rcar

Agreed. These meetings can certainly end up being as cynically founded as the version posed in the post, but they're often also useful opportunities for execs to make sure that communication of the ultimate intent that led to the proposed work has made it through to the folks who will be tasked with doing the work. Understanding the business goal of the work makes it much easier to accurately weigh the inevitable tradeoffs that come up in projects without needing to run everything all the way back up the chain.

Title should be "packages" rather than "packets".

There are far more fictional TV characters than there are types of wood. I'm a hobbyist woodworker and TV snob but could still many more characters than woods.

Their balance sheet looks very healthy for a growing company with a strong gross margin and very sustainable net losses relative to the capital they've been able to raise. As long as they can maintain their brand position in the space, the multiple they last raised at doesn't seem absurd to me at least.

From their S-1, 2020 revenue was $219m. From that, $106m was cost of revenue, $87m went out to SG&A, and $55m for marketing. Presumably if they wanted to hit pure profitability, they could take their foot off the gas on the admin expenses and marketing, but since they're still seeing good growth and seem to be having an easy enough time getting capital, they may as well keep spending on additional growth.

Sure, everyone wants to get to the latent factors that really drive the outcome of interest, but I've never seen a situation in which principal components _really_ represent latent factors unless you squint hard at them and want to believe. As for gaining insight and explaining user behavior, I'd much rather just fit a decent model and share some SHAP plots for understanding how your features relate to the target and to each other.

If you like PCA and find it works in your particular domains, all the more power to you. I just don't find it practically useful for fitting better models and am generally suspicious of the insights drawn from that and other unsupervised techniques, especially given how much of the meaning of the results gets imparted by the observer who often has a particular story they'd like to tell.

PCA is a cool technique mathematically, but in my many years of building models, I've never seen it result in a more accurate model. I could see it potentially being useful in situations where you're forced to use a linear/logistic model since you're going to have to do a lot of feature preprocessing, but tree ensembles, NNs, etc. are all able to tease out pretty complicated relationships among features on their own. Considering that PCA also complicates things from a model interpretability point of view, it feels to me like a method whose time has largely passed.

I believe that the real intention behind the statement is that not all users of their product are going to have 5 working fingers if they've had some sort of accident or similar.

Agreed with all points. I often find that these sorts of observational studies with weak methods that provide evidence for something the unblinded study participants would want to support (e.g., this study, observational studies of the effect of reduced workweek hours on productivity, many of the observational basic income studies) are really hard to evaluate fairly. It becomes easy to latch onto the conclusions when the intervention is one you're predisposed to believe in and to latch onto the methodological weaknesses when it's one you start of opposed towards, and so I feel like they don't really advance science forward at all.

I found myself wanting a little more content here. Felt mostly like hand waving followed by a plug for their product.

Fine, but you could just as easily flip the arrow or come up with a common cause to come up with a plausible narrative. In areas with low economic growth, lawyers are a high status, high earning potential field, and so why not go into that field? Maybe areas with autocratic leadership see both more complex laws and lower economic growth, leading to a necessity for more lawyers overall.

Observational studies like this are not sufficient to answer these sorts of questions even if you can use them to come up with cute, cheeky headlines.

Correlation vs. causation, etc., etc.

STL files are the 3D models that you put into your slicer of choice (which converts the project into the thin layers that the printer will gradually put down).

There's a dozen things to print in the zip. I sliced 3-4 of them, and none seemed particularly heavy. Something like $0.50 of filament each for what I pay for PLA, which would put the whole thing at ~$6 of material. Perhaps the cost is being driven by some sort of print-on-demand costs?

FWIW, you could also buy an Ender 3 for ~$200, and $140 buys a lot of filament, so you could get into the 3D printing hobby for less than the quoted cost.