Cloudlflare builds OAuth with Claude and publishes all the prompts

I'm the author of this library! Or uhhh... the AI prompter, I guess...

I'm also the lead engineer and initial creator of the Cloudflare Workers platform.

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Plug: This library is used as part of the Workers MCP framework. MCP is a protocol that allows you to make APIs available directly to AI agents, so that you can ask the AI to do stuff and it'll call the APIs. If you want to build a remote MCP server, Workers is a great way to do it! See:

https://blog.cloudflare.com/remote-model-context-protocol-se...

https://developers.cloudflare.com/agents/guides/remote-mcp-s...

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OK, personal commentary.

As mentioned in the readme, I was a huge AI skeptic until this project. This changed my mind.

I had also long been rather afraid of the coming future where I mostly review AI-written code. As the lead engineer on Cloudflare Workers since its inception, I do a LOT of code reviews of regular old human-generated code, and it's a slog. Writing code has always been the fun part of the job for me, and so delegating that to AI did not sound like what I wanted.

But after actually trying it, I find it's quite different from reviewing human code. The biggest difference is the feedback loop is much shorter. I prompt the AI and it produces a result within seconds.

My experience is that this actually makes it feels more like I am authoring the code. It feels similarly fun to writing code by hand, except that the AI is exceptionally good at boilerplate and test-writing, which are exactly the parts I find boring. So... I actually like it.

With that said, there's definitely limits on what it can do. This OAuth library was a pretty perfect use case because it's a well-known standard implemented in a well-known language on a well-known platform, so I could pretty much just give it an API spec and it could do what a generative AI does: generate. On the other hand, I've so far found that AI is not very good at refactoring complex code. And a lot of my work on the Workers Runtime ends up being refactoring: any new feature requires a bunch of upfront refactoring to prepare the right abstractions. So I am still writing a lot of code by hand.

I do have to say though: The LLM understands code. I can't deny it. It is not a "stochastic parrot", it is not just repeating things it has seen elsewhere. It looks at the code, understands what it means, explains it to me mostly correctly, and then applies my directions to change it.

>experienced engineers using AI to generate bits of code and then meticulously reviewing and testing them

And where are supposed to get experienced engineers if replaced all Jr Devs with AI? There is a ton of benefit from drudgery of writing classes even if seems like grunt work at the time.

I would say it ended up being much faster than had I written it by hand. It took a few days to produce this library -- it would almost certainly have taken me weeks to write it myself.

It's said that much of research is data janitorial work, and from my experience that's not just limited to the machine learning space. Every research scientist wishes that they had an army of engineers to build bespoke tooling for their niche, so they could get back to trying ideas at the speed of thought rather than needing to spend a day writing utility functions for those tools and poring over tables to spot anomalies. Giving every researcher a priceless level of leverage is a tremendous social good.

Of course, we won't be able to tell the real effects, now, because every longitudinal study of researchers will now be corrupted by the ongoing evisceration of academic research in the current environment. Vibe-coding won't be a net creativity gain to a researcher affected by vibe-immigration-policy, vibe-grant-availability, and vibe-firings, for all of which the unpredictability is a punitive design goal.

Whether fear of LLMs taking jobs has contributed to a larger culture of fear and tribalism that has emboldened anti-intellectual movements worldwide, and what the attributable net effect on research and development will be... it's incredibly hard to quantify.

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