GitHub热榜深度解读：public-apis飙升1588星背后的开源生态变迁

# 使用public-apis进行API发现和评估的自动化脚本
import requests
import asyncio
import aiohttp
from dataclasses import dataclass

@dataclass
class APIEvaluation:
    name: str
    category: str
    auth_required: bool
    https: bool
    cors_enabled: bool
    latency_ms: float
    is_alive: bool

async def evaluate_api(session, api_entry: dict) -> APIEvaluation:
    """评估单个API的可用性"""
    url = api_entry.get('Link', '')
    if not url:
        return APIEvaluation(api_entry.get('API', ''), api_entry.get('Category', ''),
                           False, False, False, 0, False)
    
    try:
        import time
        start = time.time()
        async with session.head(url, timeout=aiohttp.ClientTimeout(total=5)) as resp:
            latency = (time.time() - start) * 1000
            return APIEvaluation(
                name=api_entry.get('API', ''),
                category=api_entry.get('Category', ''),
                auth_required=bool(api_entry.get('Auth')),
                https=url.startswith('https'),
                cors_enabled='access-control-allow-origin' in resp.headers,
                latency_ms=round(latency, 1),
                is_alive=resp.status < 500
            )
    except Exception:
        return APIEvaluation(api_entry.get('API', ''), api_entry.get('Category', ''),
                           False, False, False, 0, False)

async def discover_apis(category: str = "AI"):
    """从public-apis发现并评估指定类别的API"""
    # 获取public-apis数据
    resp = requests.get(
        "https://raw.githubusercontent.com/public-apis/public-apis/master/README.md"
    )
    
    # 解析Markdown表格（简化版）
    apis = []
    in_section = False
    for line in resp.text.split('
'):
        if f'### {category}' in line:
            in_section = True
            continue
        if in_section and line.startswith('### '):
            break
        if in_section and '|' in line and 'API' not in line.split('|')[1]:
            parts = [p.strip() for p in line.split('|')[1:-1]]
            if len(parts) >= 4:
                apis.append({
                    'API': parts[0],
                    'Description': parts[1],
                    'Auth': parts[2],
                    'HTTPS': parts[3],
                    'Link': parts[4] if len(parts) > 4 else ''
                })
    
    # 并发评估API可用性
    async with aiohttp.ClientSession() as session:
        tasks = [evaluate_api(session, api) for api in apis[:20]]
        results = await asyncio.gather(*tasks)
    
    # 按延迟排序输出
    alive = [r for r in results if r.is_alive]
    alive.sort(key=lambda x: x.latency_ms)
    
    print(f"发现{len(apis)}个API，{len(alive)}个可用")
    for api in alive[:5]:
        print(f"  {api.name}: {api.latency_ms}ms | "
              f"HTTPS:{api.https} | CORS:{api.cors_enabled}")
    
    return alive

// Cordis框架概念示例
import { defineApp, temporal, spatial } from '@cordis/core';

const app = defineApp({
  // 时间维度：事件流处理
  temporal: temporal({
    events: {
      'user.signup': async (event, ctx) => {
        await ctx.spatial.broadcast('notifications', {
          type: 'welcome',
          userId: event.userId
        });
      },
      'order.created': async (event, ctx) => {
        // 跨空间维度的状态同步
        await ctx.spatial.update('inventory', event.items);
        await ctx.spatial.notify('warehouse', event.shippingAddress);
      }
    },
    // 时间窗口聚合
    windows: {
      'user_activity_5min': {
        size: '5m',
        aggregate: (events) => events.length
      }
    }
  }),
  
  // 空间维度：分布式状态管理
  spatial: spatial({
    regions: ['us-east', 'eu-west', 'ap-south'],
    state: {
      inventory: { type: 'replicated', consistency: 'strong' },
      sessions: { type: 'partitioned', consistency: 'eventual' }
    }
  })
});

app.start();

# Needle快速上手
# 安装: pip install cactus-needle

from cactus_needle import Needle

# 加载模型（14MB，首次下载后缓存）
model = Needle()

# 离线推理：不需要网络连接
response = model.chat(
    messages=[
        {"role": "system", "content": "你是一个智能家居助手"},
        {"role": "user", "content": "把客厅灯光调暗到30%"}
    ],
    tools=[
        {
            "name": "set_light_brightness",
            "description": "设置灯光亮度",
            "parameters": {
                "room": {"type": "string"},
                "brightness": {"type": "integer", "min": 0, "max": 100}
            }
        }
    ]
)

# Needle会返回工具调用指令
print(response.tool_calls)
# [{'name': 'set_light_brightness', 'arguments': {'room': '客厅', 'brightness': 30}}]

# ToolJet Docker Compose部署
version: '3.8'
services:
  tooljet:
    image: tooljet/tooljet-ce:latest
    ports:
      - "8080:8080"
    environment:
      - TOOLJET_HOST=http://localhost:8080
      - PG_USER=tooljet
      - PG_PASSWORD=secure_password
      - PG_DB=tooljet
      - REDIS_HOST=redis
      - SECRET_KEY_BASE=your-secret-key
    depends_on:
      - postgres
      - redis
    
  postgres:
    image: postgres:16
    environment:
      - POSTGRES_USER=tooljet
      - POSTGRES_PASSWORD=secure_password
      - POSTGRES_DB=tooljet
    volumes:
      - pgdata:/var/lib/postgresql/data
    
  redis:
    image: redis:7-alpine

volumes:
  pgdata:

# Unsloth本地训练示例（命令行版本）
from unsloth import FastLanguageModel

# 加载基础模型
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/Llama-3.1-8B",
    max_seq_length=4096,
    load_in_4bit=True,  # 4-bit量化，减少显存需求
)

# 添加LoRA适配器
model = FastLanguageModel.get_peft_model(
    model,
    r=16,
    target_modules=[
        "q_proj", "k_proj", "v_proj", "o_proj",
        "gate_proj", "up_proj", "down_proj"
    ],
    lora_alpha=16,
    lora_dropout=0.05,
)

# 准备训练数据
train_data = [
    {
        "instruction": "将以下文本翻译成英文",
        "input": "今天天气很好",
        "output": "The weather is nice today."
    },
    # ... 更多训练样本
]

# 开始训练
from trl import SFTTrainer
trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=train_data,
    dataset_text_field="text",
    max_seq_length=4096,
    args={
        "per_device_train_batch_size": 2,
        "gradient_accumulation_steps": 4,
        "warmup_steps": 5,
        "max_steps": 60,
        "learning_rate": 2e-4,
        "fp16": True,
    },
)
trainer.train()

# 保存模型
model.save_pretrained("my-finetuned-model")