Agreed — I also always append to my agents.md — "If there are any technical choices available, please surface them to me so I can understand them and choose"

> Whst is this bit of code doing?

This only applies to bad code. A formal programmic language beats an informal language anytime. Why on earth would you translate an unambiguous formal language to English?

> Is there a risk of data leaking here?

> Are permissions enforced downstream of this function?

If you trust AI to give you that answer, you are going to run into serious issues.

Do you need that analysis every time from an LLM? Those are things I’d expect anyone to grow out of pretty quick once you get used to understanding code. Especially someone with Senior in their title.

> LLMs are great with focused questions, up and down abstraction layers and across all kinds of concerns. Stack up these focused concerns into a rich understanding of what you are doing or writing.

I agree. This is much better advice than what the article tries to do, which is to give best practices around how to use or not use AI to code.

Doing this may give you understanding but won’t really teach you how to understand. For students (as the article is focusing on them), the latter is equally important.

> This misses the best use of AI in my opinion, which is to gain understanding. Whst is this bit of code doing? Is there a risk of data leaking here? Are permissions enforced downstream of this function?

Do I understand when I just let AI regurgitate the function and behavior of the code?

Closely related: one of the biggest AI superpowers I've found is testing. "Please implement a comprehensive unit testing suite for this, making sure to test ..."

You still have to check the work and shouldn't trust it blindly, but in my experience it's pretty damn good.

Wish I could upvote twice or something

Exactly, i so wish the narratives around LLMs in most fields were around how they can enable us to be better ourselves...

SOMEONE didn't read the article

> You code and you have the AI check your work. Treat it like a reviewer. That’s my advice in a sentence.

Bro you're giving the same advice as the article

If the challenge is held constant, giving people tools is just teaching a different skill set than teaching them unaugmented. The augmented skill set is more relevant to how people do work in the real world, but working through things unaugmented likely help people build a robust internal schema more quickly. We definitely don't need to spend as much time training unaugmented abilities going forward though, kind of like how the number of assembly related classes in CS departments has gone down over time as we've moved up the stack.

Exactly. I always think about the ways consumers make terrible decisions with everyday purchases at the grocery store etc. If people could easily estimate unit costs in their head for example, a lot of predatory options would have to go away. But calculators keep people from having to get good with everyday math in their head, while those same calculators sit unused in many everyday scenarios where people don't go to the effort to use them.

“You can use the electronic tuner once you learn to tune by ear” - any decent guitar teacher

I don't know if this was universal, but when I was a student we weren't allowed calculators until I believe 10th grade

You learn stuff by heart, so that when you mess up with the calculator you have a feel that stuff has gone wrong

It should depend on whether you've taken other steps to constrain the output of the LLM. I've been building some pretty elaborate test harnesses (which I test independently beforehand), to ensure that I get something that does what I ask of it.

I don't care which words we use, but they should separate this sort of empirically-minded work (versus the rationalism of typical development) from just dropping a few sentences into the prompt and crossing your fingers.

Everything old is new again. The reason natural language never caught on for programming, is that natural language is inherently unsuitable for the task of formulating precise and unambiguous instructions for a machine.

Numerous attempts have been made in past 70 years, but we landed on the compromise that is high level programming languages with their (mostly) well-defined syntax and semantic rules.

If you look outside of the world of programming you'll find that the same is true for every craft and profession that requires precision and unambiguity. Cooking, logistics, military, carpentry, mechanical engineering, law, medicine, etc. all have their own domain specific languages for describing tasks, procedures, and tools. Heck, even sufficiently large organisations have their own internal languages and terms for that reason.

Developing coding skills is both easier and more difficult than ever at the same time.

50 years ago the main barrier was access to hardware - computers were big, expensive, and not easy to come by for average people.

30 years ago, the biggest hurdle was access to resources: you had to spend a fortune on books that quickly became obsolete or had to wait for your local library to get them for you.

Today, you basically have everything - up-to-date free reference material on pretty much everything in an instant, cheap and accessible hardware (no need for high-end stuff), great online tutorials and interactive courses, message boards, the works.

But then you also have a machine that completely removes the need to put in the effort required to actually learn and hone your skills. Human psychology always seeks shortcuts and more discipline than ever is needed to not give up and have the machine do the thinking for you instead.

Proficient developers are getting pressured into outsourcing their craft and skills to the machine, thus deskilling quickly. Juniors never get the chance to become proficient in the first place - either because they're not getting hired to begin with, or because they succumb to the siren song of the machine...