Working with AI centric IDE on the mature codebase needs different skill set that I suspect related to people management. As the pattern would be describing a problem well, making a good plan, delegate and giving feedback.
On the other side, getting a good flow is not trivial. I had to tweak rules, how to describe problem, how to plan the work and how to ask the agent. It takes time to be productive.
Eg. Asking agent to create a script to do string manipulation is better than asking them to do inplace edit. As it's easier to debug and repeat.
Not really my case. Found that Codebase with good code benefit more with LLM, but it's not the prerequisite.
I just rewrote 300ish advanced PostgreSQL queries to mysql queries. The process is not magical, but it will take me 1 week rather than 3 days. Now I'm on testing phase, seems promising.
The point is if we can find a work to work along with the agent, can be very productive.
> I just rewrote 300ish advanced PostgreSQL queries to mysql queries.
Translation from one set of tokens to another is exactly the primary use case of LLMs. This is exactly what it should be good at. Developing new queries, much less so. Translation from one set of database queries to another was already very well defined and well covered before LLMs came about.
Also, both are formal grammar so if you really wanted to create a 1:1 translator, it's possible to do so (see virtual machines). But it's not as useful per se as no one really switch databases on a whim. If you really want to do so, you want to do it correctly.
I mean, using a good ORM with DB adapter options could achieve this in minutes. Sure, LLM has utility here for raw queries, hardly "replace SWEs" type of utility though
