u/vh-dev

Hi everyone,

I'm looking for closed testers for my app, FinMan (a financial tracking app). I need users to join the Google Group, opt-in for testing, and keep the app installed for at least 14 days with occasional usage so Google can approve it for production release.

Here are the steps to join:

Join the Google Group first:

https://groups.google.com/g/finman-testers

Become a tester & install the app from Google Play:

Web link: https://play.google.com/apps/testing/com.vhworx.finman

Android link: https://play.google.com/store/apps/details?id=com.vhworx.finman

Please leave the app installed and open it every now and then over the next 2 weeks.

If you leave a screenshot in the comments, I'll be more than happy to join your group and test your app in return!

Thanks a lot for your help!

My app (finman vhworx google it if interest) reads utility meters, fuel pumps, receipts and odometers from photos. For months it picked whichever vision model was cheapest and available. That protects you when a model goes down. It does nothing when a model is up and quietly worse, and with numbers that's the dangerous case: a wrong reading looks exactly like a right one. The JSON is valid, the request returns 200, nothing in the logs looks off.

What fixed it: 32 photos with known-correct values, 8 of each type, the bad-quality ones marked as a separate group. A new model has to get 70% of the fields right on that set before it's allowed near a real photo. Scoring runs through the same prompt the app actually uses, so the number means something.

The results were not what I expected. Accuracy overall / on the hard group, price per 1M input tokens:

gemini-3.1-flash-lite — $0.25 — 93% / 80%

gemini-flash-latest — $1.50 — 93% / 90%

gemini-2.5-flash-lite — $0.10 — 89% / 90%

nemotron-3-nano-omni-30b:free — $0 — 82% / 100%

gemma-4-26b:free — $0 — 78% / 90%

nemotron-nano-12b:free — $0 — 52%, rejected

Paying 6x more bought nothing. And the free nemotron was the only model that read every hard photo correctly, even though it scores lower overall.

One more thing worth knowing: every :batch endpoint I tried scored 0. If you assume batch is the same model at a lower price, test it first.

What I still don't have: enough hard photos. 32 is too few, and they're mine, so they're biased toward the meters I own. If you build anything that pulls numbers out of images, this is the cheap version of the guard — a fixed set with known answers, checked before you swap models, not after.

Disclosure: my own app (reads meters, pumps, receipts, odometers from phone photos).

32 phone photos with known-correct values, scored per field. Hard subset scored separately.

gemini-2.5-flash-lite - $0.10/Mtok - 88.6% - hard 90% gemini-3.1-flash-lite - $0.25/Mtok - 93.2% - hard 80% gemini-3-flash-preview - $0.50/Mtok - 93.2% - hard 80% gemini-flash-latest - $1.50/Mtok - 93.2% - hard 90% gemma-4-26b:free - $0 - 78.4% - hard 90% nemotron-nano-12b-v2-vl:free - $0 - 52.3% - failed

Above $0.25 price buys nothing.

My photos aren't bad enough. Link in the comments if you want to throw your worst at it.

[ Removed by Reddit on account of violating the content policy. ]

I've been asking landlords how they handle submetered utilities, and one answer stuck with me: 5 properties, 12 units, 2–2.5 hours a month on readings and rebilling — and most of that time is driving between buildings. The arithmetic itself takes minutes.

Disclosure: I build a finance app, and this is the workflow I ended up designing around that comment.

The part that removes the drive: the tenant photographs their own meter and sends it to a Telegram or WhatsApp bot — no tenant account, no app install. The reading is extracted from the photo, booked against that unit's address and its per-service tariff, and an implausible jump (or a reading lower than the previous one) is flagged for confirmation instead of being saved silently. The photo stays attached to the reading, which is what you actually want at move-out when a number gets disputed.

What I'm genuinely unsure about, and would rather hear from people who do this monthly:

• Do tenants cooperate with sending a photo, or is chasing them worse than driving yourself?
• One landlord priced this for me as a 1.5–3% admin fee per bill rather than a subscription. Is that how you'd expect to cover a tool like this?
• What breaks after the reading — is producing and sending the actual rebill the real time sink?

After Mint shut down, the two gaps people mention most are shared/family tracking and imports that don't depend on aggregators. I've been building FinMan for my own family, and it's free:

- Bank statement import from any bank — you upload the statement file, AI parses and categorizes it (no bank credentials, no Plaid/aggregator dependency)

- Family groups: everyone logs from their own phone, shared categories and one cash-flow view

- Telegram/WhatsApp bots: text "groceries 25" or send a receipt photo and it's logged

- Utility meter readings from photos, car cost per mile/km, multi-currency with live rates

It's free (premium exists only for heavy AI usage). Roadmap and feedback live on GitHub: https://github.com/vityah1/finman-feedback — the app is linked from there, or search "finman vhworx".

I'm the dev — honest feedback (especially "Mint did this better") is exactly what I'm after.