# LedgerLocal - your receipts never leave your laptop > Source: > Published: 2026-10-16 16:39:32+00:00 *This is a submission for the [Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle](https://dev.to/challenges/hacktoberfest-week2-2026-10-12)* **LedgerLocal** — a CLI that turns a freelancer's folder of invoices and receipts into a sorted, accountant-ready archive, entirely on their own laptop. Point it at a folder: it reads each document with a local vision model, extracts vendor / date / tax / currency / total, categorises it against rules you control, files it into a `year/quarter/category` tree, and exports the CSV your accountant asks for. **Who pays:** freelancers and one-person studios with 300 unsorted receipts and a lost weekend every tax season. Plenty of SaaS does this — all of it wants the documents uploaded. Receipts name your clients, expose your rates, and carry card digits and tax IDs. For consultants under NDA or anyone under data-residency rules, uploading isn't a price question, it's a non-starter. Their real choice is *local tool vs. the weekend*. The best part isn't extraction, it's distrust: every document is audited after the model answers. In the demo the model misreads `$65.09` as `$65.00` and the tool catches it from the arithmetic — nobody told it to look. **Sort a year of invoices and receipts into a clean, accountant-ready archive — on your own laptop, with an open-weight model, with no network access at all.** Point it at the folder where your receipts pile up. It reads each one with a local vision model, pulls out vendor, date, tax and total, categorises it against rules you control, files it into a sane folder tree, and exports the CSV your accountant actually wants. ``` bash $ ledgerlocal ingest ~/Downloads/receipts read hetzner-feb.pdf read coffee-with-northwind.jpg read adobe-jan.pdf read legal-invoice.pdf 2026-02-14 Hetzner Online GmbH 32.73 EUR hosting 2026-04-03 Blue Fox Coffee 11.77 EUR meals 2026-01-07 Adobe Inc. 65.00 USD software review 2026-03-02 Bright Path Legal LLP 990.00 USD professional_fees 4 ingested, 0 already known, 0 failed, 1 need review $ ledgerlocal review 1 document(s) the pipeline is not confident about: 2026-01-07 Adobe Inc. 65.00 USD · subtotal + tax differs from total ``` …`"1.234,56 €"` is `Decimal("1234.56")`. Small models are good at finding, unreliable at formatting.`urllib` against localhost. When the pitch is "audit this yourself," a 40-package tree undermines it. SQLite keyed on content hash (re-running a folder is a no-op). ~3,600 lines, 89 tests, no GPU or network needed. Apache-2.0: **github.com/Pager-dot/ledgerlocal** **The headline feature is one a closed API cannot ship.** Rather than ask users to trust a privacy policy, I removed the capability. Every command installs a process-wide socket allowlist first: ``` socket.create_connection(("api.openai.com", 443)) # OfflineViolation socket.create_connection(("127.0.0.1", 11434)) # fine — that's your model ``` A hosted version **can't implement this**, because the egress it blocks is the call the product depends on. Not a cheaper substitute — a product that otherwise can't exist. **Unmetered inference changed the engineering, not just the bill.** The repair pass runs on every uncertain document; on a metered API you'd ration that. `recategorise` replays three years of documents against new rules in under a second with **zero** model calls — there's a test asserting the provider is never touched. With per-document pricing you simply don't change your mind, and the rule system dies unused. **Swappability is leverage you keep.** A 3B on battery, a 32B at year-end, a LoRA fine-tuned on one client's bizarre invoice layout. With a closed API that last one is a feature request to a vendor who'll never prioritise it. **Where closed wins, honestly:** a frontier model reads a crumpled thermal receipt better than a 7B. I closed most of that gap with structure instead of scale — constrained decoding, deterministic parsing, arithmetic audits — and surfaced the rest in a `review` queue. The discipline a small model *forced* made a better product, because a frontier model would have tempted me to skip the checks.