Comment by _usefulcat
7 hours ago
I'd like to submit my counterpoint. I work on an established codebase building new features and fixing bugs. It has access to our story board, git and a couple of other mcps. As long as the story is well written with clear requirements and expectations it always produces quality code that I validate as a human with a variety of tests automated and manual. I peer review the code. My colleagues then peer review that too.
I have noticed two things - new features take at a minimum at least half the time it took me previously and bugs are much less frequent. Even faster when bug fixing.
My takeaway is that you need solid requirements, clear context and thoughtful human oversight primarily during planning but also during verification
I don't think this is a counterpoint.
An established codebase is already the best kind of context you could give an agent. It has all the patterns baked in so the agent simply follows established patterns. Such a codebase probably contains tens to hundreds of thousands of man-hours poured into it by humans refining it to do what it does - taking into account real world feedback and constraints.
When working on something from scratch, the best an agent can do is the average of whatever is in its training set and the clarity of the text prompts.
> When working on something from scratch, the best an agent can do is the average of whatever is in its training set and the clarity of the text prompts.
Nah. The best it can do is to use the best writing style a model learned. Post-training might fail to prioritize it, though. Autoregressive pretraining does not average things. It creates a predictive model for variety of programming styles.