Assumption: There is a lot of content on the Internet, and generally people have interests and want appropriate content. The most effective current means are based on keywords/phrases; these means can work really well in some cases but are at best poor for about 2/3rds of content on the Internet, queries people want to do, and content they want to find.

For more: Generally, finding stuff has long been part of the field of information retrieval. That field long ago divided the whole problem into pieces. One big division was between (A) content a person knew existed and had some information about, e.g., keywords/phrases and (B) content that person did not know existed. The old library card catalog subject index is close to case (A) and so are the Internet search engines based on keywords/phrases. For case (B), that is closer to what might be called discovery, recommendation, curation, notification, subscription.

My startup is for being the best for case (B). For case (A), that will remain. What I am doing for case (B) is very different from what has worked well for case (A). Very different: E.g., there is no use of keywords/phrases.

So, my work brings together in a coordinated way (i) the unmet part of the user need to find stuff on the Internet, (ii) a new and very different UI/UX, (iii) some important new data, and (iv), for processing the data to get the results for the users, some new applied math (complete with theorems and proofs, based on some advanced pure math prerequisites) that is the crucial core of the work, that is, the means of getting especially good results for the users.

Some of the efforts seem to have taken some simple techniques and simple, old data sources for recommendation engines and bent the real problem to what these techniques could do; my view is that this is not very promising.

E.g., I believe that my work will do much better for letting people look for, say, movies than what Netflix got from their Netflix Challenge contests. Why? I believe that Netflix formulated the problem poorly, in too narrow a way. What I have done does not fit what Netflix assumed in their problem formulation.

Yes, my work is quite new but, still, supposed to be really easy and intuitive for users. E.g., I hope that a child in a third world country who knows no English will be able to get good with my work in a few minutes alone or sooner watching one example of usage. Right, I might put such an example on YouTube and have my Web site Help page link to that.

There is part of the math that is surprising: One would guess that no such good thing could be true, but it is. Of course, users will not be aware of anything mathematical.

To be a little more clear, my math is original and has nothing to do with anything I've seen in artificial intelligence (AI) or machine learning (ML). To be a little more clear, the AI and ML I have seen do contain some applied math but which is apparently quite narrow. In contrast, on the shelves of the QA sections of the research libraries, there is enormously greater variety, and my work is not on those shelves. So, instead of AI or ML, I just derived some applied (applicable) math.

Then I wrote the corresponding software. Currently there is about 100,000 lines of typing, for both the programming language statements and the documentation in comments in the code. There is much more documentation outside the code. And there is the math, typed into D. Knuth's TeX.

All the code appears to run as intended. The code appears to be essentially all that is needed for at least early production. Currently the code is in alpha test. I'm a solo founder, 100% owner.

The next steps are to complete the alpha test to let me be rock solidly sure the code is correct and ready for production, load some more initial data, do a beta test and get back comments, maybe tweak some parts of the code, say, for the UI/UX, maybe load some more initial data, and, then, just go live in a routine way, get publicity, users, ads, and revenue. Simple.

Right, my math has nothing to do with the social graph, the interest graph, image processing, semantic processing, the semantic graph, speech recognition, natural language processing, singular value decomposition, AI, ML, principal components analysis, support vector machines, collaborative filtering, cluster analysis, intuitive heuristics, neural nets, classification and regression trees (L. Breiman's CART), random forests, gradient descent, etc. Instead, I just derived some new math; I'm supposed to be able to do that; I've done it before on other practical problems of wide variety. It's just some new math -- it has no name like ML. Some aspects of the math, some provable good properties, give me confidence the results for the users will be good.

At first, I will concentrate on users and advertisers just in the US. Then later I may "go international".

If people like my work, then I should have a nice business. Why? From my understanding and assumptions, I have by far the best solution for the problem I am solving; due if only to the math there is essentially no chance that anyone else will do anything at all comparable or competitive; the problem I am solving is so far at best poorly solved; the problem is important to essentially every user of the Internet in the world.

But, right, mostly people don't see the importance of the problem or a solution and are not screaming out for a solution. This is to be expected.

Supposedly Henry Ford said "If I had asked people what they wanted, they would have said a faster horse.". That is, even if people have a big problem, usually they don't envision a solution until they can see the possibility. In Ford's case, that solution involved some cast iron and some gasoline, both quite foreign to people with horses and the problem.

People don't envision my solution now because they don't see the means or the possibility. They will easily envision, and I hope embrace, my solution once they see it. That's what happened when horse owners saw Ford's Model T. It's also what happened with FedEx. And now, Amazon. And for that matter, essentially all of the commercial Internet, e-mail, PCs, laptops, smartphones, etc. For something new, for people to like it, first they have to see it.

Venture capital? Apparently, and I have a lot of evidence, there is nothing in common between my startup and venture capital. That is, across the table, we won't be able to shake hands, ever.

The venture capital people would look at the work I have done to date and say that it and a dime won't cover a 10 cent cup of coffee. They also won't like that I am a solo founder -- formulate your own reasons why. They will hate that there is a role for some original applied math with some advanced prerequisites; they seem to prefer technology that is just routine software. In the patterns they want to use, they have not seen a successful startup like mine. Right, they need to find startups that are exceptional, and to do that they want to use patterns based on successful startups of the past.

They might get interested once my startup has traction (users and/or revenue) significant and growing rapidly, but by then, with me as a solo founder with meager burn rate, I will be nicely profitable, already have a lifestyle business, and have plenty of cash for more servers, etc. So, we can't shake hands across the table because now they believe I am too early and, by the time they are ready to shake hands, I will believe that they are too late.

Is that the elaboration you had in mind?