The mathematical principles of management

I’ve read about 100 management books by now but if there’s something that always bothered me it’s the lack of first principles thinking. Basically it’s a ton of heuristics. And heuristics are great, but when you present heuristics as true objectives, it kind of clouds the underlying objectives (and you end up with weird proxy cults like the Agile movement 👹 – not that I disagree with it, I just wish they could derive it from a more systematic understanding of project management).

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Functional programming is the libertarianism of software engineering

This is a pretty dumb post, in which I argue that functional programming has a lot of the bad parts of libertarianism and a lot of the good parts:

mind blown

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The half-life of code & the ship of Theseus

trireme

As a project evolves, does the new code just add on top of the old code? Or does it replace the old code slowly over time? In order to understand this, I built a little thing to analyze Git projects, with help from the formidable GitPython project. The idea is to go back in history historical and run a git blame (making this somewhat fast was a bit nontrivial, as it turns out, but I’ll spare you the details, which involve some opportunistic caching of files, pick historical points spread out in time, use git diff to invalidate changed files, etc).

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Are data sets the new server rooms?

This blog post Data sets are the new server rooms makes the point that a bunch of companies raise a ton of money to go get really proprietary awesome data as a competitive moat. Because once you have the data, you can build a better product, and no one can copy it (at least not very cheaply). Ideally you hit a virtuous cycle as well, where usage of your system once it takes of gives even more data, which makes the system even better, which attracts more users…

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Pareto efficency

Pareto efficiency is a useful concept I like to think about. It often comes up when you compare items on multiple dimensions. Say you want to buy a new TV. To simplify it let’s assume you only care about two factors: price and quality. We don’t know what you are willing to pay for quality – but we know that everything else equals:

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When machine learning matters

I joined Spotify in 2008 to focus on machine learning and music recommendations. It’s easy to forget, but Spotify’s key differentiator back then was the low-latency playback. People would say that it felt like they had the music on their own hard drive. (The other key differentiator was licensing – until early 2009 Spotify basically just had all kinds of weird stuff that employees had uploaded. In 2009 after a crazy amount of negotiation the music labels agreed to try it out as an experiment. But I’m getting off topic now.)

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