It implies that the agents could only do this because they could regurgitate previous browsers from their training data.
Anyone who's watched a coding agent work will see why that's unlikely to be what's happening. If that's all they were doing, why did it take three days and thousands of changes and tool calls to get to a working result?
I also know that AI labs treat regurgitation of training data as a bug and invest a lot of effort into making it unlikely to happen.
I recommend avoiding the temptation to look at things like this and say "yeah, that's not impressive, it saw that in the training data already". It's not a useful mental model to hold.
But yes, with enough prodding they will eventually build you something that's been built before. Don't see why that's particularly impressive. It's in the training data.
But if even the AI agent seems to struggle, you may be doing something unprecedented.
They're equally useful for novel tasks because they don't work by copying large scale patterns from their training data - the recent models can break down virtually any programming task to a bunch of functions and components and cobble together working code.
If you can clearly define the task, they can work towards a solution with you.
The main benefit of concepts already in the training data is that it lets you slack off on clearly defining the task. At that point it's not the model "cheating", it's you.
I'd find it very interesting to see some compelling examples along those line.
That transcript viewer itself is a pretty fun novel piece of software, see https://github.com/simonw/claude-code-transcripts
Denobox https://github.com/simonw/denobox is another recent agent project which I consider novel: https://orphanhost.github.io/?simonw/denobox/transcripts/ses...