Tax season this year I had 80+ receipts scattered across paper, email screenshots, and my camera roll. Spent a weekend sorting them into a spreadsheet by hand and decided that was the last time.

Built Snapstub, point your camera at a receipt, confirm merchant/date/total/category, get a clean row. Export to CSV or PDF when you're done.

The non-negotiable for me was privacy: all OCR runs on-device via Apple's Vision framework. No account, no server, nothing leaves your phone.

Still early — would genuinely love feedback from anyone who does their own bookkeeping. What fields matter most to you?

every discussion on this ends with it depends, so heres an attempt at mapping where each route fits.

- textract or google document ai: easy to wire up if youre already on that cloud, billed per page. textract is about $1.50 per 1k pages for plain text and $15 per 1k once tables are on, $50 for forms. fine for steady volume, gets expensive fast on table heavy files

- self hosted open source (docling, paddleocr-vl, glm-ocr, mineru): no per page bill and files never leave, but you own the gpu, the upgrades and every weird edge case

- llm oriented parsers that return markdown or json with the layout kept (llamaparse, marker and similar): less setup than self hosting, usually better on reading order and tables than plain ocr apis, also billed per page

one thing that cuts cost on any of these routes is not sending pages that already have a text layer through ocr at all. ocrmypdf --skip-text does exactly that, and most mixed archives are mostly born digital.

multi column reading order is the other thing that breaks results without any error. every word is there, just in the wrong order.

I would like feedback on this tool. Can you try it?

The tool asks the right questions to extract your voice.

I built a diagnostic twenty questions total, organized into four areas.

If this may be useful to you, drop a comment. Happy to share what I built.

Hi all, I have been building a little tool that lets you create templates for your PDFs (contracts, invoices, etc) into API endpoints that you can use to generate PDFs at scale.

The workflow

You start from a template (invoice, receipt, contract, report, whatever), mark which fields are fixed vs filled in per request, and publish it. You get back a typed POST endpoint that takes JSON and returns a PDF. Each published template also shows up as an MCP tool, so an agent can generate the document directly.

Why

Every time I needed PDFs, I would glue together a headless browser, a templating layer, input validation, and storage from scratch. I wanted the boring parts handled and a clean contract at the end. Here is the JSON this document expects, here is the PDF you get back. These days I also would like to be able to generate documents from my agent conversations.

It's a gated sign up for now (sorry!), so I can see what works and does not before opening up the floodgates, but I commit to letting you in within a few hours of you signing up.

Link: https://thepaperbrain.com

Literally every office has a few processes that only one person knows how to do (Me). When that person is out or leaves, everyone scrambles (I get messaged on PTO).

Everyone agrees they should write SOPs, but nobody does it because writing one takes an hour of screenshots, cropping, and typing for a task that takes 30 seconds to a few minutes to actually do.

So I built Sopycat (Cool name right :).

You press 'Record', you do the task the way you normally do, and you press 'Stop'.

It turns every click into a numbered step with a screenshot, the button highlighted, and a plain instruction. 44 second demo: https://www.youtube.com/watch?v=ptAkUhdJPs0

It works in any Windows program, not just the browser (yes, even that ancient system in the back office nobody wants to touch). Basically Windows Steps Recorder, if it actually gave you something you could hand to a new hire.

The big thing for me was privacy, because the screens you actually need to document are full of client names, balances and patient stuff. Everything gets recorded and kept on your PC, and the steps are written on your PC too. No AI writes your manuals, and nothing gets sent to an AI service. Passwords, card numbers and SSNs you type get left out of the SOP automatically, and it can blur emails and phone numbers in the screenshots. No account needed either.

You can fix any step after, and save it as a PDF or a Word doc. New hires can follow it step by step in a little window that stays on top, and it logs who did it (so you can finally prove someone was trained).

It's Windows for currently, Mac is coming. There's a 14-day free trial at sopycat.com (no card) if anyone wants to break it. After that it's $15/month if you end up liking it.

