Erik Bernhardsson

After a number of years with Python, I started a position working in Scala about 6 months ago. I really wanted to learn something new, and become acquainted with functional programming concepts.

I agree with most of the above. A couple of additional thoughts:

* sbt -- I still have a lot of coming up to speed to do here, but the manual is like 500 pages and it's somewhat overwhelming. There are tons of little oddities, like why can't I run `sbt --version` and instead have to do `sbt sbtVersion`?

* The functional side is fascinating -- I'm still studying the cats library. I can almost describe a Monad! It's a pretty big mountain, though, and there are times where I have doubts whether the benefits will be worth it. Would love to hear some re-assurance! ;)

* The ecosystem for microservices seems pretty closely tied to akka & lagom. These are quite complex in their own right and we've been having trouble with the latter in particular. Curious to learn about alternatives. ZIO?

* Re: DSLs. Also not a huge fan of DSLs. One refreshing thing about python is that often configuration can just be in Python itself (as in Django, for example). See also:

[1] https://erikbern.com/2018/08/30/i-dont-want-to-learn-your-garbage-query-language.html

[2] https://github.com/cf020031308/cf020031308.github.io/blob/master/blog/i-dont-want-to-teach-my-garbage-dsl-either.md

[3] https://twitter.com/antonycourtney/status/589238574429515777

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I don't get the innovation in this paper - are they just running word2vec on groups of items? If so, Spotify has been doing this on playlists for years now: https://erikbern.com/2013/11/02/model-benchmarks/

Also, I know the paper isn't claiming state-of-the-art, but their SVD results are horrendous. Standard CF would create much better artist-artist pairings with even a medium sized dataset.

As an aside, I've run some quantitative and qualitative tests and have found the best recommendations come from a combination of user-item and item-item. I co-gave a talk at the NYC machine learning meetup recently (https://docs.google.com/presentation/d/1S5Cizi9LFQ7l0bMYtY7gASvOPqxNsQk0-NuP5KWAl-4/pub?start=false&loop=false&delayms=3000&slide=id.p4) that shows how this can work, starting at slide 20. The idea is to create a candidate list of matches using item-item, and then reorder using item-user. I've found this creates "sensible" suggestions using item-item, but truly personalizes when re-ordering. You can remove obvious recommendations by removing popular matches or matches the user has already interacted with (I consider this a business decision rather than something inherent in the algorithm).

I guess one good example (that was mentioned just yesterday here on HN) would be Flask vs FastAPI: https://news.ycombinator.com/item?id=31953470 - "There are no open issues or pull requests on Flask "

Quick count finds that FastAPI has 48422 lines of code, while Flask has 9995. Flask just achieved "Zero standing issues/PRs" while FastAPI has 1.1K open issues and ~500 open PRs.

Large surface area/API quickly leads to be overwhelmed when you're trying to maintain it. Adding new features/fixing existing ones becomes harder as well.

Best bet to make sure something is maintainable over time is to add as little as possible to it, and if you really have to, make sure you're also removing something at the same time.

Otherwise you need a massive team just to be able to "survive" and not making things rot.

There is this blogpost as well about the "half-life of code": https://erikbern.com/2016/12/05/the-half-life-of-code.html

Someone run that tool on the Clojure codebase as well, and it really shows how well the Clojure codebase has been written, as most code that was initially written is still there and does what it needs, without having to be rewritten.

I was very curious to see how this sounded.

They did a "great" job... for me, it's exactly ambiguous between a high-pitched male voice and a deep-pitched female voice. It's like the old "is it a vase or two faces?" illusion, or "which way is the ballerina spinning?" It keeps flipping in my brain.

But for the same reason, I would never choose it because it sounds so out-of-the-ordinary, an extreme outlier for human voices. When a computerized voice is speaking, I want it to sound "natural" (e.g. common, unexceptional) so that I'm paying attention to the message, not the voice.

Unfortunately a single "gender-neutral" voice turns out to sound quite uncommon, very exceptional. I only know a couple of people who sound like that, out of 1000's. I applaud the intention, but unfortunately I don't think it works in the end.

Regularly or randomly alternating between "average" female and male voices feels like perhaps a better practical solution, matching the two "humps" in the bimodal distribution of actual voice frequencies. [1]

[1] https://erikbern.com/2017/02/01/language-pitch.html

You don't need data to prove that the number of Nouveau apples someone purchases will go down if I, the sole farmer and distributor, raise the price. The application of an economic law to a new problem doesn't require the law to be re-proved.

The only evidence PG needs is that people continue to spend money on private schools in order to increase university admissions. That serves as evidence that private schooling increases acceptance rates in competitive admissions. It is a variable that schools select for.

When selections are made based on multiple criteria, and the market is mutually competitive, it follows that within each selection set (except sometimes those at the very, very top) those criteria will be negatively correlated.

Because academic performance and private schooling are both criteria that universities use in selecting admitted students, those two traits will be negatively correlated within each student body, i.e., "at any given university."

https://erikbern.com/2020/01/13/how-to-hire-smarter-than-the-market-a-toy-model.html