What are private schools doing that you couldn't implement in public schools with adequate political will and money?

+10000. The goddamn slides. If I were a student now going to engineering school, I'd basically take the slides and throw them into NotebookLM and get way better lectures. Then I'd ask claude or GPT all my hard questions. Hell, I'd get the PDF version of my textbooks and do the same.

The number of lectures actually worthy of your time was so low.

Want a VM? You'll also need this network security group, network interface, network manager, ip, virtual network... and maybe it'll be connected to the internet so you can SSH in? Compare to GCP or EC2 -- you just pick an instance and start it. You can SSH in directly, or even do it in the browser.

Billing also a nightmare: if you're running a startup, AWS and Google make it relatively easy to see how many credits you have left. The Azure dashboard makes you navigate a maze, and the button to click that says "Azure Credits" is _invisible_ for 30s until ostensibly some backend system finds your credits, then it magically shows up. Most people don't wait around and just assume there's no button.

And if you click it, maybe you will happen to be in the correct billing profile, maybe not! Don't get confused: billing profile and billing scope are different concepts too! And in your invoice, costs just magically get deducted, until they don't. No mention of any credits. Credits inaccessible through API (claude tried everything).

VMs, bucket storage, and copying data are the _simplest_ parts of the stack. Why would anyone bother trying to use other services if they can't get these right?

They literally give startups 2x the credits as GCP, 20x the credits of AWS and nobody wants to use them.

Investment is a weird term because most people would consider keeping cash or cash equivalents (gold) to be investments, even if they don't generate wealth. Cash is also an opinion, in terms of the market.

Its price reflects that utility and like any modern asset, a lot of speculation. You can speculate on whether it's more or less useful given current events -- nothing wrong with speculating that it is only going to be increasingly useful.

    global_step = 1377; phase = continuous; lr = 5.00e-03; average_loss = 0.609497
  current tokens: ' Superman' '$MESS' '.");' '(sentence' '");' '.titleLabel' ' Republican' '?-'

    global_step = 1956; phase = continuous; lr = 5.00e-03; average_loss = 0.589661
  current tokens: ' Superman' 'marginLeft' 'iers' '.sensor' '";' '_one' '677' '».'

    global_step = 2468; phase = continuous; lr = 5.00e-03; average_loss = 0.027065
  current tokens: ' cited' '*>(' ' narrative' '_toggle' 'founder' '(V' '(len' ' pione'

    global_step = 4871; phase = continuous; lr = 5.00e-03; average_loss = 0.022909
  current tokens: ' bgcolor' '*>(' ' nomin' 'ust' ' She' 'NW' '(len' ' pione'

This was for classifying sentiment on yelp review polarity.

In terms of performance, their agents differ. The base model their agents use are the same, but for example how they look at your codebase or decide to farm tasks out to lesser models, and how they connect to tools all differ.

Would be nice to see how inference speed stacks up against say llama.cpp

This is a crippling disadvantage. Consider what it takes to evaluate a single software release for a robotaxi.

If you have a simulator, you can take long tail distribution events and just resimulate your software to see if there are regressions against those events. (Waymo, Zoox)

If you don't, or your simulator has too much error, you have to deploy your software in cars in "ghost mode" and hope that sufficient miles see rare and scary situations recur. You then need to find those specific situations and check if your software did a good job (vs just getting lucky). But what if you need to A/B test a change? What if you need to A/B test 100 changes made by different engineers? How do you ensure you're testing the right thing? (Tesla)

And if you have a simulator that _sucks_ because it doesn't have physics-grounded understanding of distances (i.e. it's based on distance estimates from camera), then you can easily trick yourself into thinking your software is doing the right thing, right up until you start killing people.

Another way to look at it is: most driving data is actually very low in signal. You want all the hard driving miles, and in high resolution, so that you can basically generate the world's best unit testing suite for the software driver. You can just throw the rest of the driving data away -- and you must, because nobody has that much storage and unit economics still matter.

This is to say nothing of the fact that differences between hardware matter too. Tesla has a bunch of car models out there, and software working well one one model may not actually work well on another.

So you _do_ agree that willingness to pay is only helpful if there is segmentation.

> Pre-Uber, we had both standard yellow cabs and black car services at different levels

There is more to the gap between yellow cab and black car than cleanliness. Stuff like service / helping you with bags, ETAs, partitions between yourself and the driver, niceness of the car itself, etc.

I'm sure we'll see segmentation along the lines of vehicle size and capability, but I expect cleanliness to be the same across those segments.

> Services often launch with non-scalable attention to detail to control the initial public impression (eating the cost), and then relax over time.

I don't think cleaning is the burden you're making it out to be. These cars return to depot when their battery is down. If you're to clean them at all, you should clean them when they return for charging, and then to your set standard. It's not a big knob for controlling costs.

But I'm saying we _don't_ have that for Waymo, and it's very unlikely to happen, for many reasons. A big reason is simply that managing a fleet in heterogeneous fashion as you're describing (different cleaning schedules for the cars) doesn't really make sense IRL. It's a purely imagined scenario on your part.

> Incorrect.

Pray tell how I can pay for a cleaner car when there's only one option, car or no car?

> No, I'm not assuming that.

Then please explain how cars would get dirtier as the service scales up? If today is already seeing the cars at full utilization, barring a cost-cutting measure that determined that cleaning less frequently would be a significant cost savings (which is a big assumption on your part), then we should be seeing roughly how clean the cars will be into perpetuity.

> Again, Uber Black.

Uber Black achieves higher standards for cleaning by farming that out to the people renting out their personal vehicles. The drivers are incentivized to clean the cars more (than UberX drivers) to get more expensive fares.

But again, fleet management companies already do this for _all_ their cars. So for Waymo this is moot.