Very nice project. I think this is is in the step of the right direction for agent-driven assistance.
> How it works: you import a CSV from any bank, in any currency.
I think this is where you're going to lose people. If you're having to pull in data from multiple different sources, each with their own flow for exporting, I think this might be a non-starter for creating good financial habbit.
I recnetly had success integrating my local AI agent with Monarch money via an open source monarch MCP project. It took a bit of (AI) hacking the login stuff, but now I'm able to pull transactions, query patterns, have a daily report generated from hermes, etc. Much more technical that your target audience but possibly offers some inspiration.
Appreciate the feedback. There's also a connected mode leveraging Plaid where transactions pass through servers memory and never stored. Outside the US, CSV is the only path for now otherwise agree CSV export is a chore for multi-bank users.
You could add support for something like simplefin.org, which provides an API for connecting to many different financial orgs. It is what I have been using for my local setup with Actual budget.
Actual budget is good. Connectivity in Australia sucks though so I've kludged my own approach (open banking connections to a single local aggregator then puppeteer to sign in and get an access token, then directly calling the aggregators internal APIs with it and pushing the data into a local Actual server using Actual's APIs).
A lot of banks make it easier to export PDF statements. I wonder if you could try adding the ability to extract transactions from a PDF. I've been working on something more limited (extracting just the account balance) with Qwen3 4B with decent success.
"if you're having to pull in data from multiple different sources, each with their own flow for exporting"
you say this, but i got ChatGpt to just open chrome logged into two credit cards and a bank app and download everything with a certain criteria (i did this to populate items in a travel split app). So maybe it's not that bad
Thanks. Well, accuracy depends on the code that owns the ledger, which does all the math against the app data model. I also added checks that compare a couple of readings of the question through the same calculation layer. Since there are also many other moving parts coupled with the ledger (categories, rules, transfers, refunds), it just felt better to own the whole stack.
I gave it a go and imported a transaction CSV from my bank - National Australia Bank. 150 row CSV. The Description import column was assigned to the Merchant Name CSV column (the Merchant import column was also assigned to this CSV column) and as a result 25 rows were going to be ignored. Here's the CSV header row:
Date,Amount,Account Number,,Transaction Type,Transaction Details,Balance,Category,Merchant Name,Processed On
Hand-picking the Description column to be assigned to the details column sorted it out; not sure how you're auto-selecting column mappings but maybe 'details' should be recognised as closer to 'description' than 'merchant name' is.
I love the idea of local only apps woth no network comms. I do wish macOS had some way you could enforce that out of the box though. Rather than trust the claims of the developer.
Another app I love that does the same. Queryable which lets you natural language search your photos on device on iOS and predates most of the current AI craze (2022) and also apple adding this themselves to photos:
https://queryable.app/
Indeed, macOS only blocks incoming traffic. For this app and in "local mode", there are only two kinds of requests: one to download the models from HF and an optional version check that sends nothing about the user and can be turned off. A simple test, once you download, try turning off your wifi and use the app.
You have a free budgeting app for Mac? Can you have as an app so I can download it in my iPhone? Can Pulse Local connect to my bank app and automatically records my spendings?
Also am curious about your reasoning behind usage of Qwen3.0 model as well!
Hi, only a Mac app today, working on a Windows app next. As for bank connections, yes this was requested and I added a "Connected Mode" ($29 a year Sync Pass) which syncs US banks via Plaid otherwise you can import a CSV which also works with any bank and any currency. As for Qwen3, 0.6B is small enough to run fast on most laptops, showed good evals and easily tunable. In my tests, the tuned 0.6B model scored about level with gpt-4.1-mini on a dataset of 77 real budgeting questions and outperformed a stock 8B model as well.
Apologies, will try to simplify. In a nutshell: Pulse Local is a free budgeting app where you could import bank transactions and ask about spending with the AI running on your machine so neither the data nor your questions leave (in "local mode").
Very cool. What does it use the model for? Could you replace it with something lighter, like some classifiers[0]? Or do you need the chat? Besides the chat, what other tasks is the model used for? Thank you!
Plaid is the main risk - if they were hacked, that's way worse than a log of my AI queries. What's exciting with LLMs is the possibility to do classification much better.
after a “not sure,” how often does reprhasing get a correct answer on the next try/do users give up? retries could make a simple lookup surprisingly slow?
Feedback: manually categorizing transactions is a common and tedious task. Your 'for review' transaction list shows the category, but it's not editable. To review and fix the category, you must:
- read the category
- click on the '...' button at the end of the line (not near the thing you want to correct, and not my first guess for settings the category)
- this creates a popup
- mouse into the popup and click on the dropdown for setting the category.
Just have the category label in the list clickable, to show the category dropdown.
It saves one click, two mouse movements, and having to figure out that '...' is the action you need to fix a category.
I use budgeting apps on a daily basis, and I'll just share my personal feedback: I'm absolutely not downloading ~3 gigs for a budget app. Everything I need to "ask" the app is already available to me in the form of reports, charts, and tables. I don't really have many open-ended questions about my budget because anything important to track is already handled deterministically.
