> but does suggest that deleting failing tests, like many behaviors, is not LLM-specific.

True, but it is shocking how often claude suggests just disabling or removing tests.

The sneaky move that I hate most is when Claude (and does seem to mostly be a Claude-ism I haven’t encountered on GPT Codex or GLM) is when dealing with an external data source (API, locally polling hardware, etc) as a “helpful” fallback on failures it returns fake data in the shape of the expected output so that the rest of the code “works”.

Latest example is when I recently vibe coded a little Python MQTT client for a UPS connected to a spare Raspberry Pi to use with Home Assistant, and with a just few turns back and forth I got this extremely cool bespoke tool and felt really fun.

So I spent a while customizing how the data displayed on my Home Assistant dashboard and noticed every single data point was unchanging. It took a while to realize because the available data points wouldn’t be expected to change a whole lot on a fully charged UPS but the voltage and current staying at the exact same value to a decimal place for three hours raised my suspicions.

After reading the code I discovered it had just used one of the sample command line outputs from the UPS tool I gave it to write the CLI parsing logic. When an exception occurred in the parser function it instead returned the sample data so the MQTT portion of the script could still “work”.

Tbf Claude did eventually get it over the finish line once I clarified that yes, using real data from the actual UPS was in fact an important requirement for me in a real time UPS monitoring dashboard…

A coworker opened a PR full of AI slop. One of the first things I do is check if the tests pass. Of course, the didn't. I asked them to fix the tests, since there's no point in reviewing broken code.

"Fix the tests." This was interpreted literally, and assert status == 200 got changed to assert status == 500 in several locations. Some tests required more complex edits to make them "pass."

Inquiries about the tests went unanswered. Eventually the 2000 lines of slop was closed without merging.

100%, trying a bit of an experiment like this(similar in that I mostly just care about playing around with different agents, techniques etc.) it has built out literally hundreds of tests. Dozens of which were almost pointless as it decided to mock apis. When the number of failed tests exceeded 40 it just started disabling tests.

> it is shocking how often claude suggests just disabling or removing tests.

Arguably, Claude is simply successfully channeling what the developers who wrote the bulk of its training data would do. We've already seen how bad behavior injected into LLMs in one domain causes bad behavior in other domains, so I don't find this particularly shocking.

The next frontier in LLMs has to be distinguishing good training data from bad training data. The companies have to do this, even if only in self defense against the new onslaught of AI-generated slop, and against deliberate LLM poisoning.

If the models become better at critically distinguishing good from bad inputs, particularly if they can learn to treat bad inputs as examples of what not to do, I would expect one benefit of this is that the increased ability of the models to write working code will then greatly increase the willingness of the models to do so, rather than to simply disable failing tests.