I've often marveled at the success many guitar players had with experimental electronics - Hendrix, EVH, Les Paul, Brian May, Jack White, and Tom Scholz (special case, of course) are just a few examples.
It's because there's clearly a near-1:1 ratio of input to output. I also noticed some LLMisms, and I suspect the author may have ran the text (perhaps in the form of a large number of bullet points) through an LLM. But because he's using the LLM to clean instead of multiply, it's still worth reading.
Probably similar to what I do with my papers and resumes, I write them myself then throw them through LLMs for suggestions and corrections, manually reviewing the output.
LLM-isms are tolerably bad. LLM's narrative ability is intolerably terrible. As others said, because a human actually wrote the overall narration for this, it was still compelling to read.
I think LLM's lack of "theory of mind" leads to them severely underperforming on narration and humor.
Art and engineering are both constrained optimization problems - at their core, both involve transforming a loosely defined aesthetic desire into a repeatable methodology!
And if we can call ourselves software engineers, where our day-to-day (mostly) involves less calculus and more creative interpretation of loose ideas, in the context of a corpus of historical texts that we literally call "libraries" - are we not artists and art historians?
We're far closer to Jimi than Roger, in many ways. Pots and kettles :)
This is why I feel the recentish (last 10-15 years) shift in decoupling CS curricula from EE and CE fundamentals (which only 10-15 years ago would have been treated as CS) in the US is doing a massive disservice to newer students entering the industry.
DSP, Control Engineering, Circuit Design, understanding pipelining and cacheing, and other fundamentals are important for people to understand higher levels of the abstraction layers (eg. much of deep learning is built on top of Optimization Theory principles which are introduced in a DSP class).
The value of Computer Science isn't the ability to whiteboard a Leetcode hard question or glue together PyTorch commands - it's the ability to reason across multiple abstraction layers.
And newer grads are significantly deskilled due to these curriculum changes. If I as a VC know more about Nagle's Algorithm (hi Nagle!) than some of the potential technical founders for network security or MLOps companies, we are in trouble.
I came into a CS and math background without CE or EE, and took two dedicated optimization courses (one happened to be in a EE department, but had no EE prereqs), as well as the optimization introduced in machine learning classes. To be honest a lot of the older school optimization is barely even useful, second-order methods are a bit passe for large scale ML, largely because they don't work, not because people aren't aware (Adam and Muon can be seen as approximations to second-order methods, though, so it is useful to be aware of that structure).
Isn't Nagle usually introduced in a networking class typically taken by CS (non-CE/EE) undergrads?
Just because EEs are exposed to some mathematical concepts during their training doesn't mean that non-EEs are not exposed through a different path.
In a sluggish economy
Inflation, recession
Hits the land of the free
Standing in unemployment lines
Blame the government for hard time
We just get by
However we can
We all gotta duck
When the shit hits the fan
I've often marveled at the success many guitar players had with experimental electronics - Hendrix, EVH, Les Paul, Brian May, Jack White, and Tom Scholz (special case, of course) are just a few examples.
There is art in engineering that we cannot deny.
While some try to make it as exact science, it is not, there are things you still cannot put a number on and it works ...
This is one of the few articles where I noticed a bunch of LLM-isms and still read to the end because it was interesting.
It's because there's clearly a near-1:1 ratio of input to output. I also noticed some LLMisms, and I suspect the author may have ran the text (perhaps in the form of a large number of bullet points) through an LLM. But because he's using the LLM to clean instead of multiply, it's still worth reading.
Probably similar to what I do with my papers and resumes, I write them myself then throw them through LLMs for suggestions and corrections, manually reviewing the output.
LLM-isms are tolerably bad. LLM's narrative ability is intolerably terrible. As others said, because a human actually wrote the overall narration for this, it was still compelling to read.
I think LLM's lack of "theory of mind" leads to them severely underperforming on narration and humor.
I bailed, it just really kills my desire to keep reading.
The original title: "Jimi Hendrix's Analog Wizardy Explained."
> and the component was the Octavia guitar pedal, created for Hendrix by sound engineer Roger Mayer.
So, Roger was the engineer. And, Jimi was the artist.
Art and engineering are both constrained optimization problems - at their core, both involve transforming a loosely defined aesthetic desire into a repeatable methodology!
And if we can call ourselves software engineers, where our day-to-day (mostly) involves less calculus and more creative interpretation of loose ideas, in the context of a corpus of historical texts that we literally call "libraries" - are we not artists and art historians?
We're far closer to Jimi than Roger, in many ways. Pots and kettles :)
We should not call ourselves engineers - it's a massive insult to actual professional engineers.
This is why I feel the recentish (last 10-15 years) shift in decoupling CS curricula from EE and CE fundamentals (which only 10-15 years ago would have been treated as CS) in the US is doing a massive disservice to newer students entering the industry.
DSP, Control Engineering, Circuit Design, understanding pipelining and cacheing, and other fundamentals are important for people to understand higher levels of the abstraction layers (eg. much of deep learning is built on top of Optimization Theory principles which are introduced in a DSP class).
The value of Computer Science isn't the ability to whiteboard a Leetcode hard question or glue together PyTorch commands - it's the ability to reason across multiple abstraction layers.
And newer grads are significantly deskilled due to these curriculum changes. If I as a VC know more about Nagle's Algorithm (hi Nagle!) than some of the potential technical founders for network security or MLOps companies, we are in trouble.
I came into a CS and math background without CE or EE, and took two dedicated optimization courses (one happened to be in a EE department, but had no EE prereqs), as well as the optimization introduced in machine learning classes. To be honest a lot of the older school optimization is barely even useful, second-order methods are a bit passe for large scale ML, largely because they don't work, not because people aren't aware (Adam and Muon can be seen as approximations to second-order methods, though, so it is useful to be aware of that structure).
Isn't Nagle usually introduced in a networking class typically taken by CS (non-CE/EE) undergrads?
Just because EEs are exposed to some mathematical concepts during their training doesn't mean that non-EEs are not exposed through a different path.
Nice article, but that the signal chain in the top image doesn't match the signal chain described in the text annoys me more than it should.
It's also a standard right handed strat, which seems like an oversight for a guy famous for playing with a right handed strat flipped upside down.
Jimi on the radio is my shorthand for bad economic times. Happened in 2007 and he's playing on the airwaves now
Interesting economic indicator. But isn't Jimi playing on the radio all the time somewhere?
I prefer the Circle Jerks:
And God is a DJ.