Also yes, it's local but the data you're importing is already with a private organization on the cloud. In that sense, having a local copy of it does not really do anything for me in terms of privacy etc.
As I said, I use budgeting apps daily (one for personal use (self-made), another for business (very old freenium era software that has not failed me in the last 5+ years of self-hosting it)) and I do not see any need for generative AI in that workflow from my pov. YMMV
Thanks for the feedback and describing your budgeting routine and needs. Just to add that the 2.5G checker can be switched off in Settings. The reader is only a small tuned 0.6B model (397 MB).
Chat is optional, the app is a complete and free budgeting app (reports, rules, money flow, forecast etc.). As for privacy, "local" usually means your data not your questions, sure the banks hold the transactions but your questions and analysis shouldn't go to the cloud hence this project.
> Also yes, it's local but the data you're importing is already with a private organization on the cloud. In that sense, having a local copy of it does not really do anything for me in terms of privacy etc.
I'm not sure what you mean? Surely processing on your computer instead of Anthropic's computer is a significant privacy improvement?
you only need 2 sections. an above the fold area and something below the fold. simple.
thanks for making this. “its
private” is not a selling point, whats other differentiators does it have? really the AI analysis and potential agentic aspect right?
you could point out that it doesnt use data centers or harm the environment without explaining how or what that means upfront
Please don’t. This is really a matter of personal preferences. I like long informative landing pages that as I scroll, give me more and more reasons to try the software.
Compare the omp landing page a few months ago versus now: https://web.archive.org/web/20260624030333/https://omp.sh/ versus https://web.archive.org/web/20261007154534/https://omp.sh/ The former does a compelling job telling me why I should try it by listing its advantages. By the time I got to hashline I became convinced. The latter landing page has a single sentence that’s generic and vague. I would not have tried omp at all if its landing page looked like what it is today.
> How it works: you import a CSV from any bank, in any currency.
I think this is where you're going to lose people. If you're having to pull in data from multiple different sources, each with their own flow for exporting, I think this might be a non-starter for creating good financial habbit.
I recnetly had success integrating my local AI agent with Monarch money via an open source monarch MCP project. It took a bit of (AI) hacking the login stuff, but now I'm able to pull transactions, query patterns, have a daily report generated from hermes, etc. Much more technical that your target audience but possibly offers some inspiration.
Bridge is actually provided by mx.com, pretty much the same service as Plaid.
It also requires you generally to trust some third party with your data.
Here’s an API one for Australia / New Zealand / USA: https://redbark.com/developers
you say this, but i got ChatGpt to just open chrome logged into two credit cards and a bank app and download everything with a certain criteria (i did this to populate items in a travel split app). So maybe it's not that bad
Another app I love that does the same. Queryable which lets you natural language search your photos on device on iOS and predates most of the current AI craze (2022) and also apple adding this themselves to photos: https://queryable.app/
https://github.com/objective-see/lulu
Also am curious about your reasoning behind usage of Qwen3.0 model as well!
[0]https://fastino.ai/blog/fastino-nemotron-3-5-lightning-finan...
0: https://jeffyclassify.com/blog/when-llms-fail-at-classificat...
Still it could be handy to be able to do that locally, maybe model tweaks could be shared back upstream.
The bridge between deterministic accounting principles to non-deterministic patterns trying to simulate it at will is a pretty big thing to cover.
- read the category
- click on the '...' button at the end of the line (not near the thing you want to correct, and not my first guess for settings the category)
- this creates a popup
- mouse into the popup and click on the dropdown for setting the category.
Just have the category label in the list clickable, to show the category dropdown.
It saves one click, two mouse movements, and having to figure out that '...' is the action you need to fix a category.
Also yes, it's local but the data you're importing is already with a private organization on the cloud. In that sense, having a local copy of it does not really do anything for me in terms of privacy etc.
As I said, I use budgeting apps daily (one for personal use (self-made), another for business (very old freenium era software that has not failed me in the last 5+ years of self-hosting it)) and I do not see any need for generative AI in that workflow from my pov. YMMV
Chat is optional, the app is a complete and free budgeting app (reports, rules, money flow, forecast etc.). As for privacy, "local" usually means your data not your questions, sure the banks hold the transactions but your questions and analysis shouldn't go to the cloud hence this project.
Downloads will already be on the machine.
https://developer.apple.com/documentation/foundationmodels
I'm not sure what you mean? Surely processing on your computer instead of Anthropic's computer is a significant privacy improvement?
you only need 2 sections. an above the fold area and something below the fold. simple.
thanks for making this. “its private” is not a selling point, whats other differentiators does it have? really the AI analysis and potential agentic aspect right?
you could point out that it doesnt use data centers or harm the environment without explaining how or what that means upfront
Compare the omp landing page a few months ago versus now: https://web.archive.org/web/20260624030333/https://omp.sh/ versus https://web.archive.org/web/20261007154534/https://omp.sh/ The former does a compelling job telling me why I should try it by listing its advantages. By the time I got to hashline I became convinced. The latter landing page has a single sentence that’s generic and vague. I would not have tried omp at all if its landing page looked like what it is today.
the layouts with the most aligned engagement wins rollout to all users
coding isnt the hard part of a product launch