引，用自然语言即可提问结构性与行为性问题。Natural-language Q&A over any GitHub repo — builds a Neo4j dependency graph + Qdrant vector index so you can ask structural and behavioral questions in plain English
🧠 为 Telegram、Discord 与 Reddit 打造的私有、源码溯源知识库，基于混合 RAG，并提供每周摘要。🧠 Private, source-grounded knowledge base for Telegram, Discord, and Reddit with hybrid RAG and weekly digests.
企业级 Java AI Agent 任务编排、安全审批、RAG 与全链路审计平台
从学术数据库中采集学术元数据，用于组织文献检索与构建研究参考目录。Harvest scholarly metadata from academic databases to organize literature searches and build research bibliographies.
Subconscious Memory 2026：基于混合语义搜索与 MCP 的 AI 编码上下文引擎。Subconscious Memory 2026: AI Coding Context Engine with Hybrid Semantic Search & MCP
基于 n8n、Groq（Llama 模型）、Node.js、React 和 PostgreSQL 构建的多 Agent AI 研究流水线。用户提交研究问题后，由规划 Agent 拆解，由专业的研究/技术/成本 Agent 并行调查，由事实核查 Agent 验证，最终合成为带来源的结构化报告。A multi-agent AI research pipeline built with n8n, Groq (Llama models), Node.js, React, and PostgreSQL. A user submits a research question, which is broken down by a planning agent, investigated in parallel by specialized research/technical/cost agents, verified by a fact-checking agent, and synthesized into a structured report with sources.
面向有据可循的 Agent 工作流的 TypeScript 参考实现，支持混合检索、人工审核与可回放的审计追踪。TypeScript reference implementation for grounded agent workflows with hybrid retrieval, human review, and replayable audit trails.
使用纯 C 从零构建 LLM 推理引擎，不依赖任何框架。Build an LLM inference engine from scratch in pure C with no frameworks.
借助这款自托管、本地优先的应用，跨多个账户与多种货币追踪收入和支出。Track income and expenses across multiple accounts and currencies with this self-hosted, local-first application.
探索由 19,846 篇量子计算论文组成的 AI 原生知识库，面向搜索、趋势分析与 LLM 友好的研究工作流Explore an AI-native knowledge base of 19,846 quantum computing papers, built for search, trend analysis, and LLM-ready research workflows
Pinakes —— 可移植、Agent 优先的知识库。一个目录即一个 KB。Pinakes — a portable, agent-first knowledge base. One directory = one KB.
von der Heyde, L., Keusch, F., Buskirk, T. D., & Eck, A. (2026). AI in the Loop?! A Systematic Review of the Use of Large Language Models in Survey and Public Opinion Research. SocArXiv. https://doi.org/10.31235/osf.io/eubj4_v1 的复现材料与文献数据库Replication materials and literature databases for von der Heyde, L., Keusch, F., Buskirk, T. D., & Eck, A. (2026). AI in the Loop?! A Systematic Review of the Use of Large Language Models in Survey and Public Opinion Research. SocArXiv. https://doi.org/10.31235/osf.io/eubj4_v1.
Telegram RAG 机器人，以暗黑荒诞的《权力的游戏》谋士风格回答问题；支持英中双语，基于 LLaMA 3.3 70B + Firestore 向量搜索。A Telegram RAG bot that answers your questions as a darkly absurdist Game of Thrones advisor — bilingual (EN/ZH), powered by LLaMA 3.3 70B + Firestore vector search.
这个仓库展示了我的技能、项目，以及从数据工程转型为数据科学家角色的持续学习历程。This repository showcases my skills, projects, and continuous learning journey as I transition from Data Engineering to a Data Scientist role.
自动化 n8n 工作流，抓取 LinkedIn 产品经理职位，通过向量相似度与简历匹配打分，并使用 Claude 为每个职位生成简历修改建议Automated n8n workflow that scrapes LinkedIn PM jobs, scores them against your resume with vector similarity, and uses Claude to suggest per-job resume edits.
🔍 Self-Corrective RAG 基于 LangGraph 与 Google Gemini 优化查询并评估文档相关性，提升检索效果🔍 Enhance your searches with Self-Corrective RAG, a system that optimizes queries and evaluates document relevance using LangGraph and Google Gemini.
基于 .NET 的 RAG 文档摄取流水线 — 追踪 git 备份仓库中的文件夹，提取并分块文件，保持向量索引同步。.NET document ingestion pipeline for RAG — tracks a folder of files in a git-backed vault, extracts and chunks them, and keeps a vector index in sync.
基于你的 Zotero library 的个人论文推荐器。Personal paper recommender based on your Zotero library
为仿制药临床法规事务团队打造的法规准备加速器。A regulatory prep accelerator for a generic-drug Clinical Regulatory Affairs team.
自托管共享记忆，面向 AI Agent 团队。支持房间（Rooms）、L0-L3 层级深度与 MCP；读取路径不调用任何语言模型。基于 vectorize-io/hindsight（MIT）的 fork。Self-hosted shared memory for a team of AI agents. Rooms, hierarchical L0-L3 depth, MCP. The read path never invokes a language model. Fork of vectorize-io/hindsight (MIT).
全栈量化研究平台：因子分析、系统化策略回测、真实交易成本建模、滚动前推 ML 验证、投资组合风险分析与稳健性测试，基于美股日频数据。为可复现研究而构建——非实盘交易。Full-stack quantitative research platform for factor analysis, systematic strategy backtesting, realistic transaction-cost modeling, walk-forward ML validation, portfolio risk analytics, and robustness testing on daily U.S. equity data. Built for reproducible research—not live trading.
AI 驱动的每周生活规划器 — 由多供应商 Agent 生成饮食、学习、锻炼、习惯和娱乐方案，并附带每周日程追踪。AI-powered weekly life planner - nutrition, study, workouts, habits and fun generated by a multi-provider agent, with a weekly schedule tracker.
GMS 6803 临床研究数据科学课程材料，含幻灯片、代码与实操实验，内容涵盖真实世界健康数据、基于 EHR 的队列构建、SQL、Python、统计分析与临床研究中的机器学习Course materials for GMS 6803 Data Science for Clinical Research, including slides, code, and hands-on labs on real-world health data, EHR-based cohort development, SQL, Python, statistical analysis, and machine learning for clinical research.
OptiBuild 工程案例研究——一款 AI 驱动的施工现场管理平台：工程量清单解析、成本核算、进度报告与按角色划分的数据权限。Engineering case study of OptiBuild, an AI-enabled construction-site management platform: quantity-survey parsing, costing, work-progress statements and role-scoped data.