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Cover image for LeakLens: A Swiss-Editorial Financial Autopilot Powered by TabPFN-3.5 That Audits Messy Bank CSVs
Devasish Mishra
Devasish Mishra

Posted on AI-assisted

LeakLens: A Swiss-Editorial Financial Autopilot Powered by TabPFN-3.5 That Audits Messy Bank CSVs

#devchallenge #hf26challenge #opensource #ai

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").

Links & Quick Links

(Judge Quickstart: Click *01 INDIE DEV SAAS, **02 STUDENT TRIAL CREEP, or 03 FREELANCER CASHFLOW at the top of the workbench to run real in-context TabPFN inference with zero CSV upload required!)*

1. The Problem: The $133/Month "Laziness Tax"

Every month, millions of us download raw bank, credit card, or UPI statement CSVs with the intention of doing a proper audit. And almost every month, those files sit unopened in ~/Downloads.

Manually wrangling messy bank descriptors (POS DEB STRIPE*RENDER-SRV, TEMP AUTH COURSERA*PLUS), calculating irregular billing cadences, spotting silent price creep, and projecting runway in a spreadsheet takes hours of tedious mental labor.

According to C+R Research’s Consumer Subscription Study, the average consumer estimates they spend $86/month on recurring subscriptions, while their actual itemized spend is $219/month—a $133/month ($1,596/year) blind spot, with 42% of consumers admitting they still pay for subscriptions they completely forgot existed.

Existing solutions fail the "Slow and Steady" test:

  1. Paid Trackers (The Subscription Irony): Services like Rocket Money charge up to $144/year (becoming another subscription!) while requiring you to hand over live bank login credentials to third-party aggregators.
  2. Manual Spreadsheets: Writing regex, pivot tables, and balance formulas feels like unpaid homework, so we procrastinate.
  3. Closed Cloud Chatbots: Uploading raw financial statements to commercial LLMs compromises privacy, and text-based generative models are notorious for arithmetic hallucinations on structured ledger tables.

2. What I Built: LeakLens (// 03)

LeakLens is an open-weight financial autopilot built around Prior Labs' TabPFN-3.5 and an open-weight legal drafter.

It takes the core spirit of Hacktoberfest Week 3 ("Slow and Steady"): rewarding user laziness by automating a dreaded, cognitive-draining chore so the user does nothing while the system works.

Instead of another generic dark-mode chatbot with neon gradients, LeakLens is designed as a Swiss Editorial Financial Instrument—grounded in warm alabaster paper (#F6F4EE), exposed 1px architectural gridlines, high-contrast Instrument Serif editorial headlines, and monospace JetBrains Mono tabular figures.

You drop a raw statement CSV (or click a persona preset), and LeakLens immediately executes a 3-Layer Hybrid Pipeline:

[ Raw Statement CSV / Persona Preset ]
│
▼
┌───────────────────────────────────────────────────────────┐
│ LAYER 1: Deterministic Symbolic Engine (Pandas) │
│ • Noise & payment processor stripping │
│ • Living expense quarantine (Rent, Groceries, Dining) │
│ • Exact cadence regularity, price drift %, duplicate cycle│
└─────────────────────────┬─────────────────────────────────┘
│
▼
┌───────────────────────────────────────────────────────────┐
│ LAYER 2: TabPFN-3.5 Predictive Core (Prior Labs) │
│ • TabPFNClassifier: 7-feature in-context leak probability │
│ • TabPFNRegressor: 90-day cash runway quantile forecasting│
│ (p10, p50 median, p90 uncertainty fan) │
└─────────────────────────┬─────────────────────────────────┘
│
▼
┌───────────────────────────────────────────────────────────┐
│ LAYER 3: Autonomous Action Studio (Groq / Open-Weights) │
│ • FTC 16 CFR Part 425 & ROSCA Cancellation Notices │
│ • Price-Match Negotiation & Downgrade Scripts │
│ • RFC-5545 .ics Calendar Renewal Shields │
└───────────────────────────────────────────────────────────┘

3. Signature UI/UX & Design Thinking

To ensure LeakLens felt like a calm, precision financial instrument rather than AI slop, I built four custom micro-interactions:

  1. 550ms Laser CSV Deconstruction (CsvDeconstructTransition): When switching personas or uploading a statement, the interface sweeps raw monospace transaction lines with a vermilion laser scanline before physically morphing into grouped merchant cards.
  2. Tactile Hardware Kill-Switches & Spring Odometer: Toggling any detected leak on the ledger immediately strikes through the monthly cost, applies an archival emerald tint (#E6F4EF), and rolls the Reclaimed Annual Cash counter using Framer Motion spring physics (stiffness: 380, damping: 32).
  3. Live 90-Day TabPFN Runway Simulator: An interactive SVG canvas displays the baseline cash balance trajectory (dashed vermilion) alongside the solid emerald Reclaimed Path. As you toggle kill-switches, the green line and the shaded +$X,XXX Recovery Wedge dynamically expand in real time.
  4. "Verify Before You Cancel" Trust Box: Rather than pretending AI is infallible, LeakLens explicitly highlights what the CSV mathematically proves versus subjective context you should verify (e.g., "CSV proves charge jumped +52.6%, but cannot verify if your team intentionally upgraded compute tiers").

4. Why Open Innovation Matters for What I Built

Financial statements represent the single most sensitive digital artifact a human produces—exposing where you live, eat, travel, and spend money.

  1. Privacy Without Compromise: Auditing your personal finances should never require uploading your bank history to a closed cloud vendor. LeakLens is architected with complete offline capability: the entire FastAPI + TabPFN-3.5 + open-weight pipeline can run locally on an Apple Silicon or Linux machine (Local Private Runtime) with zero external network calls.
  2. Foundation Models Engineered for Tables: General-purpose text LLMs struggle with multi-quantile regression and calibrated tabular probabilities. Prior Labs' TabPFN-3.5 represents true open-weight innovation: a tabular foundation model trained on synthetic Bayesian priors that performs instant in-context classification and non-parametric quantile forecasting without days of hyperparameter tuning.
  3. The $0 Personal FinOps Era: By building on open weights and open-source infrastructure, LeakLens proves that personal financial autonomy can be delivered at zero marginal cost forever—without charging the user a subscription to manage their subscriptions.

5. Partner Category Submissions

Best Use of TabPFN ($200)

LeakLens uses TabPFN-3.5 as the analytical brain of the application in two ways:

  • TabPFNClassifier (Leak Risk Scoring): Evaluates a 7-dimensional feature vector (amount_zscore, cadence_regularity, price_drift_pct, is_micro_charge, charge_count_90d, days_since_first_seen, is_trial_conversion_pattern) against in-context prior distributions to assign calibrated leak probabilities (e.g., 99.9% on sudden API spikes vs 0.0% on essential living expenses).
  • TabPFNRegressor (Multi-Quantile Runway Forecasting): Generates 13 weekly forecast points over 90 days with native p10, p50, and p90 uncertainty bounds directly driving our interactive SVG simulator.

Best Use of Render ($200)

LeakLens is deployed on Render as a decoupled full-stack architecture:

  • Backend Service (leaklens-api): Python 3.11 FastAPI web service hosting the deterministic Pandas rules engine and TabPFN client runtime.
  • Frontend Service (leaklens-web): Next.js 15 (App Router, Tailwind CSS, Framer Motion) communicating with the backend via environment variables.

Built with care for Hacktoberfest 2026. Code is open-source under the MIT License.

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