For all the current US administration has complained about the opposition being “socialist,” they’ve certainly gone all-in on the state partially owning private companies.
Almost like cries of “socialism” have become a dog whistle instead of what the term actually means.
Have you done any serious graphics programming? Even at the OpenGL 1.x level? What you’re saying just doesn’t make sense.
Just because you’re rotating and translating things in 3-space doesn’t negate that you have a stack of transforms that relate a point in world space to one on screen space and you want to be able to project from one to the other.
Nor does it make it any easier when you need to think about how to stack transforms to achieve effects like rendering a mirror.
I honed a lot of useful practical skill with linear algebra trying to get graphics to do what I wanted. And I say this as someone who’s spent the bulk of my career using linear algebra in the context of quantum mechanics, physical simulation, and ML-adjacent areas.
PCA is an orthogonal transformation of the covariance matrix, so like all orthogonal transformations, it’s _literally a rotation_ in N-dimensional space.
SVD is more complex but ultimately it’s just another useful decomposition of a matrix.
I’m not sure why you’re both negative and dismissive. Transformation matrices in graphics are a good and approachable way to get used to linear transformations, which turn out to be useful pretty much everywhere.
Whether or not that helps you with ML depends more on what you’re doing in ML. FAANG doesn’t have a monopoly on ML or on interesting work in ML.
> It has always been overpriced and had huge margins.
This is the engineer’s take on things. I am entirely sympathetic to it.
I also think it missed a lot of what management values in consulting. At its best, you can offload a lot of things unrelated to your business to people who are experts. At its worst, you’ve offloaded the blame to a group of over-worked twenty-something’s with impressive degrees who have no idea what they’re doing, but who sound really fucking confident about it.
Can an internal team do it better? Probably. Will they be cheaper? Probably. Will they assuage management’s anxieties and deflect some/all of the blame? Nope, not at all.
Claiming “50-100 years” is a misleading and hand-waving way of saying “futuristic.”
It tries to get you to imagine that advances in the last 50-100 years will project linearly into advances in the next 50-100 years.
This is not generally the way that science and medicine work. Even if you add in gobs of questionable data collected by companies with a bad track record of doing right by it.
They’re essentially trying to get you to believe that AI + your data will give you the kind of step change in medicine that we got from penicillin and X-rays/MRI/CT imaging. It’s a cheap rhetorical trick.
Regardless of how you think about LLMs (I do find them useful), there’s something really odd to think that you can select for “proven experience” in a young technology where current experience appears to have little to do with experience 15 months ago, and where the biggest boosters fully claim it will have nothing to do with experience in 15 months.
What you’re selecting for is enthusiasm, knowing the current shibboleths of the in-group, and possibly for who knows how to use them to make a good demo.
And, fair enough, if that's what you want. But it's not "proven experience" in my mind.
I love that we’re already talking about “proven experience” for a technology that’s essentially 15 months old, arguably only broke into the mainstream 3-6 months ago, has an unclear RoI for many companies, and seems to be changing quickly in both cost and “best practices.”
You’re more or less admitting that you’re playing trendy tech lottery. Which is fine, but maybe not generalizable to the whole industry.
> For any practical application, you are only interested in finite set of concrete identities
I do a lot of numerical work in settings where computational efficiency is useful.
In my work, most cases you can do numerically using integration or Monte Carlo sampling or whatever.
It’s slow. It often pays to find a closed-form solution. Even if it’s just a starting point that needs refinement.
To put in terms of the Pythagorean theorem: Proving the Pythagorean theorem gives you a relationship that’s reliable, fast to evaluate, and general. Proving individual tuples gives you none of this.
That doesn’t even touch on how theorems give us a glimpse at deeper structure and truths. Proving a bunch of right-triangle tuples will probably never lead you to the rest of the identities in trig.
Ideally contractors that benefit you personally (eg: your buddy who now owes you one), but definitely contractors that let you outsource the responsibility.
Even better if you get some management consultant to suggest the idea and/or do the subcontracting.
Definitely buys you a few quarters of bonus and some time to land your next gig.
This more or less agrees with my assessment of recent changes in Claude Code where a lot of new features are either:
- A lot of half-baked features or half-done features.
- Or have significant overlap with existing features, and aren’t clearly an improvement.
More code is not better. More features are not better. It would be lovely to see more intentional design than just more.
I know they’re dog fooding this. I have to believe they have some people with taste. So it makes me wonder if anyone has the time to think or if they’re just shoveling prompts as fast as possible.
Marc Andreessen has a strong financial incentive to feel this way and to convince others to feel this way.
I also think it’s easy to think that AI gives good answers if you don’t know the field well. In fields where I know the material, the answers are pretty variable and can be quite bad.
If you’ve never written or worked in a Forth-like language, it’s not a hard system to bootstrap up. If you’ve done it before and know assembly, you can even get something that compiles to (stack-heavy and pretty unoptimized) native code in essentially a weekend. No LLM needed.
Forth-likes are almost magical in ways that are hard to describe. You start with primitives and literally build the language out of them. The interpreter and compiler are two different modes of the same REPL loop.
It’s just a very different paradigm than most programmers know.
For all the current US administration has complained about the opposition being “socialist,” they’ve certainly gone all-in on the state partially owning private companies.
Almost like cries of “socialism” have become a dog whistle instead of what the term actually means.