Ten billion profiles/captures = one amp

[MirrorProfiles]

Yeah I've posted the article a few times I think. But where are the results?

Since the TINA reveal we have seen like only a few new models. You would think this would result in them updating or adding models at a fairly rapid pace.

There’s probably no benefit to them dumping loads of new plugins out if they aren’t selling well. Frustrating watching them drip feed, but Mayer and maybe Misha’s aside, there seems to have been steadily less interest in each new release.

 

[Lysander]

We truly don't know how long TINA has been used.

It was announced with a fanfare back in July 2024 thou.

 

[Mongillo19]

It was announced with a fanfare back in July 2024 thou.

Right but Nameless X was a few months before that and Fortin has explicitly said TINA was used on it

 

[Richard_G]

Fortin has stated that TINA has been used on more than the Nameless as well, like in this post on Instagram in February:

 

[Orvillain]

There's much more to making an amp and releasing it, than the actual content generation steps. There's a fuck ton of marketing, sales, infrastructure stuff that needs to happen.

I can't be arsed to run the numbers. But... it is entirely possible that NDSP are going to drop a shit ton of amps in the future in a NDSP Native type plugin... which I'd be totally down for.

The truth is, beyond a certain point, we're all just guessing.

Sorry, I'm in NDSP fanboi mode right now!

 

[KHAAAAAAANNNNNNN!!!!!!!!]

I don't buy it simply because of the logistics.

For example, let's consider a relatively simply amp with six knobs: Gain, Bass, Mid, Treble, Presence, Master Volume. Now let's say we use a fairly coarse resolution and sample each knob at only 10 positions (1, 2, 3, ..., 10).

That's 1M possible combinations. 1 million!!!

Now let's assume that this so-called machine learning can learn the response in a mere 120 seconds which is extremely optimistic. That means it will take 120M seconds to learn all possible combinations of the controls.

120M seconds = 2M minutes = 33,333 hours = 1,388 days = 3.8 years.

Add a Depth knob and the time increases to 38 years.

Don't forget the bright switch...

 

[Orvillain]

 

[BenIfin]

... Sorry, I'm in NDSP fanboi mode right now!

Its ok. Admitting the problem is the first step

 

[Piing]

I don't buy it simply because of the logistics.

For example, let's consider a relatively simply amp with six knobs: Gain, Bass, Mid, Treble, Presence, Master Volume. Now let's say we use a fairly coarse resolution and sample each knob at only 10 positions (1, 2, 3, ..., 10).

That's 1M possible combinations. 1 million!!!

Now let's assume that this so-called machine learning can learn the response in a mere 120 seconds which is extremely optimistic. That means it will take 120M seconds to learn all possible combinations of the controls.

120M seconds = 2M minutes = 33,333 hours = 1,388 days = 3.8 years.

Add a Depth knob and the time increases to 38 years.

You forgot to add the reaction of the amplifier when rolling down the guitar volume knob. A feature that, unlike the profiles/captures, is faithfully modeled at the Fractal units.

Just another exponential....

 

[Orvillain]

unlike the captures

This is not an accurate statement.

 

[Piing]

This is not an accurate statement.

If that is the case, I must say that my ToneX doesn't get it right. I don't know about other profilers because I haven't spent much time with them.