Interesting complaint, because many might not share any of their ideas if it weren't for LLMs making it easy. Not everyone has the incentive to dedicate a day to producing writing worth publishing. But maybe they would if it took significantly less time.
Even considering HNs no LLMs for comments rule, which I mostly agree with, I think we would all lose of the same rule were applied to publishing in general.
All of the output beyond the prompt contains, definitionally, essentially no useful information. Unless it's being used to translate from one human language to another, you're wasting your reader's time and energy in exchange for you own. If you have useful ideas, share them, and if you believe in the age of LLMs, be less afraid of them being unpolished and simply ask you readers to rely on their preferred tools to piece through it.
Is there a SOTA OCR model that prioritises failing in a debuggable way?
What I want is an output that records which sections of the image have contributed to each word/letter, preferably with per word confidence levels and user correctable identification information.
I should be able to build a UI to say: no, this section is red-on-green vertically aligned Cyrillic characters; try again.
Very helpful analysis that confirms everything I’ve encountered. OCR remains a thorny issue. The author talks about professional workflows struggling with tables and such, but I’ve found it challenging to get clean copies of long documents (books). The hybrid workflow (layout then OCR) sounds promising.
It's very surprising to me that the state of the art tools for data entry and digitizing still require a lot of supervision. From the article it's not that surprising that handwritten documents are harder for old-school OCR or AI as that can be hard even for humans in some cases. But tables and different layouts seem like low hanging fruit for vision models.
Truer words have never been spoken. LLMs make mind blowing demos, but real-world performance is much less (but still useful).
An example from yesterday:
I asked Google / Nano Banana to repaint my house with a few options. It gave a nice write up on three themes and a nice rendering of 1/3 vertical slices in one image of each theme.
Then, I asked it to redraw the image entirely in one of the themes. It redrew the image 1/3 in the one theme I asked for and 2/3 in a theme I did not ask for. Further prompting did not fix it. At the end of the day, this was a useful exercise and I was able to get some sense of what color scheme would work better for my house, but the level of execution was miles away from the perfection portrayed in demos and hypester / huckster bloggers and VCs.
Please write in your own words! I’m not inclined to read something if it consists of what you copy and pasted from Claude
Interesting complaint, because many might not share any of their ideas if it weren't for LLMs making it easy. Not everyone has the incentive to dedicate a day to producing writing worth publishing. But maybe they would if it took significantly less time.
Even considering HNs no LLMs for comments rule, which I mostly agree with, I think we would all lose of the same rule were applied to publishing in general.
"I would rather read the prompt"
https://claytonwramsey.com/blog/prompt/
discussion: https://news.ycombinator.com/item?id=43888803
All of the output beyond the prompt contains, definitionally, essentially no useful information. Unless it's being used to translate from one human language to another, you're wasting your reader's time and energy in exchange for you own. If you have useful ideas, share them, and if you believe in the age of LLMs, be less afraid of them being unpolished and simply ask you readers to rely on their preferred tools to piece through it.
Is there a SOTA OCR model that prioritises failing in a debuggable way?
What I want is an output that records which sections of the image have contributed to each word/letter, preferably with per word confidence levels and user correctable identification information.
I should be able to build a UI to say: no, this section is red-on-green vertically aligned Cyrillic characters; try again.
Very helpful analysis that confirms everything I’ve encountered. OCR remains a thorny issue. The author talks about professional workflows struggling with tables and such, but I’ve found it challenging to get clean copies of long documents (books). The hybrid workflow (layout then OCR) sounds promising.
It's very surprising to me that the state of the art tools for data entry and digitizing still require a lot of supervision. From the article it's not that surprising that handwritten documents are harder for old-school OCR or AI as that can be hard even for humans in some cases. But tables and different layouts seem like low hanging fruit for vision models.
> The Demo Works. Production Does Not.
Truer words have never been spoken. LLMs make mind blowing demos, but real-world performance is much less (but still useful).
An example from yesterday:
I asked Google / Nano Banana to repaint my house with a few options. It gave a nice write up on three themes and a nice rendering of 1/3 vertical slices in one image of each theme.
Then, I asked it to redraw the image entirely in one of the themes. It redrew the image 1/3 in the one theme I asked for and 2/3 in a theme I did not ask for. Further prompting did not fix it. At the end of the day, this was a useful exercise and I was able to get some sense of what color scheme would work better for my house, but the level of execution was miles away from the perfection portrayed in demos and hypester / huckster bloggers and VCs.