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Comment by nullc | Hacker News Reader

It works much better when you already know what you're doing-- so you can ask for exactly the right thing, spot the LLMs errors, and know what you need to do to fix them rather than iterating forever with the hallucinating machine.

I can't help but feel that while these tools speed everyone up they actually increase the difference between an expert and a novice rather than leveling the playing field.

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Steve Yegge has laid this thesis out: https://sourcegraph.com/blog/the-death-of-the-junior-develop...

He also has some good content out there on how to program using LLM's. He calls it Chat Oriented Programming, aka CHOP. That's a good term to Google for.

I enjoyed the examples.

My previous company's lawyers really had success writing complex contracts in LLMs.

The writer one really hits home: a friend who's a writer is able to make amazing stuff with ChatGPT, because he knows how to do it himself. I can only make stupid shit, because I while can recognise quality writing, I don't really know why.

Similarly, I see the same thing happening with junior devs: a lot of them just can't get the answers I get out of Copilot or ChatGPT. I have to sit next to them and tell them what to ask the LLM, or when to say no to a proposed solution.

Similarly, I can get started myself on Computer Vision and Machine Learning projects with LLMs, but I need to hand over to a proper CV or ML Engineer to get the last 10% done.

> Similarly, I can get started myself on Computer Vision and Machine Learning projects with LLMs, but I need to hand over to a proper CV or ML Engineer to get the last 10% done.

Unless by "Computer Vision and Machine Learning" projects, you mean you are trying to develop new approaches to computer vision or ML, I don't see why you would need to involve a CV/ML engineer? The stuff is all generally packaged as re-usable libraries like everything else, no?

I'm definitely doing new stuff that's not available on ready made libraries or as paid APIs.

Exactly. These tools test whether we're brilliant managers, understanding the potential and limitations of everyone we manage, and understanding the work well enough to do it ourselves.

I too am using AI to play with new languages and problem domains, and to write tools I've long wanted but never found time to implement. I find it helpful to ask other LLMs about best practices, with no conflict of interest where they're trying to write me code.

For my initial experiences with Windsurf Editor, the AI got ahead of me and then into trouble it couldn't resolve, thrashing in ways my daughter learned to avoid in her first semester of programming. I've found Claude 3.5 Sonnet understands the problems I work on best, so I'll use Cursor for access to this AI. Workflow enhancements are secondary to the underlying intelligence.

I think I agree. It might take me 30-60 min to look at a Figma mock and transfer a form with several different HTML elements then wire them up to an API service on the front-end. I can do that now in 5 minutes by simply having AI look at the mock and generate the code for me, where I spot the errors right away due to my level of experience. It looks exactly as professional code should look from a senior+ engineer. I've got things honed down now to where it maybe takes 2 iterations to get exactly what I'm asking the LLM to do, but I'm often getting it on the first prompt now or maybe making a couple of one line changes in manual tweaks.

I think junior devs could easily get lost in the output if they're not familiar with what it's doing and why it's doing it.

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