EDIT: I realize you asked about bars not brands, but I’m in transit and brands was easier than individual bars. I’m a huge fan of the Askinosie orange bar in particular.

I eat a lot of high end single origin chocolate bars, and I simply don’t believe this. Two bars from the same brand at the same percentage of cocoa content using different beans taste completely different. In exactly the same way as wine or coffee. It’s one of the most interesting parts of eating good chocolate. I just don’t believe this approach will ever replace my chocolate consumption, but may have a shot at the larger market of bad chocolate bars.

We've 1. made it extremely difficult, even illegal, to build more denser housing. 2. devalued the status of electricians, plumbers, and carpenters leading to a shortage of people we need to build more homes. 3. made it much too easy to get very large mortgage loans, incentivizing people to leverage themselves much more than they should to purchase homes. Thus bidding up the price of homes as otherwise financially smart people are forced to play the game as well. 4. built software to collectively price fix rents, favoring higher rents over maximum occupancy.

All of this has turned housing into an asset class, in which a significant fraction of the average American's net worth is invested, and has led to huge inflows of investor money. The incentives to not fix any of this are very strong.

But LSTE has so little volume, that you frankly forget they exist most of the time.

> We don’t actually know why resnets work so well.

Yes actually we do. We know, from the literature, that very deep neural networks suffered from vanishing gradients in their early layers in the same way traditional RNNs did. We know that was the motivation for introducing skip connections which gives us a hypothesis we can test. We can measure, using the test I described, the differences in the size of gradients in the early layers with and without skip connections. We can do this across many different problems for additional statistical power. We can analyze the linear case and see that the repeated matmults should lead to small gradients if their singular values are small. To ignore all of this and say that well we don't have a general proof that satisfies a mathematician so i guess we just don't know is silly.

I think the best textbooks are still Deep Learning by Goodfellow etal and the more modern Understanding Deep Learning (https://udlbook.github.io/udlbook/).

Of course I don't mean to imply they operate in a vacuum. They can obviously see competitors raising their prices. I just mean to say they don't get into a room and actively decide on a price.