PassionLedger AI: I Turned My Cashbook Into a Funding Engine for Dreams

asprojects — Sat, 11 Jul 2026 14:50:32 +0000

This is a submission for Weekend Challenge: Passion Edition

What I Built
I built PassionLedger AI, an offline-first personal finance application that helps people turn their everyday financial data into a realistic plan for something they genuinely care about.

Most cashbook applications tell users where their money went.

PassionLedger AI tries to answer a different question:

How can the money I have today help me build the future I care about?

A user can create a passion goal such as:

Building a home recording studio
Buying a professional camera
Joining an educational course
Starting a side project
Publishing a book
Planning a family trip
Starting a small business
Supporting a community initiative

The application analyses income, expenses, savings and financial surplus, then shows:

Current goal progress
Remaining amount
Required monthly contribution
Safe monthly contribution
Funding gap
Estimated completion date
Financial feasibility
Baseline and simulated savings projections

It also includes a What-If Simulator where users can explore how reducing optional spending, increasing income or extending the goal period may affect the completion date.

Why I built it

I have maintained personal financial records for several years.

My cashbook helped me understand my income, expenses, loans, savings and financial commitments. However, it mainly explained the past.

It could tell me how much I spent and how much remained, but it could not show me how my current financial position could support a meaningful goal.

That inspired me to transform my cashbook into PassionLedger AI.

The goal was not to create another generic expense tracker or an AI chatbot. I wanted to create a privacy-conscious planning tool that connects real financial information with human ambition.

Demo

Live Application

https://cashbook-snowy.vercel.app/

For the fastest experience:

Open Passion AI from the sidebar or Dashboard.
Click Try the 60-Second Demo.
Explore the sample home recording studio goal.
Adjust the What-If Simulator.
Generate the AI-powered passion plan.
Download the plan as a PDF.

The demo loads an isolated scenario:

Goal: Build a home recording studio
Target amount: ₹60,000
Current savings: ₹8,000
Desired period: 8 months
Average monthly income: ₹45,000
Average monthly expenses: ₹40,500

The demo data does not overwrite or modify the user’s real cashbook records.

Suggested Demo Flow
Start the demo and observe the goal progress, monthly contribution requirements and projected completion date.

Then increase the optional spending reduction using the What-If Simulator. The financial metrics and runway chart update immediately without calling the AI service.

Finally, select Generate My AI Passion Plan to receive practical suggestions, milestones, first-week actions and personalised encouragement.

Code

GitHub Repository

https://github.com/afsalprmric/cashbook

The repository contains:

The React and TypeScript frontend
Dexie and IndexedDB local database
Deterministic finance calculation engine
What-If Simulator
Recharts savings runway chart
Gemini-powered planning endpoint
Zod request and response validation
Local fallback plan generator
PDF export service
Demo data and challenge documentation

How I Built It

Core Technology

The application was built using:

React 19
TypeScript
Vite
React Router
Dexie.js
IndexedDB
Recharts
Google Gen AI SDK
Zod
Vercel Serverless Functions
jsPDF
jspdf-autotable
date-fns
Custom responsive CSS

Offline-First Architecture

PassionLedger AI is built on an offline-first cashbook application.

Financial data is stored locally in the browser using Dexie.js and IndexedDB. This means users can continue using the core application even without an internet connection.

The following features work locally:

Goal creation
Financial analysis
Progress tracking
What-If Simulator
Savings projections
Local fallback plan
PDF export

Internet access is required only for Gemini-powered guidance and optional Google synchronisation.

Deterministic Financial Calculations

I did not use AI to perform the core financial calculations.

All important figures are calculated locally using TypeScript:

Average monthly income
Average monthly expenses
Monthly surplus
Safe monthly contribution
Required monthly contribution
Funding gap
Goal progress
Estimated completion period
Feasibility status

For example:

Remaining amount =
Target amount - Current savings
Required monthly contribution =
Remaining amount / Available months
Safe monthly contribution =
80% of Average monthly surplus

The locally calculated values remain the source of truth.

This separation improves reliability because Gemini is not asked to invent or calculate financial numbers.

What-If Simulator

The simulator allows users to adjust:

Optional spending reduction
Expected additional monthly income
Goal duration

The application instantly recalculates the completion timeline and financial feasibility.

No Gemini request is made while the sliders are being adjusted.

This makes the simulator fast, private and usable offline.

Savings Runway Chart

I used Recharts to display:

Current savings
Baseline savings projection
Simulated savings projection
Target amount
Expected completion timeline

This helps users understand their financial path visually instead of reading only numbers.

Google Gemini Integration

Gemini acts as the planning intelligence behind PassionLedger AI.

After the user explicitly requests an AI plan, the frontend sends a compact financial summary to a Vercel serverless endpoint.

