They most certainly do, but on a mid-higher level. They don't probably know every single motion and definitely lack hands-on experience. But they know enough to be able to manage them efficiently.
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GitHub: maciej-trebacz
They most certainly do, but on a mid-higher level. They don't probably know every single motion and definitely lack hands-on experience. But they know enough to be able to manage them efficiently.
Try it - google your name, name of your company or "how to do X in software Y" and you'll see for yourself.
I personally don't mind AI generated content when it's properly reviewed, but unfortunately more often than not the author just glances at the result and decides it's good enough.
Example: https://opencv.org/wp-content/uploads/2026/06/image-1.jpeg
I'm not knowledgable enough to determine whether this diagram is 100% accurate, but some things look off - the arrows in the bottom left seem superficial, some arrows are connected in weird ways, the mini diagram in AttentionLayer block doesn't look right (it has two Softmax icons and one MatMul icon, while the "before" diagram is the opposite).
You are assuming that the additional speed comes at a cost of codebase comprehension. For me it's not the case - I never push generated code I don't fully understand. It does take time, sure, but it still takes me much less time to write a spec, execute with AI and then review than write the thing myself.
It happened to me personally - LLMs and agentic coding tools enabled me to pick up old side projects and actually finish them. Some of these projects were in the drawer for years, and when Sonnet 4 released I gave them another try and got up to speed really quickly. I suspect this happened to many developers.
This was the case for me a year ago. Now Claude or Codex are routinely delivering finished & tested complete features in my projects. I move much, much faster than before and I don’t have an elaborate setup - just a single CLAUDE.md file with some basic information about the project and that’s it.
In reality while project does indeed have Servo in its dependencies it only uses it for HTML tokenization, CSS selector matching and some low level structures. Javascript parsing and execution, DOM implementation & Layout engine was written from scratch with only one exception - Flexbox and Grid layouts are implemented using Taffy - a Rust layout library.
So while “from scratch” is debatable it is still immensely impressive to be that AI was able to produce something that even just “kinda works” at this scale.
In reality this project does indeed implement a functioning custom JS Engine, Layout engine, painting etc. It does borrow the CSS selectors package from Servo but that’s about it.
when ({ status: s if s >= 500 }) -> throw new Error(’Server Error’)
Is it only me or this doesn’t look like JavaScript anymore?I think you meant "until 1989"