Slowth Money: a local AI agent for your financial goals

Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady Submission 🦥

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady

Personal finances can often be a source of stress.

Money management can have a big psychological impact. It consumes a lot of our attention.

This is why I built Slowth Money, a calm, local-first savings and financial goal-planning desktop app that leverages open-source AI to define strong boundaries between public resource sharing and private data kept on your local machine.

Slowth Money tries to minimize the stress of financial planning by embracing laziness through various forms:

Feel free to fork it, experiment with it, and share feedback.

You could try adding your own country pack to run it from your own home for example.

Disclaimer: This is a proof of concept. It may contain bugs and incomplete features. Do not rely on it as financial advice.

No web deployment or cloud backend is required. The projects runs locally as an Electron desktop app.

Slowth Money is a calm, local-first savings and goal-planning desktop app. Canada is the proof-of-concept market for the current MVP: its registered-account wrappers, public institutions, and deposit products provide a concrete way to validate the privacy and planning architecture. The longer-term design can support other markets through country-specific packs, provider adapters, schemas, and UI, but only Canada is implemented today.

Slowth Money helps answer four questions without turning personal finance into another feed to monitor:

The hackathon MVP runs as an Electron app on macOS, keeps the private financial profile encrypted locally, performs financial math in deterministic TypeScript, and uses an open-weight model through local Ollama only when language reasoning adds value.

The internet knows what financial products exist. The local model knows your financial situation. Neither…