"I think of this fragment as the Director’s Cut of Autoware."

"A deep dive that treats every technical choice as a strategic decision, and how it's done. I discovered ‘dead Hydranet’, the surprising AutoSteer strategy, how use real results come from the inference in cpp code.

​I liked the Jupyter Notebook the most: I ran some quick tests using personal video files to see how well it performs in a real-world scenario"

"Seeing the open-source project's development alone clears the mind of many intrusive thoughts and what-ifs"

"All the clues were in the emails, but I thought Autoware was dealing with surround cameras already. Turns out that's a future version.

In this fragment, I learned the ease of onnx files but also why I never see them with research papers. Tracking one closest in-path object instead of everything of course. Production code. Training on many datasets at once, but I'm curious if all the current models are useful once rear and side cameras are added.

I really liked the trifecta of Autoware diagrams, connecting that to their code, and notebook. Seeing the open-source project's development alone clears the mind of many intrusive thoughts and what-ifs"

"Thank you for sharing The Edgeneer’s Land. I found it to be a thoughtful and well-conceived initiative that brings together creativity, engineering thinking, and real-world problem-solving in a very natural way. 

What stood out to me most is the emphasis on curiosity-driven learning and hands-on exploration, which is exactly what is needed to nurture future innovators. I truly appreciate the clarity of vision and the intent behind creating such an ecosystem."

"Thank you Jeremy for Edgeneer Land, where we can get the latest updates in autonomous technology, insight into real world applications via company interviews, and deep dives into the algorithms that shape the future of the autonomous world."