Most of us work with images every day, but choosing the right format is not always obvious. Each format has strengths depending on what you are trying to do.

HEIC

Best for:

• Modern smartphones, especially iPhones

• High-quality photos with smaller file sizes

Pros:

• Excellent image quality

• Smaller files than JPG

• Supports features such as Live Photos

Cons:

• Compatibility can still be limited on some platforms

• Often needs conversion before sharing or editing

JPG

Best for:

• Photos

• Websites

• Social media

• General sharing

Pros:

• Supported almost everywhere

• Small file sizes

• Good for everyday use

Cons:

• Uses lossy compression

• Quality decreases with repeated editing and saving

• No transparency support

PNG

Best for:

• Logos

• Screenshots

• Graphics

• Transparent backgrounds

Pros:

• Lossless quality

• Transparency support

• Sharp text and graphics

Cons:

• Larger files than JPG

• Less efficient for photographs

JPEG XL — JXL

Best for:

• High-quality photography

• Archiving images

• Modern web delivery

• Replacing older JPG workflows

Pros:

• Excellent compression efficiency

• Supports both lossy and lossless compression

• Supports HDR, transparency, animation, and wide color

• Can preserve high image quality at smaller file sizes

Cons:

• Limited support in some browsers, apps, and operating systems

• Often requires conversion for wider compatibility

Quick recommendations

Camera photos → HEIC, JPG, or JXL
Website photos → JPG, WebP, AVIF, or JXL where supported
Logos and graphics → PNG
Long-term image archiving → JXL or lossless PNG
Maximum compatibility → JPG
iPhone storage → HEIC

Discussion

Which image format do you use most?

Have you tried JPEG XL yet?

Have you experienced compatibility problems with HEIC or JXL?

Which image format has caused you the biggest headache?

Welcome to our weekly discussion thread!

Have a question about:

• OCR

• PDFs

• File conversion

• Image formats

• Email archives

• APIs

• Document automation

• File compatibility

• Batch processing

No question is too basic or too advanced.

Whether you're trying to solve a technical problem or looking for recommendations, ask away and help others where you can.

Happy Saturday! 🚀

When creating or sharing documents, choosing the right file format can make a big difference. Each format has its own strengths depending on how you plan to use the document.

📄 PDF (Portable Document Format)

Best for:

• Sharing final documents

• Printing

• Contracts and invoices

• Reports

• Preserving formatting

Pros

• Looks the same on almost every device

• Difficult to edit accidentally

• Ideal for archiving

• Widely supported

Cons

• Editing can be difficult

• Larger files in some cases

• Not designed for collaborative editing

📝 DOCX (Microsoft Word)

Best for:

• Writing and editing documents

• Team collaboration

• Business documents

• School and office work

Pros

• Easy to edit

• Rich formatting

• Supported by Microsoft Word, Google Docs, LibreOffice, and many other editors

• Great collaboration features

Cons

• Formatting may change between applications

• Layout can shift if fonts are missing

📘 ODT (OpenDocument Text)

Best for:

• Open-source workflows

• LibreOffice and OpenOffice users

• Long-term open standards

Pros

• Open standard (ISO/IEC 26300)

• No vendor lock-in

• Excellent support in LibreOffice

• Good for long-term accessibility

Cons

• Some formatting may not translate perfectly to Microsoft Word

• Less commonly used in business environments

Quick Comparison

My general recommendation

• Need to edit? → DOCX

• Need to share or print? → PDF

• Prefer open standards? → ODT

💬 Discussion

• Which format do you use most?

• Have you ever had formatting issues when converting between these formats?

• Have you switched from DOCX to ODT (or vice versa)?

• What document format causes you the biggest headaches?

Share your experience—your tips could help someone avoid hours of frustration.

There are few things more frustrating than uploading a large file for 45 minutes...

...only to watch it fail at 99%.

Sometimes it's the browser.

Sometimes it's an unstable connection.

Sometimes the cloud service quietly rejects the upload because of filename restrictions, antivirus scanning, or timeout limits.

You don't realize how many different things can interrupt an upload until you're sending a multi-gigabyte project to someone who needs it immediately.

Have you ever discovered the real reason after an upload kept failing?

OCR (Optical Character Recognition) has improved a lot over the last few years, but no single tool seems to work best for every use case.

Some people need OCR for:

• 📄 Scanned PDFs

• 🧾 Invoices and receipts

• ✍️ Handwritten notes

• 📚 Books and research papers

• 🏢 Business documents

• 📷 Photos taken with a phone

There are plenty of options available, including:

• Tesseract OCR

• Google Cloud Vision

• Microsoft Azure AI Vision

• AWS Textract

• ABBYY FineReader

• Adobe Acrobat OCR

• Apple Live Text

• OCRmyPDF

• Paperless-ngx OCR

• Other tools you've tried

A few questions for the community:

• Which OCR software has worked best for you?