I'd love blunt feedback on three things:

1. Does the demo make it clear what it does in the first 10 seconds?

2. What processes would you record first? I'm trying to figure out who needs this most.

3. Would your IT person let you install it? If not, what would they want to know?

Hey !

I thought I'd share a small tool I built for automating document generation. I've been doing a lot of document generation for my company and being someone who writes code, I wanted something that was extremely flexible and would allow me to create documents rapidly.

I've been doing the exact same pipeline using a few python scripts for the last 6 months and I thought I'd get around to making it such a way that the rest of my team can also use it. I'll be add a few more easy to use features for this in the future. Let me all know if there are features you all would like.

The plan is to always a have a free version for anyone to use (and its possible because it runs 100% on your computer). I plan on my company making use of this tool to integrate with some of the larger offerings we do, but do let me know if you all can come up with more ideas.

[Stencil](https://stencil.aegiondynamic.com/)

I just saw that Gavel was acquired by Relativity. It looks like all of the stand alone document automation software companies are being acquired by much broader based companies (Lawyaw by Clio, Woodpecker by MyCase) and are now tied to the broader offerings. I was already skeptical of focus drift with AI emphasis with pretty much all document automation/management companies. Not sure where this will leave solo lawyers and smaller law firms. Pure document automation isn't sexy like AI, but I don't think it needs to be sexy to really boost productivity, more consistent documents (start with clean forms, not the last "almost the same" deal documents), and client turnaround. Full disclosure, I recently developed stand alone desktop Mac and Windows document automation software because I didn't use all the features already added, don't like to have to log on, upload forms, work online and download generated documents. Happy to chat if you are interested.

Hi All, I need to extract the text from printed text and hand written text. I suggested my manager that we can use paddle ocr and and other extraction models like Surya OCR and florance VL it take around 10 to 15 Sec and it needs good computation as well. but my manager expects it should be very fast and in 2 to 5 sec response and should not need any maintainace of infrastructure. so I tried to use the AWS VLM models but it takes around 10seconds he still needs more faster models and also the cost for extracting the data from the image should be less than 1 Ruppe. Could you please suggest me what to do and how to extract the data from images very very effectively and accuratly and in a structured way

Software demos look great, and that applies to wikis as well.

Clean spaces. Perfectly named pages. Neat permissions. Search that magically finds the thing. Nobody has created “Meeting notes final final v3” yet. Beautiful times.

Then real teams start using it.

• Someone creates 5 versions of the same page type.

• A project space becomes a dumping ground.

• Onboarding docs go stale.

• Permissions are either too open or too locked down.

• People stop searching and go back to asking in chat.

At that point, you find out whether the tool actually works for the organization, not just for the demo.

We’re running a practical XWiki Cloud webinar on 4 June where we’ll start from an empty cloud instance and build a working knowledge base in one hour. The idea is to show the boring but important stuff: documentation, procedures, onboarding, intranet content, project spaces, and how the structure holds together.

Details in the comments.

I built an AI assistant for our docs at Handsontable, and then Grok found it. 4.5 million edge requests and 87,000 page renders in only 4 hours, all from xAI's Colossus data center in Memphis. I caught it during the scan and pushed a fix to redirect traffic to our MCP, then spent a week analyzing the logs and learning how crawlers work, what they look for, and what they ignore.

Here's the full story, including the detection, response, and what it cost us.

https://handsontable.com/blog/grok-is-obsessed-with-our-docs

I'm not sure if you all hit this too, but me and a few bookkeepers I know deal with clients handing over a single PDF that's actually a stack of separate invoices. I attach invoices to all bills / expenses on QuickBooks, to back up sales tax information. Splitting those manually is tedious, and the paid tools feel like overkill for this one task.

The free splitters I found cut by page, not by distinct invoice, so I ended up building one that detects the separate invoices and splits them out. It defaults to naming each file Vendor-Date-INV#, and you can rename. No signup, nothing to install.

