Voice Commander: Control Your Mac with Hey Jarvis

A developer at JPMorgan Chase built 45 AI tools that run locally on a MacBook using Ollama and open-source models like Llama 3.2, eliminating API costs and privacy concerns. The tools, available on GitHub, demonstrate how to run GPT-4-level models for free and privately.

Every token costs money. Every API call adds up. And your data goes to their servers. brew install ollama ollama pull llama3.2 ollama serve Now you have a GPT-4 level model running on your MacBook. Free. Private. Fast. python 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 answer = ask local ai "Explain Docker in one paragraph" print answer | Model | Size | Best For | |---|---|---| | llama3.2 | 2GB | General use | | codellama | 4GB | Code tasks | | mistral | 4.4GB | Fast reasoning | | phi3 | 2.2GB | Lightweight tasks | | gemma2 | 5.4GB | Complex reasoning | I built 45 tools using this stack. All free. All local. All open source. github.com/amrendramishra/ai-tools https://github.com/amrendramishra/ai-tools VP at JPMorgan Chase. Building AI tools at amrendranmishra.dev