The summary includes:

Goal name and reason
Target amount
Current savings
Desired period
Average monthly income
Average monthly expenses
Safe monthly contribution
Verified financial metrics
Top expense categories
User-selected flexible categories

Gemini then generates:

A concise feasibility explanation
Practical suggestions
A recommended contribution strategy
Funding milestones
First-week actions
Personalised encouragement

The AI response is returned as structured JSON and validated using Zod before being displayed.

Privacy-First AI Design

Personal finance information is highly sensitive, so I created a strict privacy boundary.

The application does not send the following information to Gemini:

Complete transaction records
Transaction notes
Attachments
Payee names
Phone numbers
Credit card details
Bank information
Google Drive tokens
Raw database backups

Only aggregated financial information is sent, and only after the user requests an AI plan.

The Gemini API key is stored server-side as a Vercel environment variable and is not included in the frontend bundle.

Local Fallback Engine

External AI services can fail because of:

Network problems
Quota limits
Missing configuration
Invalid responses
Temporary service unavailability

PassionLedger AI therefore includes a local deterministic fallback engine.

When Gemini is unavailable, the application still generates:

Feasibility status
Suggested contribution
Practical actions
Funding milestones
First-week checklist
Encouragement

The user is never left with a broken screen.

PDF Export

Users can download their passion plan as a PDF.

The report contains:

Goal information
Progress
Financial metrics
Feasibility
Suggestions
First-week actions
Milestones
Generation source
Privacy note
Financial guidance disclaimer

The PDF does not contain private raw transactions.

Existing Cashbook Capabilities

PassionLedger AI is built on a larger offline-first Cashbook application that already supports:

Income and expense tracking
Category management
Payment modes
Loan and repayment tracking
Credit cards and EMIs
Investments
Bank reconciliation
PDF and Excel reports
JSON backup and restoration
Google Drive and Google Sheets synchronisation

Application Architecture
React Interface
↓
Local Finance Calculator
↓
Dexie / IndexedDB
↓
Privacy-Filtered Aggregate Summary
↓
Vercel Serverless Function
↓
Google Gemini API
↓
Zod Response Validation
↓
Structured Passion Plan

Challenges I Faced
Keeping AI advice reliable

A language model should not control exact financial calculations.

I solved this by performing the calculations locally and using Gemini only to explain verified results and create an actionable plan.

Protecting private financial data

A personal finance app may contain transaction notes, contacts, card details and other sensitive information.

I created an aggregation layer that removes private details before any information reaches Gemini.

Supporting AI failure

A demo or real application should not become useless when an external service fails.

The local fallback engine ensures that the core experience remains functional.

Integrating the feature into an existing app

The original cashbook already had several modules and a versioned IndexedDB database.

I added PassionLedger AI using an additive Dexie migration without deleting or resetting existing user data.

What I Learned

This project taught me that meaningful AI integration is not about sending the largest possible amount of data to a model.

It is about deciding:

What should be calculated locally
What the AI genuinely needs
What data must remain private
How the AI response should be validated
How the product should behave when AI is unavailable

I also learned that financial planning becomes more motivating when it is connected to a meaningful human goal rather than presented only as restrictions and numbers.

What I Am Proud Of

I am especially proud of:

Connecting personal finance with passion
Keeping financial calculations deterministic
Creating a privacy-first Gemini integration
Making the core experience work offline
Building a one-click demo for judges
Adding a practical What-If Simulator
Supporting both Gemini and local fallback plans
Generating downloadable passion-plan reports
Future Improvements

Future versions could include:

Multiple passion goals
Goal prioritisation
Family-shared goals
Voice-based transaction entry
Monthly AI progress reviews
Passion-goal backup to Google Sheets
Shareable milestone cards
Advanced cash-flow forecasting
Recurring contribution reminders
More language options

Prize Categories

Best Use of Google AI

I am submitting PassionLedger AI for Best Use of Google AI.

Gemini is a meaningful part of the application’s planning experience.

It receives a privacy-filtered summary of locally verified financial metrics and transforms them into:

Practical suggestions
Funding milestones
First-week actions
Feasibility explanations
Personalised encouragement

The application does not use Gemini as a decorative chatbot.

Local TypeScript calculations remain responsible for financial accuracy, while Gemini provides the human-readable planning and motivation layer.

This combination allows PassionLedger AI to remain reliable, useful and privacy-conscious.

Final Thought

PassionLedger AI is based on a simple belief:

Money should not only record where life has been. It should help build where life is going.

PassionLedger AI — turn everyday spending into a realistic plan for the dream you care about.

DEV Community: asprojects

PassionLedger AI: I Turned My Cashbook Into a Funding Engine for Dreams

Demo

Code

How I Built It

Prize Categories