update readme

Author: refraction-ray

README.md

@@ -35,10 +35,25 @@ TensorCircuit-NG is the only actively maintained official version and a [fully c
 
 Please begin with [Quick Start](/docs/source/quickstart.rst) in the [full documentation](https://tensorcircuit-ng.readthedocs.io/).
 
-For more information on software usage, sota algorithm implementation and engineer paradigm demonstration, please refer to 100+ [example scripts](/examples) and 40+ [tutorial notebooks](https://tensorcircuit-ng.readthedocs.io/en/latest/#tutorials). API docstrings and test cases in [tests](/tests) are also informative. One can also refer to AI-native docs for tensorcircuit-ng: [Devin Deepwiki](https://deepwiki.com/tensorcircuit/tensorcircuit-ng) and [Context7 MCP](https://context7.com/tensorcircuit/tensorcircuit-ng).
+For more information on software usage, sota algorithm implementation and engineer paradigm demonstration, please refer to 130+ [example scripts](/examples) and 40+ [tutorial notebooks](https://tensorcircuit-ng.readthedocs.io/en/latest/#tutorials). API docstrings and test cases in [tests](/tests) are also informative. One can also refer to AI-native docs for tensorcircuit-ng: [Devin Deepwiki](https://deepwiki.com/tensorcircuit/tensorcircuit-ng) and [Context7 MCP](https://context7.com/tensorcircuit/tensorcircuit-ng).
 
 For beginners, please refer to [quantum computing lectures with TC-NG](https://github.com/sxzgroup/qc_lecture) to learn both quantum computing basics and representative usage of TensorCircuit-NG.
 
+### AI-Assisted Development (Recommended)
+
+To write TC-NG scripts and applications efficiently with AI coding agents (e.g., Claude Code, Cursor, Antigravity), we strongly recommend **working directly within the local tensorcircuit-ng repository** rather than an empty folder.
+
+1.  **Rich Context:** The 100+ scripts in `examples/` and extensive test cases in `tests/` provide essential references that significantly reduce AI hallucinations.
+2.  **Built-in Rules:** We provide a dedicated **[`llm.md`](/llm.md)** file. It serves as the "handbook" (i.e. `CLAUDE.md`) for AI agents, defining coding standards and best practices to ensure the generated code is idiomatic.
+
+**Recommended Workflow:**
+
+1.  Clone the repository: `git clone https://github.com/tensorcircuit/tensorcircuit-ng.git`
+2.  Switch to a local playground branch: `git checkout -b my-playground` in case messing up with the original repository.
+3.  Open the repository folder in your AI IDE. And you are ready to start writing TC-NG scripts.
+
+Now, enjoy implementing quantum algorithms entirely through natural language.
+
 The following are some minimal demos.
 
 - Circuit construction:
@@ -162,13 +177,11 @@ We also have [Docker support](/docker).
 - HPC native, distributed simulation enabled, multiple devices/hosts support
 
 - Efficiency
-
   - Time: 10 to 10^6+ times acceleration compared to TensorFlow Quantum, Pennylane or Qiskit
 
   - Space: 600+ qubits 1D VQE workflow (converged energy inaccuracy: < 1%)
 
 - Elegance
-
   - Flexibility: customized contraction, multiple ML backend/interface choices, multiple dtype precisions, multiple QPU providers
 
   - API design: quantum for humans, less code, more power
@@ -177,21 +190,13 @@ We also have [Docker support](/docker).
 
   <details>
   <summary> Tons of amazing features and built in tools for research (click for details) </summary>
-
   - Support **super large circuit simulation** using tensor network engine.
-
   - Support **noisy simulation** with both Monte Carlo and density matrix (tensor network powered) modes.
-
   - Support **stabilizer circuit simulation** with stim backend
-
   - Support **approximate simulation** with MPS-TEBD modes.
-
   - Support **analog/digital hybrid simulation** (time dependent Hamiltonian evolution, **pulse** level simulation) with neural ode modes.
-
   - Support **Fermion Gaussian state** simulation with expectation, entanglement, measurement, ground state, real and imaginary time evolution.
-
   - Support **qudits simulation** for tensor network and MPS approximation modes.
-
   - Support **parallel** quantum circuit evaluation across **multiple GPUs**.
 
   - Highly customizable **noise model** with gate error and scalable readout error.

README_cn.md

@@ -33,10 +33,12 @@ TensorCircuit-NG 是目前积极维护的唯一官方版本，是 TensorCircuit
 
 请从 [完整文档](https://tensorcircuit-ng.readthedocs.io/) 中的 [快速上手](/docs/source/quickstart.rst) 开始。
 
-有关软件用法，算法实现和工程范式演示的更多信息和介绍，请参阅 100+ [示例脚本](/examples) 和 40+ [案例教程](https://tensorcircuit-ng.readthedocs.io/en/latest/#tutorials)。 [测试](/tests) 用例和 API docstring 也提供了丰富的使用信息。
+有关软件用法，算法实现和工程范式演示的更多信息和介绍，请参阅 130+ [示例脚本](/examples) 和 40+ [案例教程](https://tensorcircuit-ng.readthedocs.io/en/latest/#tutorials)。 [测试](/tests) 用例和 API docstring 也提供了丰富的使用信息。
 
 TensorCircuit-NG 也支持 AI 原生编程资源：[Devin Deepwiki](https://deepwiki.com/tensorcircuit/tensorcircuit-ng) 和 [Context7 MCP](https://context7.com/tensorcircuit/tensorcircuit-ng).
 
+若使用 AI 编程助手（如 Claude Code, Cursor, Antigravity）编写 TC-NG 应用，我们强烈建议直接在克隆的本地代码库中进行开发以提供更好的上下文，而非在新的空文件夹中操作。
+
 初学者也可以参考[量子计算教程](https://github.com/sxzgroup/qc_lecture)学习量子计算基础和 TensorCircuit-NG 的典型用法.
 
 以下是一些最简易的演示。
@@ -112,13 +114,11 @@ pip install tensorcircuit-nightly
 - 高性能原生，分布式多卡多节点支持
 
 - 效率
-
   - 时间：与 TFQ, Pennylane, 或 Qiskit 相比，加速 10 到 10^6+ 倍
 
   - 空间：600+ qubits 1D VQE 工作流（收敛能量误差：< 1%）
 
 - 优雅
-
   - 灵活性：自定义张量收缩、多种 ML 后端/接口选择、多种数值精度、多种量子硬件
 
   - API 设计：人类可理解的量子，更少的代码，更多的可能