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# 单一静态二进制，零依赖
curl -L https://github.com/DeusData/codebase-memory-mcp/releases/latest/download/codebase-memory -o codebm
chmod +x codebm && sudo mv codebm /usr/local/bin/

# 启动 MCP 服务器（默认端口由具体 agent 决定）
codebm mcp --root .

# 11 个 agent 的自动配置命令（Claude Code / Codex / Zed / VS Code / OpenClaw / Kiro / Cursor …）
codebm agents install --all

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# 索引当前仓库（毫秒级完成）
codebm index --root . --langs all

# Cypher-like 查询：找出所有调用了 authenticate() 的函数
codebm query 'MATCH (caller:Function)-[:CALLS]->(callee:Function {name:"authenticate"}) RETURN caller.path, caller.name'

# 通过 MCP 让 Claude Code 直接调用
# 配合 .mcp.json：
# { "mcpServers": { "codebase-memory": { "command": "codebm", "args": ["mcp", "--root", "."] } } }

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# Python（PyTorch 后端）
pip install timesfm[torch]

# JAX 后端
pip install timesfm[jax]

# 从 Hugging Face 加载 2.5 检查点
python -c "import timesfm; m = timesfm.TimesFmHparams.from_hf('google/timesfm-2.5-200m-pytorch')"

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import timesfm
import numpy as np

# 加载模型（强制只用 CPU 也可）
tfm = timesfm.TimesFm(
    hparams=timesfm.TimesFmHparams.from_hf("google/timesfm-2.5-200m-pytorch"),
    checkpoint=timesfm.TimesFmCheckpoint.from_hf("google/timesfm-2.5-200m-pytorch"),
)

# 准备历史数据，shape: [batch, history_len]
history = [np.sin(np.linspace(0, 50, 1024)) + np.random.normal(0, 0.1, 1024)]

# Zero-shot 预测未来 96 个时间步 + 10/50/90 分位
point_forecast, quantile_forecast = tfm.forecast(
    history=history,
    freq=[0],          # 0=高频, 1=低频, 2=分钟级
    horizon=96,
)

print("未来 96 步预测:", point_forecast[0])
print("90 分位上界:", quantile_forecast[0, 0, :, 2])   # 索引 2 = 90 分位

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# 1. 下载 v0.3.4 (615 次提交，持续高频迭代)
# https://github.com/palmier-io/palmier-pro/releases

# 2. macOS 26 (Tahoe) + Apple Silicon 强制要求
#    检查：sw_vers; uname -m

# 3. 安装 Claude Desktop Extension（mcpb 一键安装）
#    在 Claude Desktop → Settings → Extensions → Install from file → 选择 .mcpb

# 4. 验证 MCP 服务
curl http://127.0.0.1:19789/mcp
# → 返回 MCP 协议握手 JSON

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// 通过 MCP 让 Agent 接管时间轴（伪代码，遵循 MCP 协议）
// Client: Claude Code / Codex / Cursor
// Server: palmier-pro running on http://127.0.0.1:19789/mcp

// 1. 加载资源
await client.request("resources/read", { uri: "palmier://timeline/current" });

// 2. 调用工具：插入一段 AI 生成视频
await client.request("tools/call", {
  name: "generate_clip",
  arguments: {
    prompt: "a drone shot over a misty mountain at sunrise, cinematic",
    model: "seedance-pro",
    duration_seconds: 8,
    insert_at: "00:00:12",
  },
});

// 3. 调用工具：自动添加字幕
await client.request("tools/call", {
  name: "auto_caption",
  arguments: { language: "en", style: "tiktok-bottom" },
});

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# Web（PWA）：直接访问 https://worldmonitor.app
# 桌面（Tauri 2 单一二进制）：从 GitHub Releases 下载对应平台

# 自托管
git clone https://github.com/koala73/worldmonitor
cd worldmonitor
docker compose up -d

# 配置本地 LLM（Ollama，零 API Key）
ollama pull llama3.1:8b

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// 订阅 CII 指数实时更新（伪代码）
import { WorldMonitor } from "@koala73/worldmonitor-sdk";

const wm = new WorldMonitor({ apiKey: process.env.WM_API_KEY });

wm.onCiiUpdate(({ country, score, signals }) => {
  if (country === "US" && score > 75) {
    console.warn(`US 风险飙升: ${score}`, signals);
    // 自动通知
  }
});

// 拉取指定国家 7 日新闻简报（AI 综合）
const brief = await wm.brief({
  country: "JP",
  days: 7,
  language: "zh",
  model: "ollama:llama3.1:8b",
});
console.log(brief.summary);

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# 直接克隆（或作为子模块）
git clone https://github.com/aishwaryanr/awesome-generative-ai-guide.git

# 用 mdbook / obsidian 渲染
cd awesome-generative-ai-guide
mdbook serve  # 如已安装 mdbook

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# 跟随 5 天 LLM 基础路线
cat roadmaps/5-day-llm/day-1-foundations.md
cat roadmaps/5-day-llm/day-2-transformers.md
cat roadmaps/5-day-llm/day-3-pretraining.md
cat roadmaps/5-day-llm/day-4-finetuning.md
cat roadmaps/5-day-llm/day-5-evaluation.md

# 直接跑 Agent 教程里的 RAG 示例
git clone https://github.com/aishwaryanr/awesome-generative-ai-guide.git
cd awesome-generative-ai-guide/tutorials/rag
pip install -r requirements.txt
python rag_demo.py --docs ./data --query "什么是 Agent？"

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# 克隆 Calendar 模板（推荐起点）
git clone https://github.com/BuilderIO/agent-native.git
cd agent-native/templates/calendar
npm install && npm run dev

