u/Individual_Yard846

On accident, by computing an exact solution to an np-hard problem like TSP that satisfies optimal for nearly any size TSP problem given enough resources in o(1).

(Theoretical)

There would be almost zero benefit in sharing this proof / algorithm to the academic community, and they would have a very hard time even getting looked at properly , anyone with any expertise on this would scoff at such a claim, they would likely get out right rejected for publication across a range of peer reviewed journals. They would have a difficult time getting serious peer review.

It would also cause a sort of chaos as the entire world's encryption is mostly broken overnight with the exact algorithm to make it so..

It would be much smarter to keep it a secret, right?

Build hardware and software solutions that are best in class...trade secret style..

And get rich by being the best?

Lets say a technical but completely unknown solo founder / inventor discovers some unprecedented, novel solution for some long standing problem or pain point like idk, lets say kv-cache --

kv-cache is a good example because it scales linearly and has been identified as a major bottleneck for LLM providers, responsible for up to 80% of total compute required for inference. Its actively being worked on and optimized by the worlds best computer scientists yet they cannot get past linear scaling, all the compression in the world still cannot contain the growing complexity of the kv-cache.

What if a solo founder found a way to keep a fixed size memory footprint for n token context, resulting in o(1) scaling?

This would save the entire industry billions in energy and compute costs - a solution like this could end up becoming a foundational technology serving the next generation of AI models.

It would seem completely unprecedented for an outsider/nobody to solve a major problem like this completely on their own.

completely unbelievable to most, especially to those with PhD's that have dedicated their careers to this problem.

So, my question is...for solo founders who have made deep tech breakthroughs , what is the best strategy for dealing with skeptics, without giving up proprietary algorithms/ trade secrets?

Reproducible benchmarks? Demos? White papers?

What are the benefits and draw backs of filing patents / publishing / trade secrets as a solo deep tech founder without any of the standard credentials (PhD/peer-reviewed paper) ?

Do you just provide the evidence package for anyone to install and validate themselves and just ignore the haters?

This feels more and more a relevant inquiry as frontier models are becoming increasingly capable in hard science and math domains, and more and more passionate, creative-obsessive builders are beginning to tackle hard problems, it seems inevitable that some of them will make serious and significant breakthroughs in any given domain currently dominated by PhD's and gated research.

How will the work be received? What hurdles will they face?

How do they overcome?

Top 1% of newly released packages!

Last year I figured out a way to update state in serverless without the need for a database, allowing me to distribute data and compute non-linearly and holographically across the cloud.. resulting in a novel holographic compute platform I am calling Catalyst.

I needed an easy way to build with the primitives Catalyst provides and built this SDK for myself so i could easily work with my API - its proven to be extremely useful, making it very easy to build breakthrough applications in minutes, so I decided (after months of validating / verifying/evaluating/benchmarking..i even formalized the math and filed a provisional patent) to publish a package on pypi which allows anyone to connect and build with my API almost a month ago.

passed 1000 pip installs last week (much to my surprise) and every day more and more people are discovering and using it, totally organically.

I have open sourced a kv-cache solution...a complete solve, really.

this is an adapter made from my closed source/freemium SDK, catalyst-brain.

This isn't another compression play -- this is a completely novel solution.

This dramatically lowers the barrier of entry to running local, private models as RAM will no longer explode with context.

There is a variation I am working on which allows for a sort of infinite context window trick -- I will publish the adapter for that as well.

Enjoy!!