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I can't believe people are still writing comments like this lol how can it be

by chis 1y ago

zzzeek 1y ago
I think it's a serious question because something really big is being missed here. There seem to be very different types of developers out there and/or working on very different kinds of codebases. Hypothetically, maybe you have devs or specific contexts where the dev can just write the code really fast where having to explain it to a bot is more time consuming, vs. devs /contexts where lots of googling and guessing goes on and it's easier to get the AI to just show you how to do it. I'm actually employer mandated to continue to try/use AI bots / agents to help with coding tasks. I'm sort of getting them to help me but I'm still really not being blown away and still tending to prefer not to bother with them with things I'm frequently iterating on, they are more useful when I have to learn some totally new platform/API. Why is that? do we think there's something wrong with me?
- vineyardmike 1y ago
  > I'm actually employer mandated to continue to try/use AI bots / agents to help with coding tasks I think a lot of this comes down to the context management. I've found that these tools work worse at my current employer than my prior one. And I think the reason is context - my prior employer was a startup, where we relied on open source libraries and the code was smaller, following public best practices regarding code structure in Golang and python. My current employer is much bigger, with a massive monorepo of custom written/forked libraries. The agents are trained on lots of open source code, so popular programming languages/libraries tend to be really well represented, while big internal libraries are a struggle. Similarly smaller repositories tend to work better than bigger ones, because there is less searching to figure out where something is implemented. I've been trying some coding agents with my current job, and they spend a lot more time searching through libraries looking to understand how to implement or use something if it relies on an internal library. I think a lot of these struggles and differences are also present with people, but we tend to discount this struggle because people are generally good at reasoning. Of course, we also learn from each task, so we improve over time, unlike a static model.

I can't believe people are still writing comments like this lol how can it be

by chis 1y ago

I can't believe people are still writing comments like this lol how can it be

I think it's a serious question because something really big is being missed here. There seem to be very different types of developers out there and/or working on very different kinds of codebases. Hypothetically, maybe you have devs or specific contexts where the dev can just write the code really fast where having to explain it to a bot is more time consuming, vs. devs /contexts where lots of googling and guessing goes on and it's easier to get the AI to just show you how to do it. I'm actually employer mandated to continue to try/use AI bots / agents to help with coding tasks. I'm sort of getting them to help me but I'm still really not being blown away and still tending to prefer not to bother with them with things I'm frequently iterating on, they are more useful when I have to learn some totally new platform/API. Why is that? do we think there's something wrong with me?

vineyardmike 1y ago
> I'm actually employer mandated to continue to try/use AI bots / agents to help with coding tasks I think a lot of this comes down to the context management. I've found that these tools work worse at my current employer than my prior one. And I think the reason is context - my prior employer was a startup, where we relied on open source libraries and the code was smaller, following public best practices regarding code structure in Golang and python. My current employer is much bigger, with a massive monorepo of custom written/forked libraries. The agents are trained on lots of open source code, so popular programming languages/libraries tend to be really well represented, while big internal libraries are a struggle. Similarly smaller repositories tend to work better than bigger ones, because there is less searching to figure out where something is implemented. I've been trying some coding agents with my current job, and they spend a lot more time searching through libraries looking to understand how to implement or use something if it relies on an internal library. I think a lot of these struggles and differences are also present with people, but we tend to discount this struggle because people are generally good at reasoning. Of course, we also learn from each task, so we improve over time, unlike a static model.