[mithametacs]

You still use type systems, tests, and code review.

For a lot of use cases it's powerful.

If you ask it to build out a brand new system with a complex algorithm or to perform a more complex refactoring, it'll be more work correcting it than doing it yourself.

But that malformed JSON document with the weird missing quotation marks (so the usual formatters break), and spaces before commas, and the indentation is wild... Give it to an LLM.

Or when you're writing content impls for a game based on a list of text descriptions, copy the text into a block comment. Then impl 1 example. Then just sit back and press tab and watch your profits.

[girvo]

The (mostly useless boilerplate “I’m basically just testing my mocks”) tests are being written by AI too these days.

Which is mildly annoying as a lot of those tests are basically just noise rather than useful tools. Humans have the same problem, but current models are especially prone to it from what I’ve observed

And not enough devs are babysitting the AI to make sure the test cases are useful, even if they’re doing so for the original code it produced

[chillfox]

There are very few tutorials on how to do testing and I don't think I have ever seen one that was great. Compared to general coding stuff where there's great tutorials available for all the most common things.

So I think quality testing is just not in the training data at anywhere close to the quantity needed.

[fullstackchris]

Testing well is both an art and a science, and I mean, just look at the dev community on the topic, some are religious about TDD, some say unit tests only, some say the whole range to e2e etc. etc. hard to have good training data when there is no definition of what is "right" in the first place!