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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
I work for <large software company> that provides me access to Claude Opus 4.6. My work is primarily in a several-million-line-of-code legacy service written in C#. I use Claude itself generally via the GHCP CLI. I broadly agree with the analogy that it is comparable to having a junior dev or intern to hand to actually write changes you need done. My best results at one-shotting a change are when I give Claude a change that's probably in the few dozen up to 100 line of code scope. Beyond that, it can be hit or miss as to whether Claude itself will successfully break up a change into smaller chunks or whether I need to do that preemptively. Probably the biggest benefit Claude enables is improving parallelism. I spend much less time actually typing code and more time getting other things done. If all I did was sit and stare at Claude doing changes I could otherwise be doing, I would probably be less productive by using it but I largely don't have to be. I can tell Claude to make a change and then go do something else, come back and iterate, rinse and repeat.
For now, it seems like a very useful tool but I am not particularly worried about it taking my job. I also have not used it in a context where I am exposed to how much it costs so it's unclear to me if the ROI is there either. I will say that I often have better results when I do things like give it more context, especially specific in-repo examples, and the different harnesses I've used (VSCode integration, Cline, CLI) do seem to make a substantial difference to its output.
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