alt Hacker NewsIn a way it shows how poorly we have done over the years in general as programmers in making solved problems easily accessible instead of constantly reinventing the wheel. I don't know if AI is coming up with anything really novel (yet) but it's certainly a nice database of solved problems.
I just hope we don't all start relying on current[1] AI so much that we lose the ability to solve novel problems ourselves.
[1] (I say "current" AI because some new paradigm may well surpass us completely, but that's a whole different future to contemplate)
Replies
I view LLMs akin to a dictionary - has a bunch of stuff in there but by itself it doesn't add any value. The value comes from the individual piecing together the stuff. Im observing this in the process of using Grok to put together a marketing video - theres a whole bunch of material that the LLM can call upon to produce an output. But its on you to prompt/provide it the right input content to finesse what comes out (this requires the individual to have a lot of intelligence/taste etc....) . Thats the artistry of it.
Now that Im here Ill say Im actually very impressed with Groks ability to output video content in the context of simulating the real-world. They seemingly have the edge on this dimension vs other model providers. But again - this doesnt mean much unless its in the hands of someone with taste etc. You cant one-shot great content. You actually have to do it frame-by-frame then stitch it together.
It’s 2026 and code reuse is still hard. Our code still has terrible modularity. Systems have terrible to nonexistent composability. Attempts to fix this like pure OOP and pure FP have never caught on.
To some extent AI is an entirely different approach. Screw elegance. Programmers won’t adhere to an elegant paradigm anyway. So just automate the process of generating spaghetti. The modularity and reuse is emergent from the latent knowledge in the model.
> In a way it shows how poorly we have done over the years in general as programmers in making solved problems easily accessible instead of constantly reinventing the wheel.
I just don't think there was a great way to make solved problems accessible before LLMs. I mean, these things were on github already, and still got reimplemented over and over again.
Even high traffic libraries that solve some super common problem often have rough edges, or do something that breaks it for your specific use case. So even when the code is accessible, it doesn't always get used as much as it could.
With LLMs, you can find it, learn it, and tailor it to your needs with one tool.