When I use an LLM, it tries to sound like me but there are still tendencies it falls back on, especially when the context window begins to expand.
The 'missing subject nouns' is probably the LLM's way of sounding like an authoritative source in a technical field since many programmers like to write that way.
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.
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.
How else do you think I would have come to write this comment? I got to the second major heading before realizing that there is little human input in this document.
I use LLMs but I will never impose on Claude's intellectual musings on another person as some sort of intellectual insight.
This is about the same as copying someone else's homework and then presenting the copied work as an example of deep brilliance. The copying isn't great, but the boasting is absurd. Who are we trying to con?
Think of an LLM that corrects 898,00 to 888,00. It feels like the David Kriesel Xerox case. Still, it's an interesting way to think of the issue of optical character recognition.
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.
Niels lately posted a lot about other OCR engines: https://www.linkedin.com/posts/niels-rogge-a3b7a3127_lots-of...
I did have it put confidence indexes next to the output per line, and that was pretty useless, they were either really high or really low, and the confidence didn't match the mistakes at all.
What worked: You use an OCR that provides character/word-level bounding boxes and let the LLM extract from data. Then the LLM is capable of "calculating" a confidence of extracted data.
biggest issue is OCR can't distinguish directionality - ie. if someone messages you, or you type "let's cancel the meeting" the text is identical but the intent isn't
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.
