in9

Lately, I've been enjoying a lot of posts in this blog right here:

https://eugeneyan.com/writing

My company just hired some new VPs from Amazon, and the writings in that blog convey a bit of the Amazon WOW. It's pretty insightful stuff.

Also, many posts from Julia Evans :D

More concretely, here are a few blog posts I go back to occasionally:

https://doordash.engineering/2019/02/20/experiment-rigor-for...

https://www.unofficialgoogledatascience.com/2019/12/humans-i...

https://www.unofficialgoogledatascience.com/2016/10/practica...

https://eugeneyan.com/writing/ml-design-docs/

https://eugeneyan.com/writing/what-i-do-before-a-data-scienc...

https://www.fharrell.com/post/addvalue/

https://www.fharrell.com/post/stat-ml2/

The belt analogy, IMHO, does not transfer to programming in general because each field where programming is useful or extremely required has different notions of milestones. If you want to force me into having a general notion of tiers for programming I'd look into the required non-optional courses for a major in computer science and there you have it.

Here are a few that come to mind:

1. Hal Varian's (Chief Economist at Google) presentation on how to incorporate Google Trends term information in univariate time series models: https://www.youtube.com/watch?v=6Kb89ccsSPQ

2. What is a standard error, where a few econometricians and statisticians present the notion of standard error when N = all https://www.youtube.com/watch?v=Yqs2yQOXEP8

Yep, customization is usually the answer. Or something that exists in one ecosystem that is not viable to deploy (looking at you R ecosystem).

For example, its possible, and common, to have a penalized linear model (or logistic model) where just a part of the features parameters to be fit are subjected to the penalization hyper paramenter. Or more complex and costumized loss functions, corresponding to zero inflated or truncated distributions. Those are very cool and sometimes fit to problems directly.

what CAS do you recommend? Are the open source alternatives at the same level as licensed ones? I know Mathematica from Wolfram is well used in the territory. And how does one learn how to use those?

Statistical Inference Berger and Casella. There are better options, but the exercises and notataion are fun.

In the wagon of 'read other peoples code', do check out https://refactoring.guru/design-patterns

I like how they translate to many languages, and how they build the notion of a pattern with simple example and also have a slightly more in depth example as well.

But do try to stay away from those.

I'm aware of how usefull and mind-opening Kahneman's "Thinking, Fast and Slow" is. However one should know that a lot of the experiments done to corroborate the books arguments are underpowered and people were unable to replicate a bunch of them. So don't take the books ideas too seriously and allow for much more nuance than the arguments allows.

I'm reading a couple of books that I think are worth mentioning:

- The Long Game: been thinking a lot about how to long term plan, stick to it and revise when needed. I think this book has been helpfull in those reflections.

- The Complete Investor: just to keep thinking and working on wealth creation, I've been reading this one.

- The Language of Emotions: this one came from Kent Beck's blog. The first half is not something I might ever use (contracts, etc...) but the second part, when it develops each emotion, is nice.

- Taking the Work Out of Networking: I need to make more connections :D And I think this readining will help me out a bit and freshen perspectives on how to build those. The Long Game also has some synergy with this one.

Also, inspired by Jared Henderson lastest video on Stoicism, I've been meaning to read Lessons in Stoicism and a bit of Seneca + Epictetus (no Marcus Aurelius for now, Jared's view conviced me to keep this one for later).

Folks, while we are at it, what is the best way to learn proper front end for a hobbyst? I'm a decent programmer and work with databases and backend technologies. I want to be able to build simple front ends for the stuff I work on. I've been meaning to dive into D3, but the tutorials for basic front end stuff assume I'm really starting from scratch. But what I'm blocked by is 1) what is a nice workflow for development 2) how can I build effective frontend 3) how to setup everything.

If you the way you use computers is keyboard driven, I recommend Vimium. Nice expirience for browsing with the keyboard only

Practical File System Design by Dominic Giampaolo

File Systems: Structures and Algorithms by Harbor

There is probably a relevant chapter on the The Linux Programming Interface on file systems as well.

I do magic as a hobby. And, even though I had spanish classes in highschool, and live in Brazil, I've never looked into the spanish language with much interest. However, as I dove deeper into the magic literature, my style and artistic lean were tilted towards the Spanish magic literature. Even though I've read a few english translations, I picked up a few Tamariz's books and some other magicians in the original editions, and I found it very pleasurable. So much that I went back to study the language just to read those books properly.

Such a great book. Completly forgot about the homework for life tip.

Too much wisdom in those paragraphs! Will certainly have an eye out for the refurbished options. Usually I'm also a very happy with Dells, and one of the most powerfull laptops I've had the chance of working with was an old Latitude (those with a purple finish) and boy it was a beast.

The chrome books seem like a nice path as well.

I'd love to lear more about the nomenclature/model codes that the main manufacturers use. I had no idea "7"s were the reliable ones :D

I'm an Arch user (manjaro when I'm feeling lazy). But the Alpine Installation challenge seems fun :D OpenRC has been in my list to dive into, and I think its worth a try :D Thanks for the suggestion!

I get that such tool needs to exist. In this thread there are a bunch of use cases. But node feels like it shouldn't be used for CLIs (generic ones, i.e. not tied to a specific language and use case).

Every time I see a tool that seems usefull its a hassle to install the correct node environment for it to work.

I have a bunch of little scripts I use with my dwm installation. but the one I'm most proud of is a simple translation script where I type a phrase and it translates to my selected language and places it into my clipboard. Since I work in three languages, this really helps with day to day communication :D

``` #!/bin/bash

choice=$(echo -e 'pt -> es\nen -> es\nes -> pt\nes -> en' | dmenu)

text_to_translate=$(echo '> what to translate?' | dmenu)

[ "$choice" = 'pt -> es' ] && res=$(trans -no-warn --brief pt:es "$text_to_translate") [ "$choice" = 'en -> es' ] && res=$(trans -no-warn --brief en:es "$text_to_translate") [ "$choice" = 'es -> pt' ] && res=$(trans -no-warn --brief es:pt "$text_to_translate") [ "$choice" = 'es -> en' ] && res=$(trans -no-warn --brief es:en "$text_to_translate") [ "$choice" = 'pt -> en' ] && res=$(trans -no-warn --brief pt:en "$text_to_translate")

notify-send "traducão" "$res"

echo "$res" | xclip -selection clipboard ```

I work as a DS lead and have been as DS since 2016. I don't have a master's degree, only a bachelors degree in Econ and a bachelor's degree in Stats. However I code since I'm 14yo, so I'm very capable in the programming side of things.

I have missed carrer oportunities due to the fact that I don't have any further academic achievements besides a bachelors's degree. The small startup might hire you, but the large bank, or more "settled" company, or a large consultancy (big4) won't. That has been my experience so far.

Well refactoring should never be a ticket it self. It should be done in order to facilitate, even enable, the construction of a new feature. Perfomance should be a feature request, IMO