donut VS openpilot

maybe this is better? https://github.com/clovaai/donut

I'm not sure

Yup! But I'm still exploring options. (any recommendations would be welcomed!) Here are some candidates I'm considering:

- https://github.com/mindee/doctr

- https://github.com/open-mmlab/mmocr

- https://github.com/PaddlePaddle/PaddleOCR (honestly I don't know Mandarin so I'm a bit stuck)

- https://github.com/clovaai/donut - While it's primarily an "OCR-free document understanding transformer," I think it's worth experimenting with. Think I can sort this out by letting the LLM reason through it multiple times (although this will impact performance)

- yesterday got a suggestion to consider https://github.com/kakaobrain/pororo - I don't think development is still active but the results are pretty great on Korean text

I am also interested in experimenting with something like DONUT (https://github.com/clovaai/donut) but I have never seen anything on what the VRAM expectations are for something like this. Does anyone know also if there are any newer better models than this for document parsing as well? Or what the VRAM requirements for something like this tend to be?

Here are a few useful resources you could start with: [Pix2Struct by Google Research](https://github.com/google-research/pix2struct) might be a valuable tool, although it will most likely need some fine-tuning to fit your specifics. You can also find some fine-tuned models on HuggingFace by searching 'pix2struct'. Another option worth considering is [DonutI](https://github.com/clovaai/donut). Like Pix2Struct, fine-tuning likely needed to meet your requirements. Tesseract OCR is another alternative, particularly for handling text. It's primarily designed for pages of text, think books, but with some tweaking and specific flags, it can process tables as well as text chunks in regions of a screenshot. Bit too much tweaking for my taste. As I'm also in search of OCR tools for UI and chart screenshots, so share if you find something else.

Yes, but he spent several years in self-driving cars (https://comma.ai), which while interesting is also a space that a lot of players are in, so it's not the same as seeing him back to doing stuff that's a little more out there, especially as pertains to IP.

We have a product for sale: https://comma.ai

We raised $18.1M and have made $28M in lifetime revenue to date.

Where are you getting your narrative?

What about comma? https://comma.ai/ Seems like our old friend geohot built exactly what you want.

Positive HN discussion: https://news.ycombinator.com/item?id=36927971

The issue was that the front camera on the windshield couldn’t see under the hood. You misunderstand how easy it is to solve for depth and distance with AI without requiring stereo cameras. Read https://github.com/commaai/openpilot

Nice to have: I want to get a self-driving add-on that supports some cars better than others. Not a must but high up on my nice-to-have list.

EDIT: I probably should have added that I currently have the base model of the Kona the lowest model available, and I am looking for a similar thing in the ioniq 6, because my understanding is that it's fully compatible with the comma.ai device and therefore I am not planning on getting the better on board driving system, the Kona that I got unfortunately was not compatible with that device.

I wonder how bad this is compared to the competition. https://comma.ai allows you to add self-driving features to a large number of non-Tesla cars so, if we’re including physical firmware hacks as a threat vector, I’d bet tons of alternative cars (new enough Honda Odysseys, Toyota Siennas, etc: probably anything with adaptive cruise control and lane following) have the same sort of potential vulnerability.

Not sure, but from the first article from 4 years ago:

>Last month, we had 1,209 cars drive a little over 1,000,000 miles

Let's say they've had zero growth since then, so 48,000,000 conservatively?

Actually, from their website [1]:

>100+ million miles driven and 10k users.

[1]: https://comma.ai