This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
Shoebox is a receipt sorter for gig workers and freelancers that runs entirely on their own laptop.
If you drive for Uber, deliver for DoorDash, or freelance on the side, the IRS treats you as a small business. Every gas fill-up, phone bill and box of printer ink can lower your tax bill, but only if you can show it. So you end up with a literal shoebox, or a camera roll of 400 crumpled receipts, and in April you have two bad options:
Shoebox is a third option. You put the photos in a folder and run one command. A vision model running on your own machine reads each receipt, files it under the matching line of IRS Schedule C (the tax form for self-employment income), and writes one sentence explaining why. You look the results over on a single page, fix anything that's off, and export a CSV your accountant or tax software can use.
Who would pay: the tens of millions of Americans who earn some of their income as independent workers. They aren't big enough to hire a bookkeeper, and their receipts are too personal to hand to a startup. I'd price it at $19, paid once. That price only works because each receipt costs nothing to process. More on that below.
Here's a run on a sample shoebox of seven receipts:
$ python3 shoebox.py ./receipts --work "freelance graphic designer who also drives rideshare"
[1/7] reading adobe.png ... Adobe Inc. $64.85 -> 18 Office expense
[2/7] reading carwash.jpg ... MISTER CAR WASH $39.99 -> 9 Car and truck expenses
[3/7] reading coffee.jpg ... STARBUCKS $14.48 -> 24b Meals (50%)
[4/7] reading gas.jpg ... SHELL $41.33 -> 9 Car and truck expenses
[5/7] reading groceries.jpg ... TRADER JOE'S #172 $22.93 -> Personal (not deductible)
[6/7] reading phone.jpg ... T-MOBILE $101.64 -> 25 Utilities
[7/7] reading staples.jpg ... STAPLES $60.29 -> 22 Supplies
The coffee result is my favorite. The receipt had a note printed on it, "Met client re: logo revisions." The model used it to file the purchase as a business meal and applied the 50% limit for meals. It also left the groceries out of the deductions.
The review page shows each receipt photo next to its extracted fields. The Schedule C totals update live as you make corrections, and anything suspicious, like a $0 total, is outlined in orange:
A receipt sorter for gig workers and freelancers that runs entirely on your laptop Drop receipt photos in a folder. A local vision model (Qwen3-VL 8B via Ollama) reads each one files it under the right IRS Schedule C line, and explains why. You check the results on one page and export a CSV for your accountant.
Nothing leaves the machine. No account, no upload, no per-receipt fee.
ollama pull qwen3-vl:8b
python3 shoebox.py ./my_receipts --work "rideshare driver"
Then open http://localhost:8765. Correct anything that looks off, tick Looks right, and hit Export CSV.
--work tells the model what you do. A car wash is a business expense for a driver and personal for a designer.--model swaps in any Ollama vision model.shoebox.json inside the folder, keyed by image hash, so re-runs only read new photos.
It's two files: shoebox.py (about 200 lines of Python using only the standard library, with Pillow optional) and review.html (one page, no framework). There's also make_samples.py, which generates fake receipts so you can try it without sharing your own.
Model: Qwen3-VL 8B, an open-weight vision-language model, running locally in Ollama on a MacBook (M5 Pro, 24 GB).
Pipeline:
/api/chat with a format. Ollama enforces the schema at decode time, so the model can only return a valid object, and its category must be one of the Schedule C lines.127.0.0.1 only, serves the review page and the CSV export.
The --work flag matters more than it looks. A car wash is a business expense for a rideshare driver and a personal one for a designer who works from home. The same model gives the right answer for each person because it's told who it's working for.
1. The model chose before it explained. My first schema listed category before reason. For a gas receipt, the model filed it as Personal, then wrote a reason arguing it was a deductible vehicle expense. The output is generated left to right, so it had committed to an answer before it thought it through. Moving reason above category in the schema made it explain first and then decide. I also added a one-line description of each Schedule C line to the prompt, because an 8B model doesn't know offhand what "Line 10" covers. With both changes, it filed all 7 sample receipts correctly.
2. think: false behaves differently across Ollama versions. On Ollama 0.34, turning off thinking made Qwen3-VL put its JSON in the thinking field and leave content empty. After Ollama updated to 0.40 mid-build, the model reasoned anyway and used up my 400-token output limit before it got to the JSON. The fix is to read whichever field is filled and raise the limit to 3000 tokens. That's enough room to think, and it still stops a model that runs away.
Neither fix needed a vendor support ticket. The model and the server are on my machine, so I could see exactly what was happening.
| Schedule C line correct | 7 / 7 | | Totals correct | 7 / 7 (one receipt needed the larger token limit) | | Speed | about 30 s per receipt with reasoning on | | Cost per receipt | $0.00 |
One honest note: with reasoning turned off, which is much faster, the model once read the phone bill as $0.00. That's why the review page exists. The product isn't "AI does your taxes." It's a 30-second review where the machine has done the tedious part and highlighted what needs a human.
Because a year of receipts is a map of your life. They show where you drive, what you eat, which pharmacy you use, and when you're short on money. The people who most need help with this paperwork, gig workers living from one payout to the next, are exactly the people expense apps ask to give all of that up. A model on their own laptop removes the trade-off. After the one-time model download, Shoebox needs no internet connection at all.
Because the unit economics only work at $0 per receipt. A cloud vision API costs real money per image. Multiply that by hundreds of receipts per user and you're pushed into a subscription, for a tool people use mainly once a year in tax season. Open weights let me sell it as a $19 tool you buy once and own, the way software used to be sold.
Because I could open it up and fix it. Both bugs above were in how the model and server behave, and I fixed both in the request itself in minutes. With a closed API I'd have had a prompt, a support forum and hope. Open weights also mean swapping models is one flag (--model), and fine-tuning on a few hundred labeled receipts for a niche like long-haul truckers or wedding photographers is a weekend project, not an enterprise contract.
Because nobody can raise the price on me. No pricing change or deprecation notice can break Shoebox for someone who has already bought it.