ssalazar

It looks most similar to a Rhodes piano-type electromechanical keyboard, where tuned metal elements are somehow actuated and then sonified with a guitar pickup. Unlikely theres any FM which would require independently digitizing each of the resonators and just generally a lot of complexity that doesnt seem warranted.

The similarity in timbre isn't coincidental though -- FM is noted for its ability to emulate complex timbres like bells/metallic tones (such as electric pianos) that are challenging for more traditional subtractive synthesis architectures.

Many people who get into software development before 2010 or so have easily spent hundreds of dollars on dev tools of a similar nature.

Sharing my experience if it helps. I graduated in 2006 from Princeton CS, cum laude, with no job offers from any tech company. This was even before the global financial crisis of 2007-2009, so I didn't really have an excuse. I was lucky enough to be hired in the CS lab where I did my senior thesis work, as a research assistant, to continue some of the work I did for my senior thesis.

At the time it felt like a humbling experience to still be hanging around campus after already having graduated, but now I look back on those times fondly. The work I did then was on an open source research project that ended up being a cornerstone of my future career, that people still bring up when I meet folks at conferences or other industry events. Eventually I picked up an internship in San Francisco, and from there the job opportunities poured in. I've had a rich and colorful career since, and am currently the CTO of a small-ish tech company in the music space.

Your best bet is to continue investing in work that is in public that you can point to for employers and friends. Its easier said than done to frame a perceived failure as an opportunity, but thats the only constructive way to get through it; looking back, thats exactly been my experience.

Lmao no, what Ive described is a reasonably competent junior engineer.

> very fancy search engines

This is a common misunderstanding of LLMs. The major, qualitative difference is that LLMs represent their knowledge in a latent space that is composable and can be interpolated. For a significant class of programming problems this is industry changing.

E.g. "solve problem X for which there is copious training data, subject to constraints Y for which there is also copious training data" can actually solve a lot of engineering problems for combinations of X and Y that never previously existed, and instead would take many hours of assembling code from a patchwork of tutorials and StackOverflow posts.

This leaves the unknown issues that require deeper reasoning to established software engineers, but so much of the technology industry is using well known stacks to implement CRUD and moving bytes from A to B for different business needs. This is what LLMs basically turbocharge.

I didnt make it past the long list of id() misuses, but its a rookie mistake to confuse referential equality with value equality, and when doing so youre in for a bad time.

The Agency on Showtime also prominently features the Barbican as the protagonist's residence with a number of great exterior shots.

Im saying, lets see some actual reasoning behind the extrapolation rather than "just trust me bro" or "sama said this in a TED talk". Many of the comments here and elsewhere have been in the latter categories.

Thanks -- this is what I mean by evidence, someone with actual experience and skin in the game weighing in rather than blustering proclamations based on vibes.

I agree they improve productivity to where you need fewer developers for a similar quantity of output than before. But I dont think LLMs specifically will reduce the need for some engineer to do the higher level technical design and architecture work, just given what Ive seen and my understanding of the underlying tech.

Im using them fine. Im refuting the grandparent's point that they will replace basically all programming activities (including architecture) in 5 years.

Thanks -- this is much more thoughtful than the persistent chorus of "just trust me, bro".

I code with multiple LLMs every day and build products that use LLM tech under the hood. I dont think we're anywhere near LLMs being good at code design. Existing models make _tons_ of basic mistakes and require supervision even for relatively simple coding tasks in popular languages, and its worse for languages and frameworks that are less represented in public sources of training data. I am _frequently_ having to tell Claude/ChatGPT to clean up basic architectural and design defects. Theres no way I would trust this unsupervised.

Can you point to _any_ evidence to support that human software development abilities will be eclipsed by LLMs other than trying to predict which part of the S-curve we're on?