My flow state with AI is having 5 different conversations at the same time making good progress on all of them by giving key insight and feedback at the right times.
You can actually go super fast with the right setup and focusing only on the important details like ensuring the shape of the APIs make sense and that test quality is good.
The "supported" workflow is you keep your source of truth in either the monorepo or the external repo. Then you export the current state of the source of truth to keep the mirrors up to date. Then, since we can assume the mirrors are up to date, the inverse transform can be applied to import change requests from the mirrors.
It works well when the assumptions hold, that there isn't large divergence on either side. It can actually be largely automated.
> I like being able to memorise IP addresses, it really helps testing.
This is even easier with IPv6. At work we have a bunch of test devices, and you calculate the IPv6 from the device's serial number. Simple as that, no memorization at all.
The pattern I notice more frequently at work now is:
"I'm working on X problem, I tried Y solution, AI thinks Z is wrong and W could be better, human opinion?"
This way there's never space for ambiguity, you showed you did your homework to the best of your extent, you already asked AI, all that's left is explicit request for human input.
It works quite well, and I appreciate it from both ends, as it saves everyone time.
I actually think these constraints _help_ the average project as well. By enforcing remote builds and execution you completely remove the need for something like docker. You also get cloud backups for your code automatically.
meet.hn/city/ca-Kitchener
Interests: AR/VR, DevOps, Web Development
---