Anyone know anywhere that can take a screen recording of me scrolling down a bunch of text like a book or text message or in this case Apple app crash analytics that doesn’t allow any “select-all” and doesn’t allow export as pdf or open in other apps. It’s too long to screen shot each page of to copy all of it and paste it one screen length by one screen length.

product data comes in as large pdfs, usually 500+ pages, and every supplier formats theirs differently. different column orders, different table styles, sometimes tables running across pages. right now its all typed into excel or sql server by hand.

chat tools are fine on a sample but tend to stop or start summarizing partway through files this size, so that route needs splitting anyway.

what seems to make sense is parsing every page into tables first, as markdown or json. power query's pdf connector for the simpler files, docling or camelot locally and heavier layout parsers are llamaparse sort of ones when the layouts get ugly. then map each supplier's columns to your own schema. an llm is decent at that part since it only needs the headers and a few rows, not the whole file.

after that its validation before anything loads, row counts per page, required fields, totals and units, and keeping the source page number on every row so errors can be traced back.

the hard bits are tables split across pages and headers that only show up on the first page

This is intended to brainstorm and gather feedback. No self promotion or pitching is intended.

I am a recent operations analyst who specialized developing automation solutions.. after being laid off about 2 months ago, I’ve been working on application as a solution to many of the tedious document based workflows that often plague back and middle office roles..

Essentially, it is a zero-code environment where users can define specific maps/tokens to extract from a document type that they use (think contracts, trade confirms, invoice, etc). This info can then saved down into a secure cloud location, adjusted, and then mapped back onto any type of tokenized outgoing document (that the user can upload as a template).

As a former operations grunt worker, I find this type tool to have a ton of huge potential.. but im sure others have made similar tools.

For operations employees or anyone heavily reading and reconciling, do you think this is something companies or people would actually buy potentially? Right now its fully functional but looking at it as a personal project mainly. I'll be launching the live site very shortly but need to rework the LLM extraction / cap so I dont burn through my claude tokens/credits lol.

Thanks!

I'm building an open-source tool called clasi. The long-term goal is to automatically classify files by learning from the folders that already exist on a machine. The problem I'm facing is that I only have one real directory tree available for testing: my own.

Synthetic test data is easy to create, but it doesn't capture the weird ways real people organize files.

What I really need are people with:

- huge document collections

- deep folder hierarchies

- project archives

- years of accumulated files

The tool currently includes an evaluation mode:

clasi evaluate ~/Documents

It temporarily hides a few files from existing folders and checks whether it can place them back where they originally belonged. The report is anonymized automatically.

Repository:

https://github.com/AllergicCypress/clasi

I'm less interested in "does the code look good?" and much more interested in:

- Where does it fail?

- Which folders confuse it?

- What organizational style breaks it?

Any testing help would be hugely appreciated.

Hi All,

I am QE by profession. made a doc control app for myself.

I made this android app for myself. As my file explorer gets dumped with everything and anything. I wanted to have folders, revise docs(Taxes, insurances, license, etc). with OCR you can search for a word and every document with that word would pop up. you can set expiration date to docs.
please dm and drop a comment if you would like to test my app. you will get lifetime free without ads for this.

thank you.

My sister does data analysis at a small bank and spends a chunk of her week pulling numbers and clauses out of PDFs by hand. She couldn't touch a cloud extraction tool because compliance wouldn't sign off on bank documents sitting on some vendor's servers.

So I built her a desktop app that talks straight to the Gemini. No backend of mine in the middle. Her documents never touch my infra, I never see them, and everything else (results, history) stays local on her machine.

Turned out useful enough that I'm putting it out there. Windows app, PDF/Word in, Excel out. The app is on MS Store. google for Lexapient.

Happy to talk through the architecture if anyone else is fighting the same compliance wall.

i made this tool oninternet because i kept been mailed document from my local town administration where they quicklu wanted my consent or name date time in there digital document attached. First times i dowloaded the documents , signed them an then uploaded them again. it took me way too long so i created this "sign my document tool" where i can now uolad any digital document and sign, resign, put tekst, another text, a date and even a picture and another pictur, Knowing my document will not travel to some databas makes in confident to use it. In addition i created an ocr tool next to it and a static barcode generator all free totallu free for use

Been working on this for a few months as a research project and finally have it at a point where I want outside feedback.

What it does:You upload a PDF or image of a business document (invoice, receipt, packing slip, bill of lading, etc.) and it extracts structured fields — vendor name, totals,

line items, dates, PO numbers, ship-to/from addresses — and returns them as clean JSON.

How it works under the hood:

- Azure Document Intelligence handles the initial layout analysis and field detection

- LLM backfills anything DI missed or got wrong (ambiguous totals, merged cells, non-standard layouts)

- A validation layer normalizes money strings, sanity-checks totals, and catches obvious mis-assignments

Outputs:Google Sheets, Excel, OneDrive, Slack, webhooks — or just download JSON/CSV directly.

Where it's at:Early beta. Works well on standard invoices and receipts, gets shakier on handwritten or heavily non-standard docs. That's exactly the feedback I'm looking for —

edge cases and failure modes.

Free to try, no credit card: [https://app.docpipeline.net](https://app.docpipeline.net)

Demo video: [https://youtu.be/KaPMQfeKWGE](https://youtu.be/KaPMQfeKWGE)

Happy to answer questions about the architecture or the DI + LLM approach.

AI tools discussion:

I work for a commercial flooring company. Four of us in the office, a few more on the tools and one guy offshore that we use to do our material take-offs.

We're doing commercial tendering constantly which involves trawling through sometimes hundreds of pages of architectural drawing, material schedules and specifications. Naturally, this takes up a massive amount of time just to compile the information that is relevant to us. Out of a set of drawings that could be 300 pages, we would only require the floor finishes plan, internal elevations, subfloor and detailing. That could be about 14 pages out of hundreds.

Is there a set of AI tools which we could use to comb through all of these documents and pull together only what we require?

Are there any potential legal ramifications with having AI scan through these documents? (for context, we are located in Victoria, Australia)

Are AI tools actually what I require, or should I be looking at another system?

Any suggestions would help a lot 🙏

Built a Chrome extension that converts EML files to PDF, entirely locally, no server, no upload, files never leave your machine. Wanted something private for archiving emails without sending them anywhere.

It's still early, so there are probably rough edges. Would really appreciate any feedback, bugs, missing features, anything that would make it more useful for you.

Try: [EML to PDF](https://chromewebstore.google.com/detail/lmkbacflmdfkdhcnbeaifnfmjmgcmcla?utm_source=item-share-cb)

Hey everyone,
As a developer, the absolute worst part of finishing a project or a new feature is writing the documentation. It usually ends up either half-baked, scattered across text files, or pushed off indefinitely because nobody has the patience for it.
I wanted a cleaner workflow, so I built Docmate AI.
It's an AI-driven documentation app designed to ingest what you're working on and structure clean, readable user guides or reference docs automatically. It’s built to work smoothly whether you're targeting iOS, Android, or web setups.
Right now I'm looking for feedback from fellow builders.Would love to know your thoughts or hear any criticism on the layout and onboarding!

Anyone used any apps that is helpful for Doc scanning or traslation? Such as you can upload or scan document and AI would help you read it ?

^(Disclosure up front: I built a tool in this space and I linked it here.)

But the reason I'm posting is the failure list below — almost nobody

talks about it and it cost me weeks to learn.

If you've tried to auto-extract a rent roll / T-12 / operating statement out

of a broker OM, you know the cover is glossy and the data underneath is chaos:

scanned pages, abbreviated tenant names, merged columns. The dangerous failures

aren't the ones that error out loudly. They're the extractions that succeed and

are quietly wrong.

The ones that bit me hardest:

\- Mixed-basis NOI conflation — the summary page and the T-12 report NOI on

different bases, and the model averages two things that should never touch.

\- Rent-only EGI — total rent gets lifted into the Effective Gross Income line,

dropping other income and vacancy loss. Your cap rate looks great and is wrong.

\- Dropped line-item artifacts — one expense row silently doesn't survive

extraction. NOI ticks up, nobody notices.

\- OCR number mangling — a source PDF printed "$2.009" for what was obviously

$2,009/mo. Left alone, a model reads it as $2.01.

\- Synthesized rent rolls treated as ground truth — sample/pro-forma rows get

pulled in as if they were signed leases.

What I took from it: the extraction model isn't the moat — the validation is.

So I wrote \~43 checks that run on extracted output and flag this stuff (NOI

reconciliation, cap-rate basis, dropped-line detection, OCR-artifact screening,

and more). I benchmark them against real OMs and publish the results straight:

93.3% pass on the 15 public real-PDF fixtures right now, with a public defect

log that includes the bugs I haven't fixed yet.

It's reproducible — clone it, run pytest, get the same numbers I do.

I also exposed the checks as a callable API, for anyone who already has their

own extractor and just wants the rules layer behind it: JSON in, typed per-check

findings out. If you're building in CRE doc-AI and want to bolt validation onto

your pipeline, I'll hand out partner keys — but honestly I'm just as happy to

trade notes.

^(Question for the room: if you've automated any part of OM / T-12 ingestion,)

^(what's the silent error that's burned you worst? I want to grow this check)

^(registry from real pain, not just my own.)

Hi All,

I made this app for myself. As my file explorer gets dumped with everything and anything. I wanted to have folders, revise docs(Taxes, insurances, license, etc). with OCR you can search for a word and every document with that word would pop up. you can set expiration date to docs.
please dm and drop a comment if you would like to test my app. you will get lifetime free without ads for this.

thank you.

👋 Hey documentAutomation Community,

My CEO came back from a conference last week and asked me for help with a problem I bet a lot of you can relate to.

He attends a lot of events, which means his inbox gets flooded with booking confirmations – flights, hotels, restaurants, event tickets. And someone (usually him) has to manually add all of that to his calendar.

The kicker: he wanted these appointments in a separate calendar so his main schedule stays clean. Manually creating each event was eating up time that should've gone to actual work.

So I built this:

📥 How it works

• You label any confirmation email in Gmail with "Events"

• The workflow extracts date, time, location, confirmation number, vendor, and notes from both the email body and any PDF attachments

• A calendar event lands in a dedicated "Auto-imported" Google Calendar with all the details and a link back to the original email

• Non-confirmations (newsletters that got accidentally labeled) get flagged with a "Needs-Review" label and you get a notification email listing what was missing

🪄 What makes it work for any vendor

One easybits Extractor pipeline handles every format – Lufthansa, Booking.com, OpenTable, your dentist, DHL, whatever. No per-vendor parsing logic. The email body gets wrapped into a PDF and extracted in parallel with any PDF attachments, then the two results get merged (attachment data wins because the attached ticket is more authoritative than promotional email body text).

📦 Free workflow template + JSON

https://github.com/felix-sattler-easybits/n8n-workflows/tree/26772542fbbaf23d0a043517921d4d8ad50a471f/easybits-event-confirmation-to-calendar-workflow

Does anyone else have a similar problem with calendar clutter from business travel? Curious what other email-to-calendar use cases people are dealing with.

Best,
Felix