Edit: This is the benchmark. But if you suspect it's wrong it's always fun to run your own evals. https://martinalderson.com/posts/which-web-frameworks-are-mo...
Like how half of PHP's problem for a while was w3schools.
Then React turned that confidently idiotic folklore into an ecosystem: every confident answer depends on which year it was posted, which version it assumes, and which of six abandoned libraries it recommends. The LLM blends them into a seventh approach that never existed.
For now, I suspect Svelte's shorter, cleaner history with fewer wrong paths taken and retreated from will mean there are fewer incoherent competing approaches for LLMs to blend together into confident hallucinations.
Perhaps less contradictory training data is better than more incoherent training data.
LLMs may not experience the mind-expanding joy that humans do from using Svelte or the mind-numbing frustration that humans do from using React, but they can inherit the coherent focus of Svelte and incoherent confusion of React without sharing Svelte's pleasure or React's pain.
It's like Rust. Way more Python than Rust in the training data, but with LLMs you're much better off defaulting to a Rust backend and Svelte frontend than any other combination. I'd say "unless you have a specific reason", but besides legacy requirements, there is actually no reason.
I have backend bugs that I need to manage myself, usually from unknown-unknowns that I don’t think I can easily avoid, but I never have frontend bugs (since Opus 4.6 anyway).
In the "Unix Haters Handbook", "X-Windows Disaster" chapter, "Ice Cube: The Lethal Weapon" section, I quoted Jamie Zawinski, whose timeless and tactile observation also applies to React:
> Using these toolkits is like trying to make a bookshelf out of mashed potatoes.
https://news.ycombinator.com/item?id=35631889
https://donhopkins.medium.com/the-x-windows-disaster-128d398...
At this point I don’t think it’s about training examples, once there is sufficient training data it becomes a problem of verifiability, ie how easily can the model check its work for success.