This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
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, up 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.
$ 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.