AI Code Reviewer: Never Ship Bugs Again

说明与用途

在MacBook上运行本地Llama3.2模型消除了令牌成本，保持数据私密，并为代码审查和其他任务提供即时响应。

问题

Every token costs money, each API call adds up, and data is sent to cloud servers.

结果

Local LLM deployment eliminates API costs, keeps data private, and offers instant responses for code review and other AI tasks.

工作流

• Install Ollama via Homebrew
• Pull the llama3.2 model with ollama pull llama3.2
• Start the local server with ollama serve
• Send requests to http://localhost:11434/api/generate using the provided Python snippet

证据

Every token costs money. Every API call adds up. And your data goes to their servers.

来源

Now you have a GPT-4 level model running on your MacBook. Free. Private. Fast.

来源

brew install ollama ollama pull llama3.2 ollama serve

来源

import requests
def ask_local_ai(question): r = requests.post("http://localhost:11434/api/generate", json={"model":"llama3.2","prompt":question,"stream":False}) return r.json()["response"] # Works immediately, no API key needed

来源