@AdamHoltererer great question!
depends so much on the performance of the model in the thread.
any data to proxy productivity? like, did the thread have a happy ending in the form a PR?
something like, number of prompts to get to PR, per model
@AdamHoltererer thanks!
yeah, i agree, this isn't showing what i was hoping it would.
i wonder if the population should be only people who have 2+ models, and to compete them against each other...
going to think about it some more ๐ค
@GergelyOrosz Waterfall and Agile were solutions to a problem that does not exist anymore: execution is expensive.
This means 2 things:
1. Ideas are expensive again
2. The trade-off between agile (ship small) and waterfall (ship big) is now zero.
Ideas are cheap. With AI, execution is cheaper.
Waterfall and Agile were solutions to a problem that does not exist anymore: execution is expensive.
Execution is now cheaper than ideas, and ideas are now expensive.
What this means for you is that testing your idea piecemeal is now bad advice. While you ship a small slice of your idea, I ship an entire product. While you collect feedback on a small feature, I collect feature on the whole idea.
In other words: the tradeoff between waterfall and agile is now zero.
Hear me out:
We hated "waterfall" because after Big Upfront Planning, implementation took FOREVER, and by the time we were done, the plan was out of date
But with AI, implementation is rapid (100x faster, or more, than before)
So Big Upfront Planning can now make sense?
both approaches are solutions to a problem that's largely solved now: execution.
Ideas were always considered cheap, relative to execution. Now, execution is cheaper.
Logically, ideas are now expensive, and entire ideas, not execution-capped slices, can be iterated on quickly.
Plan big!