I’ve been using fable recently not to write code, but to write briefing notes on projects. “Read and compare these three libraries. What are the unique differences in their implementations? Why were those ideas chosen? What are the pros and cons of those designs? What ideas would you copy into a new project? Write a report on your findings.”

In one of the studies they got incoming CS students to fill out a multiple choice exam on programming, before the class had even started. Then they looked for hints in their exam results to see if they predicted the end of semester grade.

Turned out following this strategy was an incredibly strong predictor of success in computer science. Some students somehow got 0 (or close to it) in the multiple choice test. But the researchers looked at their answers, and could see that the student was forming hypotheses about the questions. Even if they got all the answers wrong, they answered all of them with a consistent idea of what the computer programs might have meant.

Weak students just answered each question individually based on vibe, without using what they learned (or figured out) from other questions.

They’re positively correlated. Presumably because people who have good plastic intelligence often end up learning more throughout their lifetime. The only way to form knowledge in our minds is by learning it.

Of course! Most people in the computing world work way higher up the ladder of abstraction. I suspect a small minority of working software engineers know what an ISA even is.

I did some contract work in web development for a time. It is staggering how few people understand how the javascript they write gets executed on the machine. People don't understand pointers, or virtual machines, or in many cases how JS bundlers work, despite using them daily.

In some ways, this is a sign that our abstraction layers have been a great success! People can program for the virtual javascript machine, without needing to understand how the actual machine works, or how it emulates javascript. Is this the future we wanted? I'm not sure. But it's here.

Why not both? Stenographically fingerprint llm output to make cheating risky. And use other approaches too.

Most people have no idea open source LLMs exist, let alone how to use them. Right now, they’re much worse than the frontier models.

Usernames are the same. Who gets @example.bsky.social ? Whoever registers it first. Problem solved.

A proper watermarking system should be able to have an arbitrarily high accuracy - as many nines as you want. And it should be able to actually report the accuracy of its judgements.

If you're worried about kids being falsely accused of cheating using LLMs, you should be cheering on these developments.

EDIT: Sorry, I misread your comment above. Yeah, hopefully orders of magnitude fewer kids than the number who are getting falsely accused of cheating with LLMs now.

Increasing the accuracy of these systems - both in terms of false positives and false negatives - seems like a good thing.

We could use this to fix some of DNS's long running problems. Like, we could add upstream cryptographic signatures to DNS records. And put TLS certificates in there too. We might be able to ditch TTL entries. In a DNS server, records should update via server-push from the firehose. You shouldn't have to wait for TTL to expire.

I think it'd make a nice little system.

Watermarking will never be a perfect tool. But there’s a lot of value in making low effort llm slop detectable. Even if high effort llm slop is still undetectable. Don’t make perfect the enemy of good.