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.
I have also found that LLMs do not help me communicate my ideas in any way because the bottleneck is getting the ideas out of my head and into the prompt in the first place, but I will disagree with the idea that the output beyond the prompt contains no useful information.
In the article you linked the output he is complaining about probably had a prompt like this: "What are the downsides of using Euler angles for rotation representation in robotics? Please provide a bulleted list and suggest alternatives." The LLM expanded on it based on its knowledge of the domain or based on a search tool (or both). Charitably, the student looked it over and thought through the information and decided it was good (or possibly tweaked around the edges) and then sent it over - though in practice they probably just assumed it was correct and didn't check it.
For writing an essay like "I would rather read the prompt" LLMs don't seem like they would speed up the process much, but for something that involves synthesizing or summarizing information LLMs definitely can generate you a useful essay (though at least at the moment the default system prompts generate something distinctively bland and awful).
Pretty balanced take. I think if a human gains information or saves time, it's still worthwhile. Surely, I don't publish those clickbaits. That's AI slop.
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?
I think I made it obvious what the article is about: no boasting, not "copying someone else's homework". Which text did you last publish? Can you be more specific? I would be genuinely interested in specific changes you would do if you were the editor.
> I mean "a" text! I was just curious how you write. Do you prefer to write comments?
In all fairness, I've been accused of sounding like an LLM this year, which is quite unfortunate as I think we're coming to the end of careful writing.
This reads less like LLM output than it does someone just transcribing their brief notes as they did their research. Lot of missing subject nouns, which is not something I'd expect to see from AI output.
You can ask an LLM to write in a different voice—they don't all sound exactly the same, though this one is no different than other examples.
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.
- Subtitles have the rhetoric turned to 11 with LLMs. (Note: Who has ever had multiple sentences as a blog post heading? It's bizarre) :
- LLM "The Demo Works. Production Does Not."
- Human "AI is why this project exist, and why it's as complete as it is"
- Sources for claims that call for evidence
- LLM "Six months ago, a practitioner could name a preferred OCR engine with confidence. Based on what I read, that confidence is gone." - *What was read?*
- Human "AI coding tools and playing slot machines"[ref]
- Variable paragraph lengths, where things that need more explanation have longer paragraphs (and vice versa)
- LLM *Scroll through—each thing is about the same length*
----
There are lots of tells like this. This is a moment to get good at detecting LLM text in case it's surreptitiously used to your detriment.
Absolutely. You got the joke, or? This was the main point of the full article. No primary sources. Only unverified aggregates. Strong contrast to what I did normally once per month.
> Variable paragraph lengths
I tried to compare it to the URL you posted. It's quite similar. I would have rather have said. Shorter sentences. Shorter Paragraphs. But let's not fight on this ;)
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.
I found only a few that correct OCR by using LLM. I think it feels too risky.
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.
Funny enough I was processing some handwritten tables into excel with Sonnet. It did way better than I thought it would, I'd say like 95%.
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.
IMHO LLMs cannot provide statistically confident measures, and they are terrible at pretending to be capable of doing so.
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.
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.
The relevant term is "bounding box", as you probably need the confidence level of a character or word, not just the image. I built such an interface. I think the effort is only worth it if you really have multi-millions of pages.
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.
Speaking about "that the state of the art tools", might be 6 months or 20 years old. Surfaced opinions might rely on software that a company licensed 2 years ago. Sadly, we need to take this enterprise speed of adaptation into account.
working with continuous OCR capture across 3 monitors using screenpipe. at 1.2fps you get usable text extraction but use 600mb-2gb ram.
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.
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.
I have also found that LLMs do not help me communicate my ideas in any way because the bottleneck is getting the ideas out of my head and into the prompt in the first place, but I will disagree with the idea that the output beyond the prompt contains no useful information.
In the article you linked the output he is complaining about probably had a prompt like this: "What are the downsides of using Euler angles for rotation representation in robotics? Please provide a bulleted list and suggest alternatives." The LLM expanded on it based on its knowledge of the domain or based on a search tool (or both). Charitably, the student looked it over and thought through the information and decided it was good (or possibly tweaked around the edges) and then sent it over - though in practice they probably just assumed it was correct and didn't check it.
