I let Gemma 4 analyze my credit card statements so I wouldn't have to

The article describes Swipey, a local-first, privacy-focused web app that uses Google's Gemma 4 AI model to analyze credit card spending across multiple banks. By uploading CSV transaction files from Chase or Capital One, the app generates monthly digests with spending spotlights, patterns, suggestions, and editable transaction groupings. The author notes that Gemma 4 served as a capable open-source replacement for Claude in this workflow, successfully categorizing transactions and surfacing spending insights.

This is a submission for the Gemma 4 Challenge: Build with Gemma 4 Swipey, a local-first, privacy focused web app for people juggling multiple credit cards across multiple banks. Drag in a CSV of transactions from Chase or Capital One, pick a month, and Gemma 4 produces a digest of that month's spending: a spotlight, a few patterns, and a few suggestions. It also proposes "groups" that bundle similar transactions and sum the spend, which you can edit inline. The problem: I have four credit cards. I enjoy collecting credit card reward points anyone else? to help subsidize travel costs 1. This usually means chasing different category bonuses on each one for dining, another for travel, a third for everything else . Past two or three cards, no single bank app shows you the whole picture, and the interesting questions "what was my biggest category this month?", "how does this compare to last month?" get hard to answer. The first version shipped transaction-level data to Claude every month. Swipey on Gemma 4 keeps the same workflow but runs inference on Cloudflare Workers AI. Note: Transaction data was mocked for demo purposes Monthly insights digest spotlight, patterns, suggestions Transaction grouping that bundles similar merchants/themes and sums their spend A local-first, privacy-focused way to manage credit card spend across multiple banks, built with Next.js and PostgreSQL. Supports Chase and Capital One CSV exports. ⚠️ Work in progress, local use only. This project is intended to run on your own machine against a local database. API routes have no authentication. If hosted, anyone who can reach the server can read or modify your transactions. The Docker Compose setup ships with default dev credentials that are not safe for any deployed environment. Don't expose this to the public internet as-is. Model: @cf/google/gemma-4-26b-a4b-it the 26B MoE variant on Cloudflare Workers AI. This is the only Gemma 4 variant Cloudflare hosts as of May 2026, but here's how I would have picked between variants: One caveat during the migration: Gemma 4 needed a stricter setup than Claude to emit my parser's format reliably. The fix was a system prompt insisting on the exact format, plus wrapping the user prompt in XML with an <output block showing the shape to mirror. Since migration, every run has parsed cleanly. What stood out most is how capable open-source models like Gemma 4 have become at the kind of work Swipey leans on: categorizing/grouping transactions, summarizing a month of spend, and surfacing patterns. For this use case, Gemma 4 was a quality drop-in replacement against Claude. As these open-source models keep improving, I expect that gap to keep narrowing. Swipey has already surfaced spending habits I didn't even think of to track. It's an incredibly exciting time to see how AI-powered features augment into existing workflows and surface insights I would have otherwise missed. Now if you'll excuse me, Gemma 4 has some thoughts about my dining out habits that I have to reflect on. Cya Careful. This only works if the balance is paid off in full each month. Otherwise the interest eats the perks many times over. The rewards stack up to something real if done responsibly. ↩