1

curl -fsSL https://claude.ai/install.sh | bash

1

brew install --cask claude-code

1

irm https://claude.ai/install.ps1 | iex

1

winget install Anthropic.ClaudeCode

1
2

cd your-project
claude

 1
 2
 3
 4
 5
 6
 7
 8
 9
10

# 克隆仓库
git clone https://github.com/microsoft/VibeVoice.git
cd VibeVoice

# 创建虚拟环境
python -m venv .venv
source .venv/bin/activate

# 安装依赖
pip install -e .

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11

from transformers import pipeline

# 语音识别
asr = pipeline(task="automatic-speech-recognition", 
               model="microsoft/VibeVoice-ASR")
result = asr("audio_file.wav")

# 语音合成
tts = pipeline(task="text-to-speech", 
               model="microsoft/VibeVoice-1.5B")
tts("Hello, this is VibeVoice speaking.")

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13

# 克隆仓库
git clone https://github.com/google-research/timesfm.git
cd timesfm

# 创建虚拟环境
uv venv
source .venv/bin/activate

# 安装 PyTorch 版本
uv pip install -e .[torch]

# 或安装 Flax 版本
uv pip install -e .[flax]

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31

import torch
import numpy as np
import timesfm

torch.set_float32_matmul_precision("high")

# 加载预训练模型
model = timesfm.TimesFM_2p5_200M_torch.from_pretrained(
    "google/timesfm-2.5-200m-pytorch"
)

model.compile(
    timesfm.ForecastConfig(
        max_context=1024,
        max_horizon=256,
        normalize_inputs=True,
        use_continuous_quantile_head=True,
    )
)

# 进行预测
point_forecast, quantile_forecast = model.forecast(
    horizon=12,
    inputs=[
        np.linspace(0, 1, 100),
        np.sin(np.linspace(0, 20, 67)),
    ]
)

print(point_forecast.shape)  # (2, 12)
print(quantile_forecast.shape)  # (2, 12, 10)

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13

# 克隆指南
git clone https://github.com/luongnv89/claude-howto.git
cd claude-howto

# 复制 Slash Commands
mkdir -p /path/to/your-project/.claude/commands
cp 01-slash-commands/*.md /path/to/your-project/.claude/commands/

# 设置项目记忆
cp 02-memory/project-CLAUDE.md /path/to/your-project/CLAUDE.md

# 安装技能
cp -r 03-skills/code-review ~/.claude/skills/

1
2
3
4
5
6
7

# /optimize

分析当前文件的性能问题并提供优化建议。

1. 识别性能瓶颈
2. 提供优化方案
3. 生成优化后的代码

1

npm install -g @openai/codex

1

brew install --cask codex

1
2
3
4
5
6
7
8

# 启动交互式模式
codex

# 使用 ChatGPT 账户登录
# 选择 "Sign in with ChatGPT"

# 或者直接运行命令
codex -p "review this code and suggest improvements"

1
2

npx prompts.chat new my-prompt-library
cd my-prompt-library

1
2
3

git clone https://github.com/f/prompts.chat.git
cd prompts.chat
npm install && npm run setup

1
2
3
4
5
6
7

{
  "mcpServers": {
    "prompts.chat": {
      "url": "https://prompts.chat/api/mcp"
    }
  }
}

1
2
3
4
5
6
7
8

{
  "mcpServers": {
    "prompts.chat": {
      "command": "npx",
      "args": ["-y", "prompts.chat", "mcp"]
    }
  }
}

1

npm install axios

1

yarn add axios

1

<script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/axios.min.js"></script>

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23

import axios from "axios";

// GET 请求
const response = await axios.get("/user?ID=12345");

// POST 请求
const response = await axios.post("/user", {
  firstName: "Fred",
  lastName: "Flintstone",
});

// 创建实例
const instance = axios.create({
  baseURL: "https://some-domain.com/api/",
  timeout: 1000,
  headers: { "X-Custom-Header": "foobar" },
});

// 拦截器
axios.interceptors.request.use(config => {
  config.headers.Authorization = `Bearer ${token}`;
  return config;
});

1

conda install pytorch torchvision torchaudio pytorch-cuda=12.4 -c pytorch -c nvidia

1

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124

1
2
3
4

git clone https://github.com/pytorch/pytorch
cd pytorch
git submodule update --init --recursive
pip install -e .

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27

import torch
import torch.nn as nn
import torch.nn.functional as F

# 定义神经网络
class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.conv2(x)
        x = F.max_pool2d(x, 2)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

# 使用模型
model = Net()
optimizer = torch.optim.Adam(model.parameters())

1
2
3

pip install langchain
# 或
uv add langchain

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13

from langchain.chat_models import init_chat_model

# 初始化模型
model = init_chat_model("openai:gpt-5.4")

# 调用模型
result = model.invoke("Hello, world!")

# 使用 LangGraph 构建代理工作流
from langgraph.graph import StateGraph

builder = StateGraph()
# ... 定义代理逻辑

 1
 2
 3
 4
 5
 6
 7
 8
 9
10

# 使用 pip
pip install "transformers[torch]"

# 使用 uv
uv pip install "transformers[torch]"

# 从源码安装
git clone https://github.com/huggingface/transformers.git
cd transformers
pip install -e '.[torch]'

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21

from transformers import pipeline

# 文本生成
generator = pipeline(task="text-generation", 
                     model="Qwen/Qwen2.5-1.5B")
result = generator("The secret to success is")

# 语音识别
asr = pipeline(task="automatic-speech-recognition",
               model="openai/whisper-large-v3")
result = asr("audio.mp3")

# 图像分类
classifier = pipeline(task="image-classification",
                      model="facebook/dinov2-small-imagenet1k-1-layer")
result = classifier("image.jpg")

# 视觉问答
vqa = pipeline(task="visual-question-answering",
               model="Salesforce/blip-vqa-base")
result = vqa(image="image.jpg", question="What is in the image?")

1
2
3
4
5
6
7

# 使用 rustup
rustup component add rust-analyzer

# 或从源码构建
git clone https://github.com/rust-lang/rust-analyzer
cd rust-analyzer
cargo install --path crates/rust-analyzer

1

curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.4/install.sh | bash

1

wget -qO- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.4/install.sh | bash

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17

# 安装 Node.js 版本
nvm install 24
nvm install 22
nvm install 20

# 切换版本
nvm use 22
nvm use 20

# 设置默认版本
nvm alias default 22

# 查看已安装版本
nvm ls

# 在项目目录创建 .nvmrc
echo "20" > .nvmrc