It's hard not read such quotes and immediately think of the Infinite Monkey Theorem.

https://en.wikipedia.org/wiki/Infinite_monkey_theorem

If at the end Shakespeare-level literature is produced, does it matter whether we arrived there by random keystrokes?

The problem is the infinite other literature produced along the way. It doesn't work to throw more monkeys at the problem of verification, and checking for matches against existing Shakespeare plays is cheating.

don't worry, we can get another swarm of random monkeys typing and decide what ones to eliminate .

tijebs gi yo,

Yes because it is akin to producing a universe of slop and trying to find the single atom of gold.

Yes because the keystrokes aren't free and the world is finite. We need to pay these monkeys in bananas.

Shakespeare-level literature was produced. We are the infinite monkeies, and one was shakespeare.

How much slop you figure Shakespeare produced?

Honestly judging from the volume of his output, a ton.

It’s rare to run into prodigious artists who simply produce a few perfect pieces - as a rule those few magnum opuses are built on a hill of cutting room floor scraps.

The director James Cameron stands out because he has only produced huge box office hits. Extremely hard to do like you said, and most artists and auteurs would be happy to have one great work they can be remembered for, even if it's amidst a dozen forgettable pieces.

He's only published blockbuster hits. But I'm sure he has lots of practice films that have never seen the light of day?

Sometimes the forgettable pieces come later. In JC's case, that's Avatar 2

Don't let your personal love of Piranha II convince you that it was a box office hit.

And then there are those like J.S. Bach.

I don’t know how to feel about HN thinking one of the greatest artist of all time produced slop. You guys really think Leonardo davinci was drawing ladies with 6 fingers before the Mona Lisa?

I assure the worst of Shakespeares work is far beyond anything most writers can produce, much less the incoherent slop LLMs generate

> does it matter whether we arrived there by random keystrokes?

Douglas Adams said this beautifully in the Campaign for Real Time & the poet Lallalfa.

And I just found out h2g2.com is no longer online, so no reference.

2*128 is the UUID namespace, which is astronomical. That is like 21 letters. Making one sentence of Shakespeare by random search is strictly 0% chance. SHA256 is ~43 letters then. "LLM broke all of cryptography and creates random data to fit a hash" -- are we there yet? Random search does not work at this scale. In all their cases, they played with pre-existing well-studied software, so they had an embedded fitness function. An extremely detailed one.

Monkeys have been reading Shakespeare all their lives (training). Effectively, this is LLM uncompression.

What embedded fitness fuctions are you thinking of?

And if in the end it does not produce masterworks, going faster and breaking things will be seen as a huge and credulous waste of time that poisoned the internet and some codebases beyond redemption.

But I’m glad someone else is running all these experiments so we can see who is right in their guess about the outcome.

Right. The joke with shakespeares monkeys or borjes library is that you just do that and then filter out the books that match shakespeares works.

If you already know what you want in an infinite library, it is there. If you don't know exactly what you're looking for to the letter, you might not be able to find it.

Yes, because the folks waiting slack-jawed for the monkeys to finish will no longer have the skills or discernment to know what Shakespeare looks like.

Yes it will be a terrible waste of time since dedicated humans can do it faster and produce better stuff. I would prefer literature created like that instead of just random monkeys writing what they don't understand. We also lose the ability for writers and creators to get better at their craft and produce unique works instead of just randoming spamming keys for millions of years

Isn't that the core fundamental of AI? Constrain the results to a latent space which has all the probable answers and then pick up whichever token makes sense based on your random sampling?

I feel like the whole concept of harness and LLMs IS to pick random tokens which make sense. Think about this as a solution space (a very complex one), then the harness and the LLM are moving towards the local minima at which the code is complete. This is akin to how we think about AI anyways.

Yes, agentic coding is machine learning. Not because of the underlying language models, but because it is gradient descent, where the test harness provides a gradient for the agent to traverse.

I mean why even use VCS at that point?

imho, to be able to revert and recall as agents loose context due to new spawn or compaction.

Seems very reasonable to me tbh, the VCS was built to facilitate human work structures, rebuilding it to facilitate autonomous work seems appropriate.

Making a high-throughput revision control system is doable, there have been experiments here on HN with git-in-postgres etc. The question is, what is the actual bottleneck here?

I made a VCS for LLM assisted work, bootstrapping it on its own code as it was developed. In fact, if it did not really work, it would not even exist! In my experience, keeping track of things and evaluating them was the bottleneck and I focused on that https://replicated.live/blog/worktree Effectively, the question is "how to leverage human evaluation and feedback".

The Cursor people concluded their last post with "taste, judgement, and direction came from humans". Sounded surprisingly sane after "don't compile it, commit it faster" some paragraphs earlier.

People often think of tech/business as bottleneck issues ie whats my limiting factor. That's relevant to pre-ai business as humans are relatively self organizing.

The issue with AI is it has no real memory, context, long term reasoning, etc. These are a lot of what we use to determine how well we're doing something as well as why.

The "bottleneck" so to speak with ai is that it will just circularly build junk forever, expand scope infinitely, and just continue building.

At its best it will autobuild your unicorn saas. But on average it's a cancer cluster of ever expanding code with no plan.

The issue with unsupervised AI is that when you increase the throughput you decrease the amount of human supervision. So you have to have some way to deal with that.

If I'm going to give AI a gigantic task that's really complicated and it will probably mess it up 100s of times, how will I compensate for that without constantly looking over it's shoulder?

But that's mostly just vanilla models.

Some critical parts of agentic development are literally months old while most mature ones are merely a couple years old.

Critical thinking was the most critical checkpoint for models to reach.

Now its only a matter of time for the right tooling and orchestration to come along. Once agent memory and recall works well, all of those bottlenecks will go away one by one.

Back in my early career days, CPUs were clocked at 1GHz. That was 20-25 years ago.

They probably just vibe coded it with ChatGPT

What would have happen if you do not commit at all?