[jameshush]

It might be role-specific. I'm a solutions engineer. A large portion of my time is spent making demos for customers. LLMs have been a game-changer for me, because not only can I spit out _more_ demos, but I can handle more edge cases in demos that people run into. E.g. for example, someone wrote in asking how to use our REST API with Python.

I KNOW a common issue people run into is they forget to handle rate limits, but I also know more JavaScript than Python and have limited time, so before I'd write:

``` # NOTE: Make sure to handle the rate limit! This is just an example. See example.com/docs/javascript/rate-limit-example for a js example doing this. ```

Unsurprisingly, more than half of customers would just ignore the comment, forget to handle the rate limit, and then write in a few months later. With Claude, I just write "Create a customer demo in Python that handles rate limits. Use example.com/docs/javascript/rate-limit-example as a reference," and it gets me 95% of the way there.

There are probably 100 other small examples like this where I had the "vibe" to know where the customer might trip over, but not the time to plug up all the little documentation example holes myself. Ideally, yes, hiring a full-time person to handle plugging up these holes would be great, but if you're resource constrained paying Anthropic for tokens is a much faster/cheaper solution in the short term.

[disgruntledphd2]

Yup, LLMs are rocking for smaller more greenfield stuff like this. As long as you can get your results in 5-10 interactions with the bot then it works really well.

They seem to fall apart (for me, at least) when the projects get larger or have multiple people working on them.

They're also super helpful for analytics projects (I'm a data person) as generally the needed context is much smaller (and because I know exactly how to approach these problems, it's that typing the code/handling API changes takes a bunch of time).