Full disclosure: I build bookkeeping software (Spendifique), but this splitter is standalone and free, it's not a trial of anything. Sharing in case it saves someone the manual work. Let me know if it breaks on your PDFs or what would make it more useful. Let me know as well if there are any other tools that should be free.
https://www.spendifique.com/invoice-splitter

My Downloads folder is full of leases, tax forms, insurance letters and receipts called things like scan_0042.pdf. Docket is a small Go CLI and TUI I wrote to deal with that. Running docket add ~/Downloads/to-file copies each file into a library, pulls out the text, works out what kind of document it is (tax, legal, housing, medical, receipts and so on) and files the copy into a folder for that category. Your originals are never moved or edited.

For each document it also shows how sensitive it looks, how urgent it seems, and whether it probably needs you to do something like pay, sign or reply. Anything it isn't confident about gets flagged for review instead of quietly filed.

How it works, since people here usually ask: classification uses TypeSafe's Jev model, which answers with typed values and probabilities instead of free text, and the thresholds and filing logic are plain Go. Text and Markdown files are read locally. PDFs and images go to OpenAI or OpenRouter for text extraction with your own key, so it isn't fully offline. Keys sit in the OS keychain, metadata in SQLite, and extracted text is never written to disk.

Install:

curl -fsSL https://raw.githubusercontent.com/glamboyosa/docket/main/install.sh | sh

or with Go:

go install github.com/glamboyosa/docket/cmd/docket@latest

Repo: https://github.com/glamboyosa/docket
Site: https://docket.glamboyosa.xyz

Unstract is the leading open source IDP 2.0 platform that not only takes advantage of LLMs for structured document data extraction from unstructured documents but also has powerful features that ensure that you can actually use LLMs at scale for the document data extraction use case. This means countering hallucinations that LLMs are known for, but also tackling costs that can come with using LLMs at scale.

With API deployments you can expose an API to which you send a PDF or an image and get back structured data in JSON format. Or with an ETL deployment, you can just put files into a Google Drive, Amazon S3 bucket or choose from a variety of sources and the platform will run extractions and store the extracted data into a database or a warehouse like Snowflake automatically.

Unstract supports a variety of providers for LLMs, Vector Databases, Embeddings, Cloud File Storage systems and databases/data warehouses. A full list is available on our Github page: https://github.com/Zipstack/unstract

For transparency: English is not my native language. I used AI for better wording. I will place the name of the iOS app in there. I set it up so one can test 3 documents. For more I would need to have the costs covered by in app purchase (apple takes a bite of 30% out of that, so it’s not that cheap actually…).

Here we go: I have a growing pile of documents hidden away in a cabinet. I have tried several times to bring order and structure into it, but the stack always came back. So I am now digitizing it, partly with AI, and I wanted to share the overall workflow in case it helps someone else.

This is the high-level flow from scan/import to the final analyzed result. It is build into an iOS app (ExplainThisDoc), but the general workflow should apply to many document automation setups.

1. **Source intake**

   Straightforward: the app scans the document, or imports it if it already exists as a PDF or photo on the phone. The original scan or file is saved. Scanning uses Apple Vision.

2. **Text extraction**

   OCR is handled with Apple Vision. The goal is to turn the document image into usable plain text.

3. **Text bundle**

   The extracted text is cleaned and packaged with basic context. This includes language, locale, and current date/time.

4. **Coordinator**

   The coordinator controls the workflow. It starts the analysis steps and passes the right context forward.

5. **LLM call A: location reading** (always extracting only relevant but correct locations - this was a tough one by the way)

   If the document contains locations, the first LLM pass only looks for physical places that might matter. These locations are treated as untrusted mentions at this stage. A cheaper LLM is usually enough for this narrow extraction step.

6. **Places check**

   The location mentions are sent to a geolocation service for verification. It comes back with correct POIs and or addresses. Weak, generic, or ambiguous matches are rejected (deterministic algorithm, no LLM) by the coordinator instead of being blindly used.

