Comment by paulmooreparks
2 hours ago
I don't know. I find that I'm moving up a level and improving my product-management skills while delegating most of the code to the agents. I'm still very much hands-on with the design and requirements, and I'm asking questions like, "What's our security story for XYZ?", "Are we accounting for colour-blindness?", etc. Not being down in the code allows me to prairie-dog a bit more and see the landscape better.
One thing I've noticed is that LLMs have allowed middle managers trapped inside the role of a developer to finally self actualise.
chef's kiss
I'm about 50% that way. However when the AI is done coding I then step back and review to find places the code quality is unacceptable. I also have to stop the AI once in a while because it forgets the point and does something stupid. Junior engineer learn, AI does not.
I don't abandon the code to the agent entirely. I have my own... I wouldn't call it a harness as such, but rather a shared Kanban board, and it'll be the subject of a "Show HN" soon. It suffices to say that I define Kanban cards for each feature or bug, and I have clearly defined review points for each card, post-spec and post-code, where I step in. On top of that, after my review, there is an agentic review, and agents can and do catch things that I missed. The quality of the software has improved quite a bit since I instituted that flow.
> Junior engineer learn, AI does not.
This is technically true, but lets not act like we haven't seen immense improvement of both models are harnesses for these models in the past years. They may not be learning, but they are getting better
They are getting better at historical data, not at the fundamental issue.
As a recent example, I recently had to abandon the multiple LLM reviewer/verifier model I was using because zig 0.16 was released with major changes.
I actually reverted back to full self hosted because the foundation models we’re trying too hard to revert to the older versions of the language.
It is going to be a balancing act and there is fundamentally no way for LLMs to get around this.
We will have to develop methods to do so, most likely by focusing agents on problems that are more static.
2 replies →
Unless you log its mistakes and how they were solved in decisions.log
You might want to think about how that technique scales as your decisions.log accumulates more and more guidance.
Or at least maybe ask Claude what will happen when the md and log files that keep it on task start to dominate and someday even overflow its context windows.
Using AI doesn't protect you from thinking about resource constraints and algorithm tradeoffs like any other engineer might. It's just that the resource constraints and algorithms tradeoffs that you need to engineer around become those of the AI tooling rather than the project its generating.
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