Comment by kristianp
5 hours ago
I noticed some AI tells, but found overall the article not too bad. It did seem to waffle at times though.
> Storing a lake as thousands of 1 MB Parquet files is a bad practice anyway, and 2.0 does not rescue it.
The "does not rescue it". No human would write like that.
> I'll explain what that means on a table you already know.
No I don't already know that table.
Also
> and claims 40x on graph reachability
Is really hard to parse.
The section on recursive CTEs wasn't well written and didn't explain how the optimisation was done. This article explains how the recursive CTEs were improved https://duckdb.org/2026/08/25/how-duckdb-runs-recursive-ctes...
> The "does not rescue it". No human would write like that.
This is what I don’t understand. Supposedly LLMs are trained on human text. Why do they come up with such unrealistic prose? Is it intentional because the companies want the tells to be obvious?
They’re not just trained on human prose. They’re sent to RLHF, and also their language changes as a result of RL on verifiable rewards.
Getting it to write well is really hard because there’s no real way to verify whether it’s good prose or not. You and I can tell, but we can’t write a verifier that codifies our judgment.
Maybe they’ll find a way to improve this, but for now it’s certainly one of the harder problems to solve for LLMs.
Part of it is that I think they also have poor theory of mind, which I imagine is also a hard thing to train it to do.
Why is it hard? Ask it to write professionally in mid-twentieth century style English, and without resorting to the clickbait style of writing.
In any event, other LLMs may not automatically have the problem, and don't even require such a prompt. This is a Claude problem.
4 replies →
For autoregressive models (practically all hosted ones), it's because of the nature of next-token prediction. LLMs lock themselves into a particular sentence structure ahead of time and have to guess at the rest of the sentence. Samplers have no insight into the LLM's "intent" aside from the probability of each next token, and the LLM has no insight into its previous "intent" that resulted in a given probability in the first place. I don't know if this is possible to solve with more training, I think a fundamental architectural shift may be needed, like more research into diffusion language models.
I think that's quite a strong claim, especially since chain-of-though reasoning means a modern LLM has its own private scratchpad to workshop sentence structure in, if it were a significant problem.
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> The "does not rescue it". No human would write like that.
On the contrary. It is unnatural for a native speaker, which I don't think the author is. For someone that speaks English as a second language, it is not uncommon to use expressions literally translated from their first language, which may be understandable but weird for native speakers.
The author does read to me as genuinely ESL, but there is also blatant LLMish mixed in. Perhaps they used LLMs for translation or drafting. For example:
> DuckDB 2.0 is coming this fall and the alpha is out! I ran the interesting features on my own laptop, and against S3, to see what actually changes for people who build tables and pipelines rather than database engines.
The first sentence looks purely ESL, the second one looks LLM.
> Because yes, DuckDB 2.0 is faster. But to get the speed bump you need to understand how your data is shaped, and sometimes how to model it.
The second sentence here looks LLM as well.
> One comment on the tiny files: no meaningful change, because the time there is per-file round trips (footer, then data) that reading ahead cannot remove. Storing a lake as thousands of 1 MB Parquet files is a bad practice anyway, and 2.0 does not rescue it. Fundamentals still matter!
This reads as LLM with no sign of ESL left.
All in all, this seems to follow the recent trend where the very beginning of the article shows the most user input while the rest is mostly generated. The only confounding factor here is the user seems ESL as well but there's still LLM all over.
Hilarious that even DuckDB's article has many of LLM tells as well