Key Takeaways:
- Transform your local LLM setup into a practical personal finance analyzer
- Build a privacy-first solution that keeps sensitive financial data on your machine
- Learn batch processing strategies for handling large transaction datasets
- Get working code to create your own AI financial assistant
Prerequisites
- Completed setup from Part 1 (Ollama installed, GPU configured)
- Basic Python knowledge
- Ubuntu/Linux system with NVIDIA GPU (8GB+ VRAM)
- A healthy paranoia about cloud services handling your financial data
If you read my last article, „My Dive into Local LLMs, Part 1: From Alexa Curiosity to Homegrown AI,“ you know I’ve been on a bit of a journey, diving headfirst into the world of local Large Language Models (LLMs) on my trusty Ubuntu machine. That initial curiosity, spurred by my work on the Alexa team, quickly turned into a fascination with the raw power and flexibility of running AI right on your own hardware. But beyond the sheer „cool factor“ of getting Llama 3 to hum on my GPU, I started thinking about practical applications – problems in my daily life where this homegrown AI could actually make a difference.
That’s when personal finance popped into my head. Now, before you mentally flag me for suggesting you feed your bank statements to an AI, hear me out. We’re bombarded with cloud-based financial tools, and while convenient, they often come with a lingering question: Where exactly is my data going and what are they doing with it? For something as sensitive as personal finances, data privacy isn’t just a buzzword; it’s paramount. This is where the local LLM truly shines, offering a compelling alternative to cloud-dependent solutions.