In this special guest episode, Matt (founder of Atopile) talks with Tyler Maran, founder and CEO of OmniAI, about how traditional OCR gives way to AI-native tools that actually work. OmniAI uses cutting-edge vision-language models to parse datasheets, extract data from messy PDFs, and interpret charts, with accuracy levels that leave legacy OCR in the dust.
This episode dives deep into how modern AI models can read and understand electronics documentation, turning datasheets into structured, machine-usable formats like Markdown, HTML, and JSON. Forget brittle extraction rules and regex hacks — this is about intelligent parsing.
💡 What you’ll learn today:
How OmniAI uses vision-language models to parse 600 million datasheet pages
Why LLMs are outperforming traditional OCR in accuracy and cost
The insane complexities of converting PDF charts into usable circuit data
How AI models handle errata, tribal logic, and embedded graphs
The concept of "thinking models" vs "simple models" for different document types
Why designing PCBs with code gives you a feedback loop, just like software
What “agent mode” looks like for parsing hardware documentation
Why human-level AI might be running on iPads in 2 years
How Matt uses Atopile and Cursor to compile hardware like software
Plus: patent law hacks, robotic bartenders, anarchist hackerspaces, and how laziness can be a superpower in engineering.
Share this post