# Friday Blast #49

What do you believe now that you didn’t five years ago (2018) - interesting read about centralized vs decentralized systems. And how five years ago people *believed* they’d take over (I mean, more than in the case of email, the WWW etc), and that hasn’t really happened. And it’s probably *never going to* the way proponents would want.

For mathematicians, = does not mean equality (2018) - it means what they want it to mean. Much more so than programming languages, mathematics depends a lot on the context of readers. While things could be done super-formally, nobody does it. This article explains why, and how the simplest of mathematical notations carry a lot of baggage.

Operationalizing Node.js for Server Side Rendering (2018) - well, if this won’t put you off SSR. An interesting article from AirBnB on how they slowly migrated from a Rails “regular rendering setup”, to a SSR frankenstein to a simpler SSR. In all cases, the fact that we’re dealing with a compute-intensive operation changes the calculus of building such services. From how you allocate machines, to what happens when there’s more requests than capacity for them. In *backend land*, we don’t give enough credit to how puny the computation we do is relative to the I/O, and how easy that makes our life.

Circular law for random matrices (2018) - applied math warning. The eigenvalues of random matrices (ie matrices with components drawn at random from some distribution), neatly fill the complex unit circle under mild assumptions.

Scaling consistency (2018) - how to build “cross-sectional” *review boards*, *architecture committees* etc in growing organisations so that they’ll have the maximum impact and least repercussions. Stuff you can’t read in books.

Learning market dynamics for optimal pricing (2018) - a neat read about modelling problems. This is about predicting the price rooms in AirBnB will be rented at. There’s a lot to unpack here, but the model is equal parts business knowledge, applied machine learning and old fashioned time series forecasting. I really dig it when I see systems like this being advertised. Sure, Deep Learning is all the rage, but a lot of stuff happening is mostly “the simplest model that’ll work”. In many cases that’s a classical linear mode, or some tree etc. And in this case the powerful tools from classical statistics provided the juice to get the thing in a good state. Be on the lookout for some Fourier series as well.