Personal AI Assistant That Learns About You

A JPMorgan Chase vice president, Amrendra Mishra, built a suite of 45 open-source AI tools that run locally on a MacBook via Ollama and the llama3.2 model, avoiding per-token API costs and sending no data to third-party servers. The setup uses a local REST endpoint at localhost:11434 and requires no API key, with a comparison table covering llama3.2, codellama, mistral, phi3 and gemma2 for different task types.

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