For writing an essay like "I would rather read the prompt" LLMs don't seem like they would speed up the process much, but for something that involves synthesizing or summarizing information LLMs definitely can generate you a useful essay (though at least at the moment the default system prompts generate something distinctively bland and awful).
Pretty balanced take. I think if a human gains information or saves time, it's still worthwhile. Surely, I don't publish those clickbaits. That's AI slop.
Sounds reasonable until you consider that the "prompt" might include a million tokens of context, not to mention follow-up/iterations
Did you read the article?
> Did you read the article?
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?
I think I made it obvious what the article is about: no boasting, not "copying someone else's homework". Which text did you last publish? Can you be more specific? I would be genuinely interested in specific changes you would do if you were the editor.
> Which text did you last publish?
Never published an LLM text, friend.
And if somebody needs Claude to get something published, that person should find a better line of business, one more suited to her or his aptitudes.
> I would be genuinely interested in specific changes you would do if you were the editor.
This whole thing would get sent back with the kind request to think of an argument and write it out. By hand. Without an LLM.
I mean "a" text! I was just curious how you write. Do you prefer to write comments?
> I mean "a" text! I was just curious how you write. Do you prefer to write comments?
In all fairness, I've been accused of sounding like an LLM this year, which is quite unfortunate as I think we're coming to the end of careful writing.
This reads less like LLM output than it does someone just transcribing their brief notes as they did their research. Lot of missing subject nouns, which is not something I'd expect to see from AI output.
You can ask an LLM to write in a different voice—they don't all sound exactly the same, though this one is no different than other examples.
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.
Here's a great example of something written by a human that otherwise seems to have a similar structure to the OP:
https://lalitm.com/post/building-syntaqlite-ai/
Flags for LLM vs human drafting:
- Subtitles have the rhetoric turned to 11 with LLMs. (Note: Who has ever had multiple sentences as a blog post heading? It's bizarre) :
- Sources for claims that call for evidence
- Variable paragraph lengths, where things that need more explanation have longer paragraphs (and vice versa)
----
There are lots of tells like this. This is a moment to get good at detecting LLM text in case it's surreptitiously used to your detriment.
I'll amend my statement; I think the comparison text was written by an LLM with human editing. As I read it more, there are also some LLM-isms there.
Ok, let's not discuss the content but the format.
> Who has ever had multiple sentences?
Many? https://forum.wordreference.com/threads/two-sentences-in-a-t...
> Sources for claims that call for evidence
Absolutely. You got the joke, or? This was the main point of the full article. No primary sources. Only unverified aggregates. Strong contrast to what I did normally once per month.
> Variable paragraph lengths
I tried to compare it to the URL you posted. It's quite similar. I would have rather have said. Shorter sentences. Shorter Paragraphs. But let's not fight on this ;)
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.
I found only a few that correct OCR by using LLM. I think it feels too risky.
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.
Funny enough I was processing some handwritten tables into excel with Sonnet. It did way better than I thought it would, I'd say like 95%.
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.
IMHO LLMs cannot provide statistically confident measures, and they are terrible at pretending to be capable of doing so.
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.
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.
The relevant term is "bounding box", as you probably need the confidence level of a character or word, not just the image. I built such an interface. I think the effort is only worth it if you really have multi-millions of pages.
Niels lately posted a lot about other OCR engines: https://www.linkedin.com/posts/niels-rogge-a3b7a3127_lots-of...
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.
Speaking about "that the state of the art tools", might be 6 months or 20 years old. Surfaced opinions might rely on software that a company licensed 2 years ago. Sadly, we need to take this enterprise speed of adaptation into account.
working with continuous OCR capture across 3 monitors using screenpipe. at 1.2fps you get usable text extraction but use 600mb-2gb ram.
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
You scrape your screen continuously and OCR it? Never heard of this use case.
> 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.