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I saw a post on here, basically they get ai to design the change and give them each each one at a time that they manually type in.

They argued this still allowed them to fully understand what has being implemented and how it fitted together.

I’ve been doing this since I read that and it also allows you to catch stupid stuff while your typing, you can reason about what each little change does and why it’s needed.

This also lets the LLM change the future of the plan if you fine something.

This turns out to save a whole bunch of time later because you already know how it works.

It’s not nearly as fast as just letting the agent do everything.

by cfeduke2 hours ago|

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I have been thinking about trying this approach. We really need something that's in-between, right? There's going to be code that is boilerplate implementation, and I don't need to drive that - like we have, in the before times, relied on macros and what not to do that work. But then the interesting logic, seeing the suggested implementation, copying or working from that. That's the sweet spot.

I've also been thinking about the partner programming craze phase our industry went through. I write the test, you write the code; well I write the test, LLM satisfies with code. (Then we have less of these weird LLM generated test cases that test _nothing_. We can also use the LLM to suggest tests to complete coverage.)

These would be slower to work this way, but the end result is:

1. humans still learn 2. you have a good grounding in how everything has been written

by 4 hours ago|

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by Schiendelman5 hours ago|

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And more than anything, it doesn't take away the trade-offs of what to build to deliver value for customers, what to make the business improve, what avoids paging you on a vacation.