I mean, surely the main motivation for "use an LLM to rewrite a huge project in a new language" was excitement about the shiny new tech that made it possible.
Does anyone understand why LLMs have gotten so good at this? Their ability to generate accurate SVG shapes seems to greatly outshine what I would expect, given their mediocre spatial understanding in other contexts.
The Claude Plays Pokemon stream with a minimal harness is a far more significant test of model intelligence compared to the Gemini Plays Pokemon stream (which automatically maintains a map of everything that has been seen on the current map) and the GPT Plays Pokemon stream (which does that AND has an extremely detailed prompt which more or less railroads the AI into not making this mistakes it wants to make). The latter two harnesses have become too easy for the latest generations of model, enough so that they're not really testing anything anymore.
Claude Plays Pokemon is currently stuck in Victory Road, doing the Sokoban puzzles which are both the last puzzles in the game and by far the most difficult for AIs to do. Opus 4.5 made it there but was completely hopeless, 4.6 made it there and is is showing some signs of maaaaaybe being eventually bruteforce through the puzzles, but personally I think it will get stuck or undo its progress, and that Claude 4.7 or 5 will be the one to actually beat the game.
Notably 45 out of the 50 days of improvement were in two specific dungeons (Silph Co and Cinnabar Mansion) where 4.5 was entirely inadequate and was looping the same mistaken ideas with only minor variation, until eventually it stumbled by chance into the solution. Until we saw how much better it did in those spots, we weren't completely sure that 4.6 was an improvement at all!
In my experience with the models (watching Claude play Pokemon), the models are similar in intelligence, but are very different in how they approach problems: Opus 4.5 hyperfocuses on completing its original plan, far more than any older or newer version of Claude. Opus 4.6 gets bored quickly and is constantly changing its approach if it doesn't get results fast. This makes it waste more time on"easy" tasks where the first approach would have worked, but faster by an order of magnitude on "hard" tasks that require trying different approaches. For this reason, it started off slower than 4.5, but ultimately got as far in 9 days as 4.5 got in 59 days.
There were no such writeups, 99% of the discussion about difficulties in Crystal were in twitch and discord chats where Google doesn't scrape. (It hadn't yet gotten the public attention that Claude and Gemini's runs of Pokemon Red and Blue have gotten.)
That said, this writeup itself will probably be scraped and influence Gemini 4.
It's hard to say for sure because Gemini 3 was only tested with this prompt. But for Gemini 2.5, which is who the prompt was originally written for, yes this does cut down on bad assumptions (a specific example: the puzzle with Farfetch'd in Ilex Forest is completely different in the DS remake of the game, and models love to hallucinate elements from the remake's puzzle if you don't emphasize the need to distinguish hypothesis from things it actually observes).
Exactly what I was going to post. Optimizations like loop unrolling slow down the N64 because keeping the code size small is the most important factor. I think even compilers of the time got this wrong, not just modern ones.
How long until we get to the point where models know that LLMs get this wrong, and that it is an LLM, and therefore answers wrong on purpose? Has this already happened?
(I doubt it has, but there ARE already cases where models know they are LLMs, and therefore make the plausible but wrong assumption that they are ChatGPT.)