The senior should also start using AI to increase the amount of work done to stabilise the system, in a careful manner. More benchmarks, better testing, better safety net when delivering software, automated security reviews, better instrumentation, and so on.
> And this is how AI affects the two loops
There should be another image illustrating that the amount of mitigations done from senior side, red-/blue-team style.
I have the same experience. Maybe I’ve used the same amount of time getting the rewrite out, but the amount of quality checking have increased for me. Before, I would probably not bother to create end to end tests and benchmarks, but now the mental cost for being extra vigilant is so cheap.
My rewrite is running stable in production for two weeks with 50x speedup, which have made the doomed old solution viable again.
Wonder what this will mean for future legacy projects and how how we should structure the programs to be inside “rewrite with llm”-size? Maybe a renessanse for microservices?
Looks very interesting. Especially like that language environment is abstracted away, through cli, such that one are not stuck with for example python to write your UI logic (or create your own cli wrapper around PyAutoGUI).
How can one help with implementing Linux and Windows support?
Can you elaborate on this? I’m facing a similar decision in my org and think sharing a common database store sounds smart. With rules of course, like clear ownership of data, only one writer, etc.
You do not need to solve that at the language level. A common pattern is using multiple replicas, service discovery and automatic restart upon a dead replica. For example kubernetes does this out of the box.
For dark corners of the code, that is often a good middle ground between interrupting the service and never die.
Can you find other examples for when force pushing a shared branch is acceptable? I have trouble finding plausible examples for communicating a force push with 50 colleagues.
If you use helm charts or similar, the version of the image would be a variable that is updated outside the repo. ArgoCD can do this with the Application resource.
For example: build code and image, update git reference and version number in Application when build done.
You then get atomic updates of both code and configuration.
The senior should also start using AI to increase the amount of work done to stabilise the system, in a careful manner. More benchmarks, better testing, better safety net when delivering software, automated security reviews, better instrumentation, and so on.
> And this is how AI affects the two loops
There should be another image illustrating that the amount of mitigations done from senior side, red-/blue-team style.