It actually gives me quite an uncanny feeling, bulldozing over years of human optimization work with a newer, "perfect" design. Like bringing an AK-47 back to the middle ages.
it's so hard to square such totalizing statements with my day to day experience with fable and sol, (every possible, improvement, really?? they are NOT omniscient) arguing with them/my colleagues' agents that no they have slowed down the system 200x with their terrible change, doing string operations on millions of db rows, trying to get it to understand that I don't care that it's calling it a "cache" if a cache hit is slower than what we had before.
These agents do let you learn codebases quickly, and produce code way faster. I don't look at IDEs all that often. But literally multiple times every single day I catch them doing something stupid.
I don't think its impossible that we could get better performance from the agents. I know ive tried all sorts of workflows and skills, few of which seem to have much effect on the things the models struggle with. I think a big part of it is encoding enough context for large codebases, and providing it with all the tools it needs to make it successful, things to automatically check its work, etc. But that's not automatic, in fact its generally a terrible judge of what it needs or what its bad at
but the revolution is it doesn't take that long. in like 15 minutes you can chat with fable and get to the meat of whatever the issue is with repeated questioning. and then it does the solution for you. so it's not magic but it's still like a 100x speedup.
Opus and I wrote a parallelized test harness and labeled groundtruth in around 2 hours.
In 2022 that would've likely been all I did for a couple sprints
That sense that you did something better in a few days than you would have in a month 5 years ago. It's like buying a table saw for wood working.
One crazy thing I think about often is how there are so many correctness and testing harnesses that would have taken weeks to build in the past so we simply never would have. We'd just do our best then wait and see what comes to the surface. This is a huge part of what makes it possible to actually make better software with LLMs in my opinion. It isn't just 'LLM codes better than I ever could' (that's often untrue still) but 'LLM enables me to make assertions about the program to degrees that would have been absurdly impractical in the past'. It's huge
In this case the design was also AI generated, and there were limited wins to be found because the design was already superb.