You have poor grasp of human dynamics, communication, coaching, and probably leadership. You are probaly a hubristic ass who is awful to work for.

You are welcome! What a guy I am, so thoughtful.

Interesting aside, George was also the first person to successfully use machine learning to determine musical genres back in the early 2000s.

Comments URL: https://news.ycombinator.com/item?id=49857631

Points: 1

# Comments: 1

https://www.cbc.ca/news/canada/british-columbia/mother-jones...

Comments URL: https://news.ycombinator.com/item?id=49845136

Points: 5

# Comments: 2

Two things: Whether the anti AI slant would work is a marketing question. I happen to think it will, but we are all famously terrible at answering marketing questions until it's hindsight. The only way to know is to start small and find out if your slant resonates.

IMHO, this will absolutely, 100%, need to start bootstrapped and have a plan that involves NEVER giving up more >= 50% ownership. Which can definitely be done, and I've even helped PE firms invest (minority) in these companies. I've seen some fantastic ones. Investors expect you to just do whatever seems like the easy money now, and they are all fucking breathless over AI everywhere right now, to sometimes an astonishingly stupid degree.

This resource looks excellent

Look, I agree that they should be. I have literally had discussions with one of my PhD supervisors (who taught acoustics) about how much better some students would understand some of harmony if they started with some acoustics! But they aren't, so if one is going to redefine common uses of terms, at very least this should be explained in detail to the student prior to use.

My call on this stuff is always determined by "What will help my student learn most efficiently, without frustration or confusion". That article fails badly on that count.

Whether or not this info should be in acoustics books or theory books is not relevant to the new learner, that's where it is! I actually think a crash course in acoustics would be a good addition to a comprehensive theory book, but it doesn't change the fact that no theory course or book has this stuff (not even my pitch set texts!)

I teach music students regularly. I spend a lot of time explaining what something is called in various contexts. They need to know this to learn in a productive and not frustrating manner. They can then learn later why the terms are imperfect and used differently in different subcultures.

But no argument on that is going to convince me that it's good pedagogy to tell new readers that a post on acoustics and synthesis is on music theory. That's just counterproductive at best, or ignorant at worst.

On the other hand, if the author had said something like "Intro to the acoustics behind musical scales", then they might go on to order great books like Kyle Gann's or Gareth Loy's and be off to the races.

Redefining normal terms just makes people sound like they have no clue, and that is, 100%, a serious disservice to the reader/student.

Look, it's like me writing an introduction to "colour theory" and then writing about something else that eventually leads to how we see colour.

I'm not debating that the article has useful content – it does – but it's title and framing is misleading, which is a disservice to the new student. I happen to personally believe everyone getting into music theory would be well served by knowing more acoustics and that this is useful material. But muddying the waters by using terms in weird ways is off.

And when you write about a field and use the wrong terms, you lose credibility. Sorry, I don't trust authors who can't be arsed to use the standard nomenclature for a topic, it really doesn't make them sound trustworthy.

It's a decent enough intro to acoustics and digital synthesis, but saying it's music theory is just redefining terms with accepted meanings in their fields.

Yet another case of programmers thinking "I'm a programmer so I should blog about this thing I barely know anything about as if I'm an expert, what could possibly be the downside?"

On the other hand, nobody trusts the big AI companies not to pull shenanigans or dramatically raise prices... or even be in business in five years.

In my experience as a tech diligence assessor for PE firms for the last 7 years, investors really, really don't like companies being beholded to single entities that they don't control. Anthropic and OpenAI have demonstrated that they are not trustworthy, or predicatable, or finanically safe, or even capable of hitting three fucking nines. Investors know they need companies to be on the AI train, but they really don't like vendor lockin to the big AI companies. Every diligence I get asked "how easily can they change models?"

I think when open models reach 80% or 90% capability (or maybe even less!) a whole lot of companies are going to say "almost as good with way less risk is a better deal".