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•January 9, 2026

Bucketwise Planner: self-hosted budgeting app (Barefoot Investor method) — multi-user, fortnight-based, optional AI advisor

Quick Overview

New side projects offer self-hosted, AI-enhanced financial management solutions, including a Barefoot Investor budgeting app, an AI-first invoicing platform, and a Stripe revenue monitoring tool.

Bucketwise Planner: A self-hosted, multi-user budgeting app implements the Barefoot Investor method with fortnight-based budgeting and an optional AI advisor.
AI-First Invoicing Platform: An open-source, AI-native tool for personal and business invoicing that stores data in Markdown files and processes PDFs.
Stripe Revenue Monitor: A side project designed for subscription businesses to detect revenue issues such as failed payments and uncoverted trials early.
AI Integration: AI is leveraged for advisory functions, invoice generation, and even as a primary tool for codebase development.
User Control: Projects emphasize self-hosting and local data ownership to provide users with greater control over their financial information.
Community Collaboration: All tools are shared as side projects actively seeking community feedback and contributions for ongoing development and refinement.

Key Points

Key Details

'Bucketwise Planner' is a self-hosted budgeting app implementing Scott Pape's Barefoot Investor method.
It supports multi-user environments with 'JWT signup/login' and offers 'per-instance independence' (no SaaS).
The budgeting system is fortnight-based, aligning with biweekly income, and uses specific 60/10/10/20 buckets for allocations like 'Daily Expenses', 'Splurge', 'Smile', and 'Fire Extinguisher'.
Key features include a debt snowball payoff plan with a 'timeline in fortnights' and comprehensive management of income, expense, and debt payments with filtering and tags.
An optional AI advisor, disabled by default, can be enabled with a 'GEMINI_API_KEY' to provide financial guidance.

Methodological Foundation

The 'Bucketwise Planner' is built upon Scott Pape's Barefoot Investor method, a widely recognized personal finance framework.
This methodology dictates a specific allocation of income into four primary buckets: 'Daily Expenses' (60%), 'Splurge' (10%), 'Smile' (10%), and 'Fire Extinguisher' (20%), which are configurable within the application.
A core component of the method incorporated into the app is the debt snowball approach. This strategy prioritizes paying off smaller debts first to build momentum, with the planner providing a 'timeline in fortnights' for users to follow for debt repayment.

Technical Aspects & Development Approach

The application is primarily deployed via Docker Compose and offers a modern tech stack including 'Node.js + Express + TypeScript (DDD)', 'PostgreSQL', 'React + Vite + Mantine', and 'Vitest tests'.
A distinctive development aspect is that the codebase is 'mostly AI-written' using tools like Copilot/GPT, but with developer-guided 'strict prompting, clear principles, and guardrails'.
The project is open-source, released under an MIT License, and the developer actively seeks community feedback, issues, and PRs for logical accuracy, UX improvements, and edge case handling.
Collaboration is welcomed on features such as a 'shared types library', 'analytics & charts', 'logo/social preview', and 'recurring transactions and templates'.

Outline

Introduction to Bucketwise Planner

Project Overview

Motivation and Problem Solved

Development Philosophy and Codebase Origin

Key Features and Methodology

Barefoot Investor Method Implementation

Budgeting Structure

Debt Management

Transaction Management

Technical Architecture and Implementation

Core Technologies

Deployment and Hosting

User Management

Licensing

AI Integration and Capabilities

Optional AI Advisor

AI-First Approach in Other Related Tools

AI Data Handling in Related Tools

Community Engagement and Future Development

Call for Feedback and Contributions

Areas for Collaboration

Open Source Repositories

General Security and Data Ownership Considerations

Commitment to Safety and Security

Data Ownership in AI-First Solutions

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