As someone who has used linear algebra, statistics and calculus as part of my day-to-day work for years, I would be very cautious of relying on ChatGPT as your "tutor".
Occasionally I've tried to substitute ChatGPT for the shelf of reference books in my office and nearly always had poor results.
The trouble is that the outputs of these models, by their very nature, look convincing. Even as an expert it takes a fair bit of background to realize when ChatGPT is making a mistake. The results virtually always look good at first pass.
I would strongly recommend sticking to text books for self study.
> Common wisdom might make you think that I should be having companies throwing wads of cash at me
My point is just that you're misunderstanding "common wisdom". In 2016, yes a job at any FAANG would mean that you have no trouble getting a new gig. In 2024 Amazon employees do not have a great reputation, so there's no reason to think of yourself as part of a group that "common wisdom" would show preference towards. Nobody with "wads of cash" sees Amazon on a resume and thinks it's something special and worth reaching out for. FAANG in general is not as appealing as it once was, and Amazon in particular is not a strong signal at all. Someone working at OpenAI today would likely have the response you're imagining is reserved for FAANG.
Recruiters are also playing a numbers game, especially in this tech job market. My inbox has ample recruiter message for roles that are not even relevant to my skills, I wouldn't mistake that for signal.
FAANG reputations are no longer evenly distributed.
Even though most people's opinions of the company have changed, Google engineers are still widely considered to be high quality. Apple engineers also have a great reputation but it's very rare to see them on the market. Facebook/meta engineers tend to be above average but have a reputation for being aggressive corporate ladder climbers.
But if I hazard a guess, if you're struggling to find work, you're at Amazon. Amazon's aggressive hire->pip pipeline has, at least in my experience, destroyed their reputation. I've worked with plenty of ex-Amazon engineers in the past few years and they were all notably below average in skill. This is not to say that you are (if you even work for Amazon), but Amazon is no longer the resume bump it once was, if anything quite the opposite.
Talking about "FAANG" is not really relevant anymore since there is a pretty big range of expectation for engineers coming out of each of those letters in the acronym. Additionally none of those companies command quite the attention they did a few years ago, as they've all shifted from demanding engineering skill to demanding the ability to survive in a large mega-corp.
I suspect the signers were a combination of wanting to follow their comp out the door and a bit of Tom Wambsgans from Succession: "Because I've seen you get fucked a lot, and I've never seen Logan [in this case Sam] get fucked once."
There's very little risk in signing if everything falls apart, but there's a lot of risk to not signing if Sam comes back on as lead.
> I find it hard to believe
I also find it hard to believe that anyone on HN interested in this space doesn't at least have a "friend of a friend" who works at OpenAI. Based on what I've heard (which is nothing particularly quotable), it certainly gives off the vibe of being exactly that "kind of environment"
A common error people who love coffee frequently make is underestimating the value of a good grinder.
My first grinder was a regular bean chopper, and when I upgraded to a capresso I thought I was finally at a better place for fresh ground (at the time I though $100 for a grinder was insane).
I finally started looking for real high-end grinders (> $1k) because my espresso was still not up to snuff and all my barista friends kept telling me that the quality of my grinder was what was holding me back.
Funny thing was, even though I was willing to spend $2k on a grinder, supply chain issues had other plans.
Got the 1zpresso K-ultra and wow, each shot of espresso I pull now is a work of art.
Even if you're not an espresso person it will make any way you serve the coffee taste notably better.
Being manual is not even a big deal as it takes very little effort to grind coffee every morning. It's instantly replaced my capresso for not only espresso but pour over as well. I also have no plans to upgrade to a more expensive home option now.
Which is hilarious since they were well known for having an insanely huge data science team supposedly working on really tough problems in personalization. They had a constant stream of (interesting) blog posts but I was always curious how much of that work really touched the product. AI/ML was supposed to be their big market edge.
Not too surprised that didn't work out given my experience with every other company that had built out massive teams of largely inexperienced DS people.
The trouble with TDD is that quite often we don't really know how our programs are going to work when we start writing them, and often make design choices iteratively as we start to realize how our software should behave.
This ultimately means, what most programmers intuitively know, that it's impossible to write adequate test coverage up front (since we don't even really know how we want the program to behave) or worse, test coverage gets in the way of the iterative design process. In theory TDD should work as part of that iterative design, but in practice it means a growing collection of broken tests and tests for parts of the program that end up being completely irrelevant.
The obvious exception to this, where I still use TDD, is when implementing a well defined spec. Anytime you need to build a library to match an existing protocol, well documented api, or even an non-trivial mathematical function, TDD is a tremendous boon. But this is only because the program behavior is well defined.
The times where I've used TDD and it makes sense it's be a tremendous productivity increase. If you're implementing some standard you can basically write the tests to confirm you understand how the protocol/api/function works.
Unfortunately most software is just not well defined up front.
This sounds like a surprisingly good idea, something I hope we see more of (but of course I'll remain pessimistic).
What's crazy to me is that it wasn't always the case that you had to go through the formal process of school to achieve credentialing. If you were truly exceptional, and could prove that, you could then get the required credential.
For example it used to be the case that you didn't need to go to law school to take the bar exam. You still don't in a few US states. Law school might make it a million times easier to learn the law and pass, but if you already know it why not go straight to the test?
Another, admittedly extreme, case was Ludwig Wittgenstein getting his PhD from Trinity College. He had already written the Tractatus Logico-Philosophicus on his own, then basically showed up and presented it as his dissertation, defended it, mocked his friend Bertrand Russel during defense and got the PhD.
What's crazy to me is that if Wittgenstein was alive today he would not be able to achieve this anywhere. There are plenty of people out there who have done ground breaking work in their field, but because of the orthodoxy today, have zero chance of getting a PhD without going through the entire process.
There's such a huge difference between schools saying "you technically don't need us to get the credential, but it's going to be much, much harder to go it your own way" than "it doesn't matter what you do, if you don't sit here and play by our rules you will never be recognized".
This is where it's hard not to get pretty cynical about the state of higher education today.
Occasionally I've tried to substitute ChatGPT for the shelf of reference books in my office and nearly always had poor results.
The trouble is that the outputs of these models, by their very nature, look convincing. Even as an expert it takes a fair bit of background to realize when ChatGPT is making a mistake. The results virtually always look good at first pass.
I would strongly recommend sticking to text books for self study.