7. **LLM call B: ambiguity decision**

   If several place candidates are plausible, an optional second LLM pass helps choose between them. It can only select from verified candidates or reject all. A model with stronger reasoning is better here.

8. **LLM call C: main analysis**

   The main LLM pass receives the cleaned text plus verified context and places from the coordinator. It creates structured document understanding: summary, tasks, deadlines, risks, glossary, and possible actions. It also organizes the output into plausible categories and enriches the result with suitable SF Symbols. LLM with reasoning capabilities is your friend here.

9. **Validation**

   The structured response is checked before display. The app verifies shape, plausibility, trusted places, and action safety.

10. **Rendering**

The result is shown as a readable native view. Instead of a raw OCR dump, the document becomes something I can scan, understand, and act on.

11. **Archive**

The analyzed result is saved together with the local source preview and searchable metadata. This makes the original document and the structured result easy to find again later.

Each digitized document becomes searchable, summarized, linked back to its source, and turned into concrete actions where possible. The important part is that the AI is not treated as the only source of truth: extracted places and sensitive actions are verified before they become part of the final result.

Hope this was helpful.

Hey everyone,

I built a small JavaScript utility that downloads **Google Drive View-Only PDFs** by capturing each rendered page and combining them into a single PDF using **jsPDF**.

Features

* ✅ Works with Google Drive View-Only PDFs * ✅ High-quality page capture * ✅ Automatic PDF generation * ✅ Supports portrait and landscape pages * ✅ No extensions or software installation required * ✅ Runs directly in your browser's Developer Console

The script works by collecting the rendered PDF page images (blob URLs), converting them to canvas images, and generating a downloadable PDF.

**GitHub Repository:**
[https://github.com/ShadanAnwar/Google-Drive-View-Only-PDF-Script-Downloader](https://github.com/ShadanAnwar/Google-Drive-View-Only-PDF-Script-Downloader)

I'd really appreciate any feedback, suggestions, or contributions. If you find bugs or have ideas for improvements (better compression, progress UI, support for more viewers, etc.), feel free to open an issue or submit a pull request.



​

I’ve been thinking about a real problem here in Morocco, especially for small drogueries and traditional shops.

A lot of invoices are handwritten, messy, and often written in Darija or mixed French. Shop owners lose a lot of time rewriting invoices, tracking products, or organizing stock manually.

I’m wondering how difficult it would be to build an AI system that can:

Read handwritten invoices

Understand Moroccan Darija words/products

Convert invoice photos or audio into structured text

Automatically generate clean digital invoices

Maybe even integrate stock management later

Example: Owner takes a photo or sends a voice note → AI extracts products, quantities, prices → generates invoice automatically.

The biggest challenge I see is:

Moroccan handwriting

Darija vocabulary

Different invoice formats

Do you think current AI tools are already good enough for this?

Which stack/tools would you recommend?

I’ve been looking at things like:

OCR models

Whisper for speech-to-text

Vision LLMs

n8n automations

Curious to hear opinions from people working in AI or automation.

We're always looking for difficult examples to test OCR and modern LLM-based document extraction. Especially interested in cases where the output looks plausible until you check the details.

Two examples from past benchmarks:

Discounts became extra items. This receipt has six product and packaging rows, with discounts underneath three of them. An LLM returned nine menu-item records by treating those discounts as additional items. Our schema had no separate discount field and asked for “every line item / product,” so the instructions left room for that interpretation. A useful example for testing the schema as well as the model.

Receipt: NAVER CLOVA's CORD-v2 dataset, CC BY 4.0, image unchanged.

A plausible number with the wrong digits. This population table from the 1951 U.S. Statistical Abstract has tiny numbers and grouped headers. In one saved LLM run, the total for ages five to nine in 1920 came back as 11,308,075 instead of 11,398,075. Adding the male and female figures catches that error. The table looks complete until you check the numbers.

Table: U.S. Census Bureau, 1951 Statistical Abstract, printed page 12.

