This is also a really common thing in ML specifically. We joke about getting Schmidthuber'd, which is when Jurgen Schmidthuber (sometimes correctly) announces that he or one of his colleagues actually proposed your thing 37 years ago in a Japanese linguists journal.

Statistical modeling, from simple classical stuff up to modern deep learning, just has this dynamic where the theory is rich and bottomless, but the actual components of implementation are pretty neat and compact. So for any given idea, there are probably 20,000 other people who have had the same intuition, just with subtly different application or implementation. Add in that depending on what your particular flavor of research is, you might name an almost identical implementation something completely different. And it leads to a huge amount of sour grapes whenever anyone's idea really garners attention.

If you listen to any podcast with a founder in the ML space who has been in it for long enough, they will invariably say at some point "We actually developed xyz over a year before OpenAI"

> “Predicting sales conversion probability from conversations using pure Reinforcement Learning”. Can you understand what that means?

I can understand it, and it wouldn't excite me at all.

Jev has a beautiful API and is advertised as something much more general.

OP's was leaky slop from day one [0][1], as is his article [2]

It is arrogant and entitled for the author to take credit for the concept of RL over sequence embeddings, and none of the work that went into pretraining, not to mention the egregious target leakage [1]

[0]: Author fails to grasp the concept of virtual environments https://www.reddit.com/r/LocalLLaMA/comments/1kl0uvv/comment...

[1]: his `train.py` has `outcome` as a model input (conversation_metrics built from _parse_conversation which includes outcome): https://huggingface.co/DeepMostInnovations/sales-conversion-... https://huggingface.co/DeepMostInnovations/sales-conversion-...

[2]: 100% of this post is AI-generated https://www.pangram.com/history/97e0be84-391d-46b8-9c16-2d8f...

Not to go all meta but the very post were commenting in is also good marketing and branding.

So while the initial post was not good, the author is currently succeeding to some extent at what you're describing

Especially with „ sales conversion probability“ it just doesn’t sound universal to other issues - there’s tons of unique models for specific use cases

I think OP, as said on Reddit, wanted to get a lot of investment and ride the wave but did not know how to. This was said as such on Reddit today by them. This is indeed a shame especially it was a year earlier but indeed a lack of marketing; many people on Reddit told what to do in this case, in hindsight, it’s worth checking it out imho.

> Predicting sales conversion probability from conversations

That's not just bad marketing, it's an example of anti-marketing.

Sales conversion? That makes me think of an old car's salesman trying to scam me into buying something I don't want. I positively don't want to read this paper based on the title.

It's also well established that an algorithm or architecture alone are not enough to produce a useful model. The same architecture can produce vastly different results depending on the training data, post-training, harness, etc.

1052 pts on HN. I'd say they are pretty good at marketing actually.

While they may not initially done well they are certainly riding this wave.

You're right but it's not the full picture. It's much easier to market when you have a name brand behind you. Not sure the author would've done much better even if he messaged it better. It's like the difference between someone random saying something smart on Twitter and no one gives a shit and Karapthy saying the same thing and everyone talks about it. I'm not saying it in a bad way - those with clout around them earned the people's trust by doing something right. But it's not easy to get there and there are many people doing great things that get very little publicity if at all. Not to mention in this case Jev came from a startup that raised a lot of money and can spend it on good marketing.

I think their problem is more not being cited by the team at typesafe, as in general academic politeness. On the one hand you have the charitable assumption that they developed it independently. On the other hand, my opinion is that it is naive to expect companies to do that even if they took inspo from it, especially when this is a core product theme, and not just some supporting infra. They will of course market it as their own. If they ever release a technical report, they might cite it there, but there is no way their landing page and announcement tweet cites it.

Also, the way highly empirical fields like ML work is that it could very well be the case that typesafe had to do a _lot_ of work to improve this one, and in this field it ends up different enough that they feel they are doing something entirely novel[1]. I am not endorsing that 100%, but that happens a lot even between academics. In many cases it is valid.

[1] For example, this guys implementation seems to have atleast one serious issue, as {solution to OLS} points out in a sibling comment: https://news.ycombinator.com/item?id=49770027

This 100%. Engineers really lack understanding in marketing and branding.

No one cares if you are "first". They only care if your product is known by as many people as possible and is better than all the other alternatives at solving a problem that is worth paying for.

If you don't market, then no-one will care that you exist even if you solved a problem decades ago. Someone else will use your solution and take inspiration (and credit) off of your discovery because you didn't bother to tell anyone about it.

This is exactly what happened here.

I feel like a HN post hitting 1000+ points is a good way to catch up, communication wise.

I wrote an article detailing an idea I had back during web 1.0. I thought it was clever and maybe some people would find it interesting. Few years later a SaaS popped up selling the same idea as a service. It eventually became a pretty commonly followed pattern for a bunch of huge companies in the same domain.

I have no idea if I was literally the first person to have this idea or if anyone who launched one of these businesses read my article. I definitely didn't understand how much commerical value there was or even considered making a business out of it. I blame nobody but myself for missing an opportunity if there even was one.

I did get like $200 for writing it which was nice.

I don't understand why people continue to use em-dashes. As far I know, the comment system on HN doesn't offer them. And iOS and android don't make them a default. They aren't something normally used by humans because - is a keyboard option that's easier to use.

The usage of them immediately makes your commentary suspect. Either you aren't using the standard web interface to make a comment, you're using and odd 3rd party client, or its LLM generated.