# 给现有项目加 visual-plan / visual-recap skill
npx @agent-native/core@latest skills add visual-plan

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// 一个 Action 同时被 UI / Agent / HTTP / MCP / A2A / CLI 调用
import { defineAction } from "@agent-native/runtime";
import { z } from "zod";
import { db } from "./db";
import { replies } from "./db/schema";

export default defineAction({
  // 输入 schema（Zod）
  schema: z.object({
    emailId: z.string(),
    body: z.string().min(1).max(10_000),
  }),

  // 业务逻辑：一次定义，6 处复用
  run: async ({ emailId, body }) => {
    await db.insert(replies).values({ emailId, body });
  },

  // 可选：声明权限、速率限制、可观测性埋点
  policy: { actor: "user", rateLimit: "100/min" },
});

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# 通过 CLI 调用
npx agent-native run reply-email --payload '{"emailId":"abc","body":"Got it!"}'

# 通过 MCP（Claude Desktop 自动发现）
# 在 .mcp.json 加入：{ "agent-native": { "command": "agent-native", "args": ["mcp"] } }

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# Python 库
pip install headroom-ai

# CLI 代理模式（零代码集成）
headroom proxy --port 8787
# 让所有 OpenAI 兼容客户端通过 http://localhost:8787/v1/chat/completions 转发

# Agent 一键包壳
headroom wrap claude-code   # 透明压缩所有 Claude Code 调用
headroom wrap cursor
headroom wrap codex
headroom wrap aider

# MCP server 模式
# 在 .mcp.json 加入 headroom 即可获得 headroom_compress / headroom_retrieve 两个工具

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import headroom

# 1. 基础 JSON 压缩
raw_tool_output = {
    "users": [{"id": i, "name": f"user_{i}", "email": f"u{i}@example.com"} for i in range(1000)]
}
compressed = headroom.smartcrush(raw_tool_output)
print(f"压缩率: {compressed.ratio:.1%}, 节省: {compressed.saved_tokens} tokens")

# 2. CCR 可逆压缩（Agent 后续可恢复）
crushed = headroom.compress(
    raw_tool_output,
    algorithm="smartcrusher",
    reversible=True,
)
# Agent 需要原始数据时：
original = headroom.retrieve(crushed.handle)

# 3. AST 感知的代码压缩
code = open("big_file.py").read()
shrunk = headroom.codecompress(code, keep_signatures=True)
# 只保留函数签名和关键逻辑，省 70%+ token

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git clone https://github.com/calesthio/OpenMontage.git
cd OpenMontage
pip install -r requirements.txt

# 配置（可全开源模型）
cp config.example.yaml config.yaml
# 编辑 config.yaml 选择模型后端（默认支持 OpenAI/Anthropic/Ollama）

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from openmontage import Director, Brief

# 一句话 Brief
brief = Brief(
    topic="60 秒神经网络入门",
    audience="高中生",
    style="动画科普",
    duration_seconds=60,
    language="zh",
    output_path="./output/neural_net_101.mp4",
)

director = Director(brief=brief)
director.storyboard()      # 生成分镜脚本
director.produce_assets()  # 调用素材生成工具
director.edit()            # 合成最终视频
director.export()          # 导出到指定路径

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# CLI 模式
openmontage produce --brief "3 分钟的 Transformer 科普视频" --style animated

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# Transformers
pip install transformers torch
python -c "from transformers import AutoModelForCausalLM; m = AutoModelForCausalLM.from_pretrained('zai-org/GLM-5', torch_dtype='auto', device_map='auto')"

# SGLang（推荐生产部署）
pip install sglang[all]
python -m sglang.launch_server --model-path zai-org/GLM-5 --port 30000 --context-length 1000000

# vLLM
pip install vllm
vllm serve zai-org/GLM-5 --max-model-len 1000000

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from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("zai-org/GLM-5", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("zai-org/GLM-5", torch_dtype="auto", device_map="auto")

# 1M 上下文任务示例
prompt = open("huge_codebase.txt").read()[:900_000]   # ~270K tokens
messages = [
    {"role": "system", "content": "你是一个严谨的代码审查专家。"},
    {"role": "user", "content": f"以下是我的代码库，请审查潜在的安全问题：\n\n{prompt}"},
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to("cuda")
outputs = model.generate(inputs, max_new_tokens=4096, do_sample=True, temperature=0.7)
print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))

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mkdir my-flue-project && cd my-flue-project
npm init -y
npm install @flue/runtime valibot
npm install -D @flue/cli

# 初始化工作流目录
mkdir .flue/workflows

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// .flue/workflows/hello.ts
import { createAgent, type FlueContext } from "@flue/runtime";
import { local } from "@flue/runtime/node";
import * as v from "valibot";

// 1. 创建 Agent（本地沙箱 + 指定模型）
const agent = createAgent(() => ({
  sandbox: local(),
  model: "anthropic/claude-sonnet-4-6",
}));

// 2. 定义工作流 + 类型化输出
export async function run({ init, payload }: FlueContext) {
  const harness = await init(agent);
  const session = await harness.session();

  const { data } = await session.prompt(
    `Say hello to ${payload.name ?? "the user"} and share an interesting fact.`,
    {
      result: v.object({
        greeting: v.string(),
        fact: v.string(),
      }),
    },
  );

  return data;   // 强类型 { greeting: string; fact: string }
}

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# .github/workflows/hello.yml —— 把 Agent 接到 GitHub Actions
name: Hello Flue
on:
  issues:
    types: [opened]
jobs:
  hello:
    runs-on: ubuntu-latest
    permissions:
      issues: read
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with: { node-version: 22 }
      - run: npm ci
      - name: Run hello agent
        env:
          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
        run: npx flue run hello --target node --payload '{"name": "World"}'