Ask HN: What hard problems are still underexplored?
Problem with ambiguous boundaries, messy constraints and no linear path to a solution
Imagine you have some ancient toaster you are about to throw in the bin because it is old and you have no use for it and it has no value on the market. Yet, on the other side of the planet, there is a guy who is desperately looking for exactly this toaster because of #reasons. Yet, these people will never be able to find each other to trade.
Yes, there is ebay and whatnot, just like there is tinder for dating, facebook for socialising, various platforms for job hunting, but all these platforms are extremely inefficient in actually delivering on the promise of matching people based on the supply and demand.
The search engines all these platforms use are all very primitive and completely unable to provide the desired service. They are essentially all the same, they just cater to different markets. But there is little technical distinction among them.
The toaster example is a completely trivial one. You can easily expand it to a job where you need a person with specific skills and experience. But you will simply never be able to find that person via any of the existing pathways. Except sheer luck and word of mouth.
This can be likely solved via something like brain implants where we can be connected to the internet and immediately provide necessary context or answer some questions to build a better profile as a "supplier" or "buyer" that could allow a better match. But we're infinitely far away from it.
And this is just one of millions of such small problems that are really hard to solve.
The advertising companies all use tracking to try and mitigate this as much as possible so they can offer you the most likely product or a service that you actually might be interested in buying. But again, these are very primitive solutions.
I have a hunch there is something about the underlying physics we are missing, and that we have not hit the endgame of modelling physics at this scale.
The field of molecular and biological simulation is far more than simply "Newtonian mechanics". There is indeed a field called molecular dynamics (MD) that relies on "classical mechanics" yet it's defined usually in the Lagrangian formalism. Furthermore, there has been tons of work over the past few decades in developing more accurate numerical approximation algorithms. There is a ton of a theory in this field and if you're interested, the "MD Bible" is "Understanding Molecular Simulation" by Daan Frankel.
Now, MD is just the tip of the iceberg. Almost all chemistry simulations are built entirely from making subtle approximations to quantum mechanics and carefully building up frameworks. For example, Hartree-Fock theory (HF), Density Functional Theory (DFT), Couple Cluster theory (CCSD(T)), etc. Then there is a field known colloquially as semi-empirical methods which are a sort of combination of the above two methods. And that's just on the side of chemical simulations (i.e. I'm excluding physics-specific simulations etc).
And now, more recently there has been effort in building machine-learned interatomic potentials, machine-learned density functionals, equivariant graph neural networks, etc etc.
If you're still interested in these class of problems, consider trying to build a good model for OMol25: https://arxiv.org/abs/2505.08762
There is so much possible with it!!!
* no certificates
* direct access to a shell, network stack, and file system from api available directly within the viewport
* a permission system allowing custom roles and security policies
* a better mark up format that imposes accessibility criteria by default like type safety in rust
* a buffer based data serialization so that I don’t have to parse/stringify on every transaction
You can read about it here: https://en.wikipedia.org/wiki/Perfect_number#Odd_perfect_num...
You can watch a short documentary about it here: https://www.youtube.com/watch?v=Zrv1EDIqHkY
>>... a prolonged meditation on the subject has satisfied me that the existence of any one such [odd perfect number] —its escape, so to say, from the complex web of conditions which hem it in on all sides— would be little short of a miracle.
and perhaps even moreso 2) Figuring out how to get them built
It seems we mostly know the answers for 1, we just don't know how to get them built in a sea of development regulations and entrenched interests etc.
When I think of human brain or may be to some extent LLMs, it's difficult to understand what is invisible. For distributed systems we will build tools, there is ongoing research in LLM Observability, but I wonder what about human brain
A word to the unwise is insufficient. https://www.paulgraham.com/word.html