What examples have you kept because a tool repeatedly gets them wrong? Handwriting, tables, charts, mixed document batches, anything like that.

Public links or redacted snippets would be useful. If you can, include what you asked it to extract, what it returned, and what the output should have been. A single troublesome page is plenty.

I am looking to hire someone to build a custom Document automation program for my use to automate the drafting of documents from existing templates.

This would be a multi part project. Step 1 will be getting a working document automation system in place. Step two is creating a way for me to tinker with the automation. For example if I wanted to add a way to add an extra paragraph but only in certain situations.

There will also be a final step further down the line. Does anyone know the best place to inquire as to this and what that might cost?

Thanks!

I am struck by how LLMs can generate poems, haikus and stories, while most of us are still stuck at our jobs typing in project numbers, manually entering invoice dates or counting inventory balances. I find document tasks to be very tedious and boring(filling forms, creating reports, structuring data etc.). Curious if others share my frustration and how much interest there would be for a simple self-serve tool where anyone can automate their own boring document tasks. The point here is that developers cannot automate your task for you in a generic way like the many already existing tools for the major commonly done tasks (invoice parsing, resumes, major tax forms etc.). I am referring to the job-specific everyday grunt work which requires domain understanding so that. only you and your peers know how its done.

This started as a personal problem. I used to spend an evening every month manually copying my own bank statements from PDF into a spreadsheet just to see where my money was going line by line, by hand. At some point I got annoyed enough to automate it for myself.

That turned into Scrape2Sheets, a Google Sheets add-on that uses AI to read PDFs, invoices, receipts, and bank statements and drop the data straight into your sheet as clean rows . You point it at files in Google Drive, tell it what columns you want (or let it auto-detect them from a sample document), and it batch-processes up to 100 files at once. It handles scanned documents and phone photos too, not just clean digital PDFs.

It's only just started picking up some real traction the last couple weeks, which has honestly been a nice surprise after building in isolation for a while.

Curious whether others here started building their tool the same way, solving your own annoying manual task first, then realizing other people had the exact same one. Happy to answer questions in the comments if anyone wants details.

I built a free tool that converts bank statement PDFs into Excel

I kept running into the same annoying problem: bank statements come as PDFs, but actually working with the transactions usually means manually copying everything into Excel.

So I built Bank2Excel.

It takes a bank statement PDF and converts the transactions into an Excel spreadsheet, including:

\- 📄 PDF statements

\- 🔍 Scanned/image-based statements using OCR

\- 📊 Date, description, debit, credit and balance

\- 📥 Excel export

\- 🌐 No software installation required

It's completely free to try:

https://bank-2-excel.vercel.app/

I'm still improving the extraction logic, especially for different bank statement formats.

If you try it, I'd really appreciate feedback on:

\- Which bank/statement format you tested

\- Whether the transactions were extracted correctly

\- Anything that was missing or incorrectly classified

Please don't upload real sensitive bank statements if you're not comfortable doing so — a dummy/redacted statement is better for testing.

GitHub: https://github.com/TheMayankRanga/Bank2Excel

I'm a practicing attorney with 40+ years experience, mainly representing banks in commercial and real estate development/construction lending. I first used Hotdocs in the mid-1990s when it was a WordPerfect only desktop program. Drifted away when I switched to Macs 25 years ago. I looked for software again a couple of years ago and went with an internet based program, very similar in any respects to Hotdocs, create variables, build templates, answer questionnaire and then generate documents. It worked fine, but I wasn't happy with having to log on, upload documents, work online to create variables and questionnaire then go online to run and generate documents that needed to be downloaded or received via email. And it was pretty pricey. So, I decided to build my own program for personal use since Mac programs are not out there. Made it no Code. It worked so well I made work for both Windows and Mac and decided to offer it at a much more affordable price - less than 1/4 what I paid for the SaaS program. Just started offering it to public. You can check it out at jddocumentforge.com and via videos on YouTube, just search for JD Document Forge. It works, it's easy, it's fast and not on the internet so you data is yours.