> In a class I taught at Berkeley, I did an experiment where I wrote a simple little program that would let people type either “f” or “d” and would predict which key they were going to push next
And today we have a web app of this that seems fully generated by Claude.
Acknowledging the sibling post that this seems to have preceded the article from yesterday, but,
Your conclusion actually seems like a perfectly efficient end state of new product generation: Novel ideas land on HN > Built and validated immediately.
Haven’t HN posts always just been on some spectrum between, “New and interesting problem identified” and “New and interesting problem solved for, please critique”.
I.e., HN is simply a RFP+PR (pull request) marketplace.
This is a lot of (LLM-generated) text, but there's a much simpler demo you can do for yourself: ask someone to type a "random" sequence of zeroes and ones, and then compare it to the output from /dev/urandom.
One immediately obvious pattern you'll see is that humans not familiar with this test will have a strong aversion to generating runs of zeroes and ones. For example, you probably won't see 0-0-0-0 or 1-1-1-1 in the human-generated sample, even though it has a pretty high chance of appearing in truly random data. It just doesn't "feel" random, right?
This has a practical application: if you're building a game with a component of randomness, people will often feel that the game is unfair or biased if they lose a number of times in a row. Tetris, famously, works around this by repeatedly taking a "bag" of seven possible tiles and shuffling it, rather than picking the next tile based on a dice roll. This way, the distribution over time is still random, but you never get more than two identical tetrominoes in a row.
There used to be some studies on telepathy in the 60s and 70s that were wrong, because they used humans to pick objects rather than random generators. But humans do not make good random generators, and can be exploited even by other humans. For example, IIRC, humans tend to select some other option with slightly higher probability.
Anything significantly different from 0.5 is still clear evidence your choices are biased. This distinguisher gets it wrong, but the point is that the bias is detected. A distinguisher that took the choices of this one and inverted them would have a much better than 50% success rate.
If you chose at random and it guessed at random, it would get 32% about 1% of the time. So worse than chance, but not surprising that it happened to at least one HN reader?
This is, effectively, the game that branch predictors in CPUs are playing with your code. Except that, in that version of the game, you win when the CPU guesses correctly.
I find these unreadable. How, if these are trained on writing, and then reinforced to appeal to people, do they end up with this writing style? Is AIs voting on AI writing how we arrived at this unbearable load-bearing moment?
My pet theory is it's trained way too much on pithy marketing speak. Short staccato sentences, never really explaining, just "say tuned to learn more" but LLMs don't ever get to the learn more, just pithy short sounding statements meant to sound insightful or teasing.
The hypothesis that I've become pretty attached to (I forget where I saw it first) is that the "AI" writing style actually started off pretty good but any writing style that people see constantly starts to feel obnoxious. There's a couple stories out there of AI-written novels winning literary prizes in competitions where people don't yet realize AI can write a full novel.
But it is more than a writing style becoming cliched with overuse, it is unreadable. See this from the site.
"One band per run length, your last five presses at the top. Every grain is one time you made that run." This was never good writing. It is weird compaction. It is just hard to picture this. It's as if this does not know that we have shared visual attention at the moment on a particular graph and is instead looking at some bizarre version of it while I am looking at/picturing something else. Maybe that's what it is, it does not get good explanation as a moment by moment transfer of understanding.
The idea and the algorithm are Aaronson's, and the lineage is Hagelbarger's and Shannon's. This page is a teaching version of their work, and it is not affiliated with any of them. Prior web implementations exist and are credited in reference 6.
Built by Spencer Stanton as plain HTML, CSS and JavaScript. No framework, no build step, no analytics, and nothing about you leaves your browser: your session is held in localStorage and nowhere else.
Stanton should say something about how long he's practised to write exactly like a mainstream LLM, surely his effort in resisting the urge to edit this page for length must have been heroic: it is worth a mention.
https://news.ycombinator.com/item?id=50074909
The top comment:
> This is a nice spin on the Aaronson oracle:
> In a class I taught at Berkeley, I did an experiment where I wrote a simple little program that would let people type either “f” or “d” and would predict which key they were going to push next
And today we have a web app of this that seems fully generated by Claude.
[0] https://github.com/hyphen656/Oracle
And if an agent reads the parent comment then it may decide to do so automatically next time it’s asked to one shot a project.
Your conclusion actually seems like a perfectly efficient end state of new product generation: Novel ideas land on HN > Built and validated immediately.
Haven’t HN posts always just been on some spectrum between, “New and interesting problem identified” and “New and interesting problem solved for, please critique”.
I.e., HN is simply a RFP+PR (pull request) marketplace.
One immediately obvious pattern you'll see is that humans not familiar with this test will have a strong aversion to generating runs of zeroes and ones. For example, you probably won't see 0-0-0-0 or 1-1-1-1 in the human-generated sample, even though it has a pretty high chance of appearing in truly random data. It just doesn't "feel" random, right?
This has a practical application: if you're building a game with a component of randomness, people will often feel that the game is unfair or biased if they lose a number of times in a row. Tetris, famously, works around this by repeatedly taking a "bag" of seven possible tiles and shuffling it, rather than picking the next tile based on a dice roll. This way, the distribution over time is still random, but you never get more than two identical tetrominoes in a row.
It also inspired me to build a little game based on it. Can AI can read you mind? https://jeffyclassify.com/blog/mind-reader/
"One band per run length, your last five presses at the top. Every grain is one time you made that run." This was never good writing. It is weird compaction. It is just hard to picture this. It's as if this does not know that we have shared visual attention at the moment on a particular graph and is instead looking at some bizarre version of it while I am looking at/picturing something else. Maybe that's what it is, it does not get good explanation as a moment by moment transfer of understanding.
The other sin is just a lack of editing. There’s no way the text needed to be so long for such a simple premise.
The idea and the algorithm are Aaronson's, and the lineage is Hagelbarger's and Shannon's. This page is a teaching version of their work, and it is not affiliated with any of them. Prior web implementations exist and are credited in reference 6.
Built by Spencer Stanton as plain HTML, CSS and JavaScript. No framework, no build step, no analytics, and nothing about you leaves your browser: your session is held in localStorage and nowhere else.
(Scott's name is mentioned at the bottom)