zlacker

I've been doing AI-assisted coding for several months now, and have found a good balance that works for me. I'm working in Typescript and React, neither of which I know particularly well (although I know ES6 very well). In most cases, AI is excellent at tasks which involve writing quasi-custom boilerplate (eg. tests which require a lot of mocking), and at answering questions of how I should do _X_ in TS/React. For the latter, those are undoubtedly questions I could eventually find the answers on Stack Overflow and deduce how to apply those answers to my specific context -- but it's orders of magnitude faster to get the AI to do that for me.

Where the AI fails is in doing anything which requires having a model of the world. I'm writing a simulator which involves agents moving through an environment. A small change in agent behaviour may take many steps of the simulator to produce consequential effects, and thinking through how that happens -- or the reverse: reasoning about the possible upstream causes of some emergent macroscopic behaviour -- requires a mental model of the simulation process, and AI absolutely does _not_ have that. It doesn't know that it doesn't have that, and will therefore hallucinate wildly as it grasps at an answer. Sometimes those hallucinations will even hit the mark. But on the whole, if a mental model is required to arrive at the answer, AI wastes more time than it saves.

>>nkoren+(OP)
> AI is excellent at tasks which involve writing quasi-custom boilerplate (eg. tests which require a lot of mocking)

I wonder if anyone has compared how well the AI auto-generating approach works compared to meta programming approaches (like Lisp macros) meant to address the same kind of issues with repetitive code.

>>jimbok+rE
The generation of volumes of boiler plate takes effort; nobody likes to do it.

The problem is, that phase is not the full life cycle of the boiler plate.

You have to live with it afterward.