Продуктивность
dsh-personal-workbench
Personal workbench for DeepSeek Harness Web: calendar + task list + AI assistant.
★ 34 звездыMIT
Смотреть руководство →AI_Find_Customer — проект с открытым исходным кодом из категории «Автоматизация» от xiongQvQ (xiongQvQ/AI_Find_Customer на GitHub). У него 181 звезда на GitHub и 49 форков, он распространяется по лицензии MIT и последний раз обновлялся на GitHub 17 июля 2026.
Run ai_find_customer locally: 面向外贸与 b2b 场景的 ai 客户挖掘系统，基于 fastapi、langgraph 和 react，实现.
Fetch the source code onto your machine.
Set up the Python environment the project needs.
Copy the sample config or env file and adjust it.
Start the app from the source directory.
Built around ai for a focused workflow.
Built around aitools for a focused workflow.
Built around b2b for a focused workflow.
Built around find customer for a focused workflow.
Inspect, fork, and modify every line of the code.
Run it on your own machine — no account lock-in.
Get the source on your machine.
git clone https://github.com/xiongQvQ/AI_Find_Customer.gitSet up Python dependencies.
cd AI_Find_Customer && pip install -r requirements.txtLaunch the application.
cd AI_Find_Customer && python main.pyIt 面向外贸与 B2B 场景的 AI 客户挖掘系统，基于 FastAPI、LangGraph 和 React，实现公司理解、关键词生成、网页搜索、线 索提取与联系方式发现AI-powered B2B lead hunting system built with FastAPI, LangGraph, and React for automated company research, keyword generation, web search, lead extraction, and contact discovery, giving you a working starting point you can run and adapt.
Yes — the code is open-source, so you can use and modify it without a license fee.
Clone the GitHub repository and follow the README; the typical flow is install dependencies, configure, and run.
It is primarily written in Python, so you will need that toolchain to build or extend it.
The GitHub repository is the hub: open issues for bugs and check the README and discussions for guidance.
Personal workbench for DeepSeek Harness Web: calendar + task list + AI assistant.
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