Checkpoints sounds like an interesting idea, and one I think we'll benefit from if they can make it useful.
I tried a similar(-ish) thing last year at https://github.com/imjasonh/cnotes (a Claude hook to write conversations to git notes) but ended up not getting much out of it. Making it integrated into the experience would have helped, I had a chrome extension to display it in the GitHub UI but even then just stopped using it eventually.
I thought the use of AI in the Secret Invasion title sequence was actually really appropriate, even "meta", maybe even a bit ahead of its time.
The seemingly purposeful AI style made it seem unnatural (on purpose), and like a facsimile of an otherwise trustworthy thing (on purpose), which was exactly in line with the idea of the show.
The execution of that show and that idea was pretty bad, but one of the few positives of it was, to me, an example of using AI art overtly, and leaning into its untrustworthy nature.
Early in one of the conversations Gemini actually proposed a Lisp-like language with S-expressions. I don't remember why it didn't follow that path, but I suspect it would have been happy there.
The comment was sarcastic, hence the "/s" at the end of the first sentence.
Everything else was a thought experiment to show how the idea of LLMs on everything including commercial planes is a very bad idea and would give regulators a hard time.
The point is: just because you can (build and run anything) does not mean you should (put it on commercial planes).
This was mainly an exercise in exploration with some LLMs, and I think I achieved my goal of exploring.
Like I said, if this topic is interesting to you and you'd like to explore another way to push on the problem, I highly recommend it. You may come up with better results than I did by having a better idea what you're looking for as output.
I tried a thread, I got that both LLMs and humans optimize for the same goal, working programs, and the key is verifiability. So it recommended Rust or Haskell combined with formal verification and contracts. So I think the conclusion of the post holds up - "the things that make an LLM-optimized language useful also happen to make them easier for humans!"
Yeah fwiw I agree. I was impressed at how well the agents were able to understand and write their invented language, but fundamentally they're only able to do that because they've been trained on "similar" code in many other languages.
I tried a similar(-ish) thing last year at https://github.com/imjasonh/cnotes (a Claude hook to write conversations to git notes) but ended up not getting much out of it. Making it integrated into the experience would have helped, I had a chrome extension to display it in the GitHub UI but even then just stopped using it eventually.
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