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I am now going to start doing this to improve the X dataset

stop building parser pipelines 👋🏻 there's a new document parser that is small, fast, Apache 2.0 licensed and is better than all the other ones! 😱 MonkeyOCR is a 3B model that can parse everything (charts, formules, tables etc) in a document 🤠

The best thing junior engineers can do early in their career is learn to solve problems from the problem statement. Not being told what to build—being given a problem and deciding that for themselves. This skill is directly transferrable and foundational to building ML systems.

Learn Docker - it's a big part of machine learning systems

Everybody today:

Dolphin: new OCR model by ByteDance with MIT license 🐬 the model first detects element in the layout (table, formula etc) and then parses each element in parallel for generation ⤵️ model and demo is on Hugging Face Hub 🤗

thanks Hugging Face for working with us on a new intro to mcp course. check it out: huggingface.co/learn/mcp-cour…

this is the most organized structure of an ai project, it’s not just about clean code, it’s also easy to navigate for LLMs and Cursor. always separate config from code, and notebooks from src code Nina actually turned this into a repo template (in replies)

Machine Learning Time Complexities

“Mommy, how did we get so rich?” “Your daddy learned machine learning”

GenAI Project Structure Stay organized because it gets messy really fast.

I've been working on extract data from scanned documents and I have been having a horrible experience with PyTesseract... it only works best on perfect toy data. Luckily, I found PaddleOCR and so far, it is working perfectly. However, the main challenge now is speed.

very cool demo to compare different OCR models (coreOCR, MonkeyOCR, docscopeOCR) 📑

This is next on my list of OCR models to test! Will let you know how it works.