> If you have something to say about the text then say it.

I could point out the individual phrases and describe the overall impression in detail, or I can just compactly communicate that by using the phrase "AI". If it bothers you, read it as "AI-like", so there is a pretension.

I have no problem with using AI for writing. I do it too, especially for documentation. But you need to read it and iterate with it and give it enough raw input context. If you don't give it info about your actual goals, intentions, judgments etc, the AI will substitute some washed-out, averaged-out no-meat-on-the-bone fluff that may sound good at first read and give you a warm wow-effect that makes you hit publish, but you read into it all the context that you have in your head, but readers don't have that.

Formatting and language is cheap now. We need a new culture around calling out sloppy work. You would not have had a problem with calling out a badly composed rambling article 5 years ago. But today you can easily slap an AI filter on it that will make it look grammatical and feel narratively engaging, now it's all about deeper content. But if one points that out, replies can always say "oh, you can't prove that, can you?"

I'm glad they shared too! Wish they shared without letting the LLM process it so heavily, it makes it too hard to read, it gives monotone importance to every piece of text. Mostly it does this by bringing everything up to a slight over-importance with tone and fluff language, and by turning everything into dry statements of fact.

As to why people call this out without going into great detail about the problems with the actual text, it's because this is happening all over the place and it's very disrespectful to readers, who dig into an article that looks very well written on the surface, only to discover it's a lot of labor to decode and often (but not always) a total waste of time. Asking for a critical report of the text is asking even more of a reader who already feels duped.

I got the same impression as the parent post. Even if its not AI-generated, the text reads like a politician's speech at a lot of places. Talks a lot, says little.

The idea itself was very cool, so I endured it. But it was not a pleasant read.

Agreeing first that it is genuinely interesting, let me make a constructive comment on the text: Early on, there are too many small paragraphs that don't on their own make a cogent argument. That important but easily overlooked structural work is pushed back to the reader. I felt rewarded in pushing past that though. Bravo.

This is a nice case study of the downside of creating explicit policies of "no AI comments" without a technical method of enforcing it. I am sure the hacker news comment quality will suffer almost as much from an escalating culture of accusation and paranoia that it will from LLM comment themselves.

> Not really sure what this obsession with calling things you don't like AI generated is but it's poor form

Admonishing someone for correctly identifying AI-written or AI-edited blog posts is poor form, friend.

It is without a doubt written by an LLM. All of the telltale signs are there. I work with these tools 8-20 hours a day and after a while the verbiage and grammatical structures stick out like a sore thumb.

Get off the high horse. I too think this is a very interesting read. I was fascinated with the subject, but the presentation was nauseatingly distracting and immediately sets off yellow flags about how Percepta operates, and what kind of quality they're willing to settle with. It tells me they are more interested in appearances and superficiality.

The numbers that are there categorically cannot be trusted, because hallucinating those details is quite common for models. There is simply no indication that a human adequately proof-read this and therefore any of its claims must be taken with a grain of salt. Don't forget the recent Cloudflare+Matrix debacle: https://news.ycombinator.com/item?id=46781516

I share the same concerns as OP; this post lacks metrics and feels like someone did something cool and raced to get an AI to post about it, instead of giving it a proper treatment.

I read the article and had the same question. It's written in such a way that it feels like it's answering these questions without actually doing so.

The right thing to benchmark against isn't a regular transformer, it's a transformer that writes programs that are then interpreted. They have a little visual demo where it looks faster but only because they make Python absurdly slow, and it's clearly not meant to be a real benchmark.

I spent the whole article thinking, wow, cool, but also ... how is this better than an LLM steering a regular computer? The closest we get is a statement about the need to "internalize what computation is" which doesn't say anything to me.

Fundamentally, running actual instructions on a real CPU is always going to be faster than running them via a neural network. So the interesting part is where they say you can backprop through it, but, ok, backprop is for cases where we don't know how to encode a function using strict logic. Why would you try and backprop through a Sudoku solver? It's probably my imagination is just limited but I could have used more on that.

Benchmark it against a fast Python interpreter optimized for AI tool calling, like Monty: https://github.com/pydantic/monty

I read a lot of LLM text every day, so I'm quite good at seeing the cadence, the narrative structures and the phrasing styles. It's not just "it's not just X but Y" or emdashes. I could point them out and you would say oh humans use this trope or phrasing style too, and of course that's true. It's still a tell. But it's pointless to argue about this.