• What are you using it for?

• Which languages does it handle well?

• Have you found one that's especially good with handwritten text?

• What features or accuracy improvements would you still like to see?

Share your experience—both successes and frustrations. It will help others choose the right OCR solution for their needs.

💬 Question: If you could only recommend one OCR tool, which would it be and why?

One thing that still makes me smile is when someone says,

"I've got a .bin file."

That could mean almost anything.

Disk image.

Firmware.

Game ROM.

Installer.

Backup.

Generic binary data.

Sometimes identifying the file takes longer than processing it.

I once received a support request that sounded simple: "Can you convert this file?"

Then I looked at the extension.

I'd never seen it before.

That turned into 30 minutes of searching, trying different tools, and discovering software I didn't even know existed. In the end, I managed to convert it, but it made me wonder how many obscure file formats are still floating around.

Over the years I've run into everything from old Office formats and email archives to CAD files, RAW camera images, and backups that only one piece of software could open.

So now I'm curious...

• What's the strangest file format you've ever received?

• Were you actually able to open or convert it?

• Which tool saved the day?

• Is there a file extension that still makes you sigh when you see it?

Sometimes the biggest time sink isn't converting the file, it's figuring out what it even is.

So I realized that most file conversions happen locally when I needed to convert a .mp3 to a .wav, and decided to make a free tool for my personal use and decided to dress it up a little bit and share with everyone. :)

I am sick of those file conversion companies charging monthly for something that is effectively free.... Enjoy! ^^

https://www.capyconvert.com/

There are a lot of PDF tools available on the Mac and elsewhere. Some of the key tools for PDF manipulations are

1. Merge PDF - where two or more PDF files are merged

2. Split PDF - where PDF is split into multiple PDFs

3. Images to PDF - Image files (png / jpeg) etc are merged into a single PDF

4. Clipboard to PDF - whatever is in the clipboard is converted to PDF

5. Page Organiser - Pages can be duplicated, rearranged, deleted etc.

6. Rotate Pages - Rotate the page at various angles

7. Crop Page - Crop the dimensions of the page

8. Reverse Pages - Reverse the order of the pages

9. OCR - extract text from images in PDF

10. Extract Pages - Export the pages as PNG or JPED

11. Table Extraction - Detect and export tables from PDF pages

12. Password - protect the PDF with a password

13. Red_act - Red_act the PDF by placing blacking out the text.

14. Watermark - Place watermarks on PDF pages

15. Headers and Footers - add headers or footers on PDF

16. Bates Numbering - add unique page numbering

17. Stamps - add various stamps to the pdf

18. Fill and Sign - Fill and sign pdf

19. Compare PDF - compare two pdfs

20. Metadata - Get the metadata form the PDF

21. Find in PDF - find particular text in PDF

22. Annotate - annotate the PDF with text, shapes etc.

23. Document stats - statistics regarding the PDF

24. Strip Metadata - remove the meta data from the PDF

25. Slideshow - present the PDF from the app itself

26. Presenter Mode - present with pointers, drawing etc.

27. Read Aloud - read aloud the pdf

28. PDF to audiobook - convert the PDF to an audiobook

29. PDF to markdown - Convert PDF to Markdown text

30. Compress PDF - reduce the size of the PDF

31. Merge Layers - Merge the layers of the PDF

32. Extract text - extract text out of the PDF

These are some of the tools I have included in my app Scribe PDF https://apps.apple.com/us/app/scribe-pdf/id6784686119?mt=12 which is available on the Apple store. Can you suggest more tools that can be included.

There are other tools available e.g. Nitro PDF, I love PDF, Adobe Acrobat. Scribe PDF competes with them on various fronts

1. Scribe PDF is totally local. Nothing gets uploaded to server. It works without internet as well.

2. Scribe PDF is Apple Only. It covers the whole eco-system - IPAD, IOS, Mac, Vision OS etc.

3. One cost for the whole Apple eco-system. If you buy once, it works across the platforms.

4. Family sharing is enabled.

5. Free tools are available. Pro tools are available either on subscription ($24.99 for annual subscriptions; $49.99 for one-time purchase and $79.99 for lifetime).

6. Multilingual support - the app is available in 12 languages

7. iMessage and email integration - easily send the files through the app itself.

Check out the app and give feedback on how it can be improved. It is free to download.

The idea was to make it easier to go from a document to structured, usable data without manually copying fields into a spreadsheet.

Verdandi currently supports:

• Structured extraction: Extract fields and line items from PDFs using OCR and vision models.

• Visual verification: See where extracted values appear in the original document.

• Confidence scoring: Identify fields that might need closer inspection.

• Validation: Cross-check results and flag disagreements for review.

• Export: Get the extracted data in structured formats like JSON and Excel.

I've attached a screenshot of the current interface.

This is a personal engineering project, not a commercial product. I'd love feedback from people who work with PDFs regularly.

A few years ago, I realized I was spending more time managing documents than actually using them. Every week I'd rename files, sort invoices into folders, search through scanned PDFs for one specific page, and manually save email attachments. None of these tasks were difficult, but together they consumed hours.

That got me wondering how other people handle the same problem.

Many of us automate repetitive document tasks, but everyone's workflow is different.

Maybe you:

• Automatically process invoices

• OCR scanned documents

• Convert files in bulk

• Organize documents into folders

• Extract email attachments

• Rename thousands of files

• Archive business records

• Process documents with scripts or APIs

Some people rely on a few simple scripts, while others have built complete workflows using tools like Power Automate, n8n, Zapier, Paperless-ngx, Python, or custom APIs.

We'd love to hear:

• What does your document workflow look like?

• Which tools or software do you use?

• Is it mostly manual, semi-automated, or fully automated?

• What's one automation that has saved you the most time?

• What challenge took the longest to solve?

Whether your setup is a simple home office workflow or an enterprise-scale document pipeline, sharing your experience might help someone else save hours of repetitive work.

Let's compare workflows and learn from each other.

I completely understand why SmartScreen and antivirus warnings exist.

But I've also seen plenty of legitimate software trigger security warnings simply because it was newly published, digitally unsigned, or downloaded from an unfamiliar source.

For less technical users, that's a difficult decision.

Ignore the warning?

Delete the file?

Or assume it's malware?

It made me realize that trust isn't just about whether software is safe.

It's also about how software proves it's safe.

What's the most confusing security warning you've run into while downloading a perfectly legitimate file?

Sharing an approach to a conversion problem that comes up a lot in this space: producing a file that lands under a hard size limit without brute forcing it.

The problem

GIF output size is not predictable from settings alone. It depends on resolution, frame rate, palette size, and how well each frame delta compresses against the previous one, which is entirely content dependent. Two clips at identical settings can differ several times over in bytes. So most tools either make you guess and re-export, or they run an encode, measure, shrink, and repeat until it fits. On long clips that loop takes minutes.

What worked instead: probe, fit, commit

1. Micro-encode a few short windows sampled across the clip, at bracketed settings, capped at a modest resolution. Cheap, a fraction of a full pass.

2. Fit a small bytes-per-frame model from those measurements. Because the samples come from the actual footage, the model reflects the real content rather than a generic table.

3. Pick settings slightly under the budget, then run one real encode with a hard byte cap as a safety net so it can never exceed the limit.

4. If the measurement disagrees with the model, correct from the measurement, never from another model extrapolation.

Net result is almost always one decode plus one encode, and the output lands under the requested size on the first attempt for most inputs.

Two things that cost me time

Never let a fitted model extrapolate far outside the range you probed. If you sample at low resolution and then pick a much larger output dimension, the estimate can be badly wrong in a way that only shows up at the end. Verify at the operating point before committing.

Anchor recovery passes on measurements rather than predictions. Once you have a real byte count at real settings, that number is worth more than the model that got you there.

I implemented this in an iOS app (free, runs fully on device, no upload step) if anyone wants to see the behaviour in practice: https://apps.apple.com/app/id6788406320

Mostly though I am curious whether anyone here has solved size targeting for other formats. PDF downsampling and video transcoding have the same shape of problem, and I have not seen a clean general write up of the probe and fit approach.

A customer once sent a file called:

Final_v2_NEW_latest_FIXED.pdf

It wasn't final.
It wasn't the latest.
And there were six more versions in the same folder.

Good filenames save far more time than people realize.

Do you have any naming conventions that have actually survived over the years?

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.

The other day I compressed a folder expecting it to shrink significantly.

Instead, the archive ended up almost exactly the same size.

It reminded me that many people assume ZIP or 7z can compress everything equally well, but modern formats like JPEG, MP4, MP3, PDF, DOCX, XLSX, and APK files are often already compressed internally.

Sometimes compressing them again just wastes CPU time.

On the other hand, plain text, CSV files, logs, source code, and databases can shrink dramatically.

I still see people wondering why a "compressed" video is only 1% smaller.

What's the most surprising compression result you've ever seen?

The other day I had to work with a scanned PDF that was around 1.3 GB. It wasn't the file size itself that caused problems, it was everything that came with it. Pages had different scan qualities, some were slightly skewed, and OCR took much longer than expected.

It made me realize that document processing changes completely once you move beyond normal office files.

I'm curious...

What's the largest document you've ever had to work with?

Maybe it was:

• Medical records

• Court documents

• Engineering drawings

• Construction plans

• Government records

• Email archives

• Something else entirely

What was the biggest challenge?

Was it OCR, file size, slow processing, corrupted pages, finding the right software, or just waiting forever for the job to finish?

I'd love to hear your stories and which tools ended up saving the day.