Tell HN: AI tools are making me lose interest in CS fundamentals

> For those who have been in the industry longer, why do you think it’s still important to stay strong in CS fundamentals?

Software abstractions have always made it easier to build software without having to understand the inner workings of the mechanism that has been abstracted. But that doesn't mean we don't have to understand how it works.

I saw this happen frequently with cloud infrastructure. Cloud services remove the physical and organizational barriers associated with building and scaling infrastructure. But you still have to understand networking to manage your network infrastructure in the cloud. When it doesn't work, you can talk to AWS Support, but they are there to support AWS services, not design your network for you.

The Java Streams API makes it easy to write fluent code. But the Streams API doesn't make that code efficient for you. You still have to think about the cost of each step in the fluent chain, and the optimal order. Does it make sense to sort and then filter, or filter and then sort? Think about it.

I was working on a Chrome Extension with Claude. The extension kept getting rate limited immediately on one site. Claude suggested a few approaches, but nothing was working. Then I realized, wait, "are we sending two requests for the content when we could be sending one?" "Yes, let me fix that." Dude.

Commercial airline manufacturers have been working on automating every step of the process for decades. But pilots still have to know how to fly the plane, and troubleshoot when things go awry.

We still have to understand O(n) notation and how it applies. We still have to understand distributed systems.

We still have to know how to fly the plane.

The assumption here is that AI can produce "strong" work, when in reality its the opposite. Its great at generating boilerplate or code that's been done by everyone.

Instead do the opposite and give it a really hard problem that's scarce on searchable resources, something that you'll struggle to implement on your own. In general what you'll find is that it seems to implement something, but once you start digging you're just going to discover holes and mistakes all over.

You're the one who should be producing strong groundbreaking work, not the AI.

Even if AI was perfect (it’s not), you still need the fundamentals to properly frame what you want it to do and evaluate the results.

Think of how it was before AI. Someone without foundational knowledge of a topic would flounder. They wouldn’t know what to search for or what questions to ask. Meanwhile, someone with that foundational knowledge is able to put together abstract ideas to ask the proper questions and know how to search for to get additional details.

When you don’t know what you don’t know, it’s almost impossible to be effective, even with AI.

It’s a variant of a knapsack problem. But neither Claude nor I initially realized it was a knapsack problem: it became clear only after the solution was found and proved.

I work in a subfield of CS that requires those fundamentals pretty regularly, and I also make regular use of AI tools. You definitely need those fundamentals because AI tools can’t always be trusted to make good decisions when it comes to them. Knowing the fundamentals yourself is critical to keep the AI assistants in check, both to know how to guide them AND to know to recognize when they made a bad decision.

A recent example for me: I had a challenging problem in a medium sized codebase (tens of thousands of lines) that boiled down to performing some updates to a complex data structure where the updates needed to be constrained by some properties of the overall structure to maintain invariants. Maintaining the invariants while the data structure was being updated is tricky since naive approaches would required repeated traversals of the whole structure. That would be really inefficient, and a smarter approach would try to localize the work during the updates. The latest Claude and GPT assistants recognized this, but their solutions were exceptionally complex and brittle. I eventually solved it myself with a significantly simpler and more robust method (both AIs even gleefully agreed that my solution was slick after I did it).

Had I let my CS fundamentals go to waste I wouldn’t have been able to solve it myself, nor would I have been able to recognize that the solutions posed by the models were needlessly complex.

Just because an AI can generate a solution that passes tests quickly doesn't mean what it generated is a long term good solution. Your skills in fundamentals is key to recognizing when it does a good job and when it doesn’t, and being able to guide it in the right direction.