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.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit electronics.dev