zlacker

>>gregor+(OP)
The commits are revealing.

Look at this one:

> Ask Claude to remove the "backup" encryption key. Clearly it is still important to security-review Claude's code!

> prompt: I noticed you are storing a "backup" of the encryption key as `encryptionKeyJwk`. Doesn't this backup defeat the end-to-end encryption, because the key is available in the grant record without needing any token to unwrap it?

I don’t think a non-expert would even know what this means, let alone spot the issue and direct the model to fix it.

>>rienbd+s22
That is how LLM:s should be used today. An expert prompts it and checks the code. Still saves a lot of time vs typing everything from scratch. Just the other day I was working on a prototype and let claude write code for a auth flow. Everything was good until the last step where it was just sending the user id as a string with the valid token. So if you got a valid token you could just pass in any user id and become that user. Still saved me a lot of time vs doing it from scratch.

>>victor+Ng2
> Still saves a lot of time vs typing everything from scratch.

In my experience, it takes longer to debug/instruct the LLM than to write it from scratch.

>>XCSme+sx2
Depends on what you're doing. For example when you're writing something like React components and using something like Tailwind for styling, I find the speedup is close to 10X.

>>Culona+BA2
Isn't there some way to speed up with codegen besides using LLMs?

>>nijave+x73
Some may have a better answer, but I often compare with tools like OpenAPI and AsyncAPI generators where HTTP/AMQP/etc code can be generated for servers, clients and extended documentation viewers.

The trade off here would be that you must create the spec file (and customize the template files where needed) which drives the codegen, in exchange for explicit control over deterministic output. So there’s more typing but potentially less cognitive overhead with reviewing a bunch of LLM output.

For this use case I find the explicit codegen UX preferable to inspecting what the LLM decided to do with my human-language prompt, if attempting to have the LLM directly code the library/executable source (as opposed to asking it to create the generator, template or API spec).