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Alright, AI bros, follow-up from last week. I was able to secure access to Claude Opus 4.6 at my job, and I gave it the same prompt that I had given to Sonnet. It overlooked the authentication part of the HTTP client library completely this time in what it generated. In a follow-up I asked it to extract out the common logic for the authentication portions specifically. It didn't do that, instead it generated a class with two helper methods.
The first helper method was just a thin wrapper around System.Text.Json for deserializing the response. There's an optional flag to pass in for when case insensitive deserialization is needed, and nothing else.
The second helper method was something for actually making the HTTP calls. The strangest part with this one is that it has two delegates as parameters, one for deserializing successful responses, the other for handling (but not deserializing) error responses. It didn't do anything to split out handling of the 2 different ways to authenticate at all.
The issues with what was generated (for both the API client as a whole, and for the authentication part of the code specifically) are numerous, here are a small handful that I identified:
It assumes that an HTTP 200 code is the only successful response code, even though some endpoints return 202, 207, and more.
It assumes that all endpoints return plaintext or JSON content, even though several return binary data, CSV data, etc.
It didn't do null checking in several places. I assume it was mostly trained on C# code that either didn't do null checks correctly, and/or on code that doesn't use the nullable reference type feature that was added in C# 8 (back in 2019). Regardless, the null checks are missing/wrong regardless of whether nullable reference types are enabled or disabled. Also it always checks nulls with == or != null. This works 99% of the time, but best practice is to use "is null" and "is not null" for the rare cases where the equality operator is overloaded. Once again, I assume this is because most of the training data uses == and !=.
It doesn't handle url query parameters (nor path parameters), it assumes everything is going to use a JSON body for the request.
It uses the wrong logging templates for several of the logging calls. For example, the logs for an error response use the log template for logging the requests that are sent. Even more troubling is that it removed all the logic for stripping user secrets out of these logs.
There are quite a few more issues, but overall my experience with Opus was even worse than my experience with Sonnet, if anything. AI bros still in shambles. I definitely have zero fears that AI will replace me, though I'm still definitely fearful that retarded C-suite execs will think it can replace me.
My post from last week about using Claude Sonnet: https://www.themotte.org/post/3654/culture-war-roundup-for-the-week/426666?context=8#context
Edit: Just saw a very relevant post over on Orange Reddit about this very topic: https://news.ycombinator.com/item?id=47660925
Yeah, personally, I've never bought into the AI hype at all. Everything I've ever tried to use it for, it promptly shits the bed on, so I just dismiss it as worthless.
But even in an alternate universe where I'm the crazy one and everyone else is sane, there are severe problems with trusting this stuff: first, you're de facto ceding control over your technical infrastructure to a third party (run by exactly the sort of people who say stuff like "idk, they trust me. dumb fucks"). Yes, yes, you're supposed to religiously check the output before committing, not let it execute unsafe commands in a privileged environment, yada yada. I've got a bridge in Brooklyn to sell ya. Second, there is existing precedent for tech services being intentionally made worse to increase usage: for example, Google intentionally made Google search worse by doing things like disabling spell check so that users would have to search multiple times to find the result they were looking for, thus "increasing usage" (yes, this is from an actual court document lol). As OP and plenty of other smart people have noted, there is is a trivially obvious incentive and mechanism for this to be done with LLM coding agents. Just make the agent worse so people have to use more tokens!
If they're going to enshittify the AIs, it'll have to happen after one company gets sufficient market dominance that swapping to a different one isn't trivial.
And we're not at that point yet. If anything, the competition is at its fiercest right now. People seem to be willing to drop one product for another if they notice any tiny loss of performance.
I've mostly stuck it out with GPT so far, but I can't see any way they could lock me in hard enough that I wouldn't leave if it was obvious their model was consistently 10% 'stupider' than the alternatives.
Microsoft already had Copilot start inserting ads into pull requests without consent a week or two ago, I think that counts as enshittification.
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