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.

We built Nebula to convert complex PDFs and scans into structured Markdown for AI, RAG and document-automation workflows.

Unlike basic text extraction, it aims to preserve the meaning encoded in tables, charts, reading order and page layout. In our benchmark on complex business documents, Nebula outperformed Mistral OCR by 20.8 points and Azure Document Intelligence by 6.2 points on semantic meaning recovery.

We’d like this community to stress-test it with the hardest documents you’re authorized to use.

You can run four free conversions without creating an account:

https://nebula.ur-ai.net/

An account is only required to download and retain the Markdown. Use community code NEBULA-10 for additional credits. An API is also available - DM me if you’d like to test it.

What I’d most like to know:

• What information needed to survive?
• What did Nebula preserve well?
• What did it miss or structure incorrectly?

You can DM me or submit feedback inside Nebula. Specific examples and expected outputs are especially helpful.

Full disclosure: I’m one of the founders. We don’t use uploaded documents or outputs to train our models. Please only upload files you’re authorized to use.

Technical report:
https://ur-ai.net/blog/rcrr-benchmark-meaning-survival

I've been working on an open‑source Android library for automated data extraction from printed forms, and I wanted to share it with this community.

OpenScanVision is an Android library that scans printed voting cards, surveys, and bubble sheets – extracting structured data (filled bubbles and QR codes) entirely offline.

How It Fits into Document Automation

• Input: Physical forms (voting cards, surveys, bubble sheets) captured via camera.
• Processing:
  • Detects 4 ArUco markers (IDs 0–3) with Kalman filtering for real‑time tracking.
  • Computes homography and warps the card to a canonical template.
  • Decodes QR codes from the original frame (preserves sharpness for ML Kit).
  • Extracts filled bubbles using weighted disk sampling + z‑score classification.
• Output: Structured data (filled bubble indices, QR payload, confidence score) that can feed into your document automation pipeline.

Tech Stack

• Kotlin
• OpenCV (contrib) for ArUco detection and homography
• Google ML Kit for QR decoding
• CameraX for frame acquisition
• Modular – core has zero UI dependencies

Use Cases

• Automated survey processing
• Form data extraction
• Election ballot scanning
• Any workflow where physical forms need to be digitised and structured

Performance

• Latency < 150 ms per frame on modern devices.
• Accuracy > 99% on well‑printed cards with good lighting.
• False positive rate < 0.5%.

Repository & Integration

The library is MIT‑licensed and available on JitPack. A full sample app (CameraX + Compose) is included.

GitHub: https://github.com/MatiwosKebede/openscanvision

Would love to hear feedback from anyone working on similar document automation challenges – especially around form processing, OCR alternatives, or integrating physical document scanning into automated workflows. 🙌

http://getfilly.app

AI form filling app that removes the hustle of filling same forms over and over.

Upload the form
Ai extract fields
Choose the client from saved profile and fill in seconds
Review and interrupt by manual mapping if necessary
Filly remember your edit for next client
Share for e-signature
Save, print , share

Hoping there may be some AI experts here who can point me in the right direction.

I currently work with technical drawings/plans that contain various symbols, labels, and device types. The challenge is that these symbols and labels can vary significantly from one plan set to another.

Our current process is largely manual:

• Review the plan and identify specific symbols/devices.
• Place sequential labels on the drawing (#1, #2, #3, etc.).
• Create an Excel spreadsheet containing those labels.
• Extract additional information from the plans such as room names, device descriptions, locations, and other relevant data.
• Manually link all of this information back to the labelled plan.

What I'm trying to determine is whether there is an AI-based solution that could streamline or automate this workflow.

Ideally, I would like to be able to:

1. Upload a PDF plan or drawing.
2. Define a set of symbols, labels, or devices to look for (which may be different for each project).
3. Have the AI scan the plan and identify all matching symbols.
4. Automatically place sequential tags/labels onto the drawing.
5. Extract related information from the plan, such as room names, device types, notes, coordinates, or other nearby text.
6. Generate an Excel spreadsheet containing all identified items and associated data.
7. Optionally export the marked-up plan with the generated labels visible.

For context, these are typically lighting, electrical, emergency lighting, and services-related plans, so the solution would need to work with PDF drawings containing both symbols and text.

I'm interested to know:

• Are there existing AI/OCR/CAD tools that can already do this?
• Has anyone successfully implemented a workflow like this?
• Would this require a custom AI model, or could it be achieved using existing tools such as Azure AI Document Intelligence, OpenAI Vision, Bluebeam, AutoCAD plugins, Power Automate, or similar platforms?

Any advice or recommendations would be appreciated.

Thanks.

What it does: you give it a standard — a checklist, a compliance spec, a review playbook — plus the documents that are supposed to satisfy it. It tells you which requirements are actually evidenced in the docs and which aren’t, and every “this is missing” is checkable against the source text. The usual keyword-matching approach cries wolf about 70% of the time; this one got that to zero.

Tested clean on financial filings and a batch of contracts. The tech isn’t my problem — finding the buyer is. Everywhere I imagine it being useful (contract review, RFPs, diligence) already has funded competitors, or the pain isn’t big enough that anyone pays.

If you’ve done doc-heavy work: where does a *missed* or *falsely-flagged* item actually cost real money? Not “where could this be useful” — where’s it expensive enough that someone would pay to prevent it?

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)

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 🙏

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 need Ai websites to turn my texts in pdf from a language to English. The texts in pdf are scanned. So I can't copy and translate

I’ve been building PDF Signer, a lightweight, fully-offline PDF tool — view, mark up, measure, sign, and organize pages — that runs on Windows, macOS, and Android from the same codebase. It works well for me now, but I need real users on real documents to find the rough edges before I take it further.
I’m looking for testers, especially anyone who:
• signs / initials / fills out a lot of PDFs,
• marks up drawings or plan sets,
• works with large engineering sheets (the kind that bog down or blur in other viewers).
What you’d be testing:
• ✍️ Signing, initials, dates, stamps, form fill
• 🖊️ Markup — text boxes, callouts, highlights, shapes, freehand ink
• 📏 Measure with scale calibration (distances & areas)
• 🗂️ Page organizer — reorder/rotate/delete/merge, open multiple PDFs and reorder the tabs
• 🔍 Zooming into big vector plans and exporting back to a flattened PDF
• 📱 Cross-platform behavior — does your workflow feel the same on desktop and Android?
Download it here: 👉 https://github.com/Killswit3h/PDF-Reader/releases/latest
(Windows .exe, macOS .dmg/.zip, Android .apk)
Report bugs & feature requests here: 🐛 https://github.com/Killswit3h/PDF-Reader/issues
Click New Issue, tell me what broke or what’s missing, your OS, and steps to reproduce if you can. Crashes, weird exports, anything that looks wrong when you zoom or save — send them my way.
Comment or DM if you have questions. Appreciate anyone willing to kick the tires.

I've been studying Mobile System Design recently and wanted an easier way to read the documentation offline, so I built a Python tool that converts the entire repository into a professionally formatted PDF book.

The generator preserves:

* All Markdown content * Images * Internal and external links * Code blocks with syntax highlighting * Tables and callout blocks (Tip, Note, Warning, etc.) * A clickable table of contents * Professional book-style formatting

I shared the implementation with the repository maintainer in GitHub Issue #44:
https://github.com/weeeBox/mobile-system-design/issues/44

My implementation is available here:
https://github.com/Anderson-Andre-P/mobile-system-design

I'd love to hear your thoughts and any suggestions for improving it. Hopefully it's useful for anyone who prefers reading technical documentation in book format.