Thesis: Interesting work is less amenable to the use of AI

Curious to see examples of interesting non-boilerplate work that is now possible with AI. Most examples of what I've seen are a repeat of what has been done many times (i.e. probably occurs many times in the training data), but with a small tweak, or for different applications.

And I don't mean cutting-edge research like funsearch discovering new algorithm implementations, but more like what the typical coder can now do with off-the-shelf LLM+ offerings.

Here's a couple examples: https://lucumr.pocoo.org/2025/6/21/my-first-ai-library/ https://www.indragie.com/blog/i-shipped-a-macos-app-built-en...

> Curious to see examples of interesting non-boilerplate work that is now possible with AI.

Previously discussed on HN - oAuth library at cloudflare - https://news.ycombinator.com/item?id=44159166

For a review of this library see https://neilmadden.blog/2025/06/06/a-look-at-cloudflares-ai-...

Upshot: though it's possible to attempt this with (heavily supervised) LLMs, it's not recommended.

Such a cool review! thanks for posting it. Great to see that authoritative experts are sharing their time and thoughts, lots to learn from this review. Despite the caveats mentioned by Neil, I still think this is a good example of a "non trivial / not boilerplate thing done w/ LLMs". To think we got from chatgpt's cute "looks like python" scripts 2.5 years ago to these kinds of libraries is amazing in my book.

I'd be curious to see how the same exercise would go with Neil guiding claude. There's no debating that LLMs + domain knowledge >>> vibe coding, and I would be curious to see how that would go, and how much time/effort would an expert "save" by using the latest models.