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The issue is that in order to have the agent write good code, you need to implement standard SWE best practices. But that also means a lot of manual interventio
by this_user 8d ago
The issue is that in order to have the agent write good code, you need to implement standard SWE best practices. But that also means a lot of manual intervention in terms of writing specs, checking acceptance criteria, and reviewing code. So you end up spending a lot of time on managing your agent, which means you won't get a 1000% productivity gain, you get maybe 50 or 100, possible less in some areas and with some issues.
- beezlewax 8d ago50 or 100 seems unlikely. Even with all these improvements, custom setups and guardrails it just isn't that much faster for me.
- user43928 8d agoA 1000% productivity gain is quite possible on solo greenfield projects. At work, with a team and code reviews, the 50%-100% figure seems much more likely. This can probably move towards the more spectacular productivity gains as the AI's output becomes more reliable, people realize this, and less time is spend on code review and cleaning up the output.
- lolakutty 8d ago> implement standard SWE best practices The thing is, if you follow SWE best practices indiscriminately, then you ll have a shit code base in no time. There is no silver bullet, and no replacement for experience and mindfulness.
- bigstrat2003 7d agoYou get 0% productivity gains if you are careful and actually reviewing the code the LLM produces. The only way to actually get the massive productivity gains that AI bros claim is to throw quality out the window.