[salomonk_mur]

I had it One-shot the full architecture for a fairly advanced distributed system for a client. It then one shot the actual code design (following absolutely all our our internal requirements on auth, stack to use, security, code styling, documentation, etc). It then one shot (and we code reviewed everything thoroughly) each of the 5 micro services needed.

It one shot the infrastructure to use and created the terraform file to put it up anywhere. It deployed it.

It caught some of the errors it had made by itself after load-testing, and corrected them. It created the load test itself (following patterns from previews projects we had).

It did all of this in a week. With human supervision on each step, but in a fucking week. We gave it all the context it needed and one-shotted everything.

It is more than god-level. If you are not getting these increases in productivity, you are using it wrong.

[exe34]

Hey would you be willing to share your claude.md? I'm only starting out with AI coders, and while it often makes good choices for straightforward things, I find the token usage gets bigger and bigger as it proceeds down a list of requirements - my working hypothesis is that it's having to re-read everything as the project gets more complicated and doesn't have a concept of "this is where I go to kick it for this kind of thing".

[ltbarcly3]

Lol ok dude, good luck with your 'I just resell the output of Claude and I can't tell when it makes mistakes' business model. I'm sure it is a long term valid economic niche.

[salomonk_mur]

Before, I resold the output of engineers.

Now, I resell the output of AI supervised by engineers.

We can tell when it makes mistakes. It used to make a ton. Now, with the right context, it really makes very few mistakes (which it can find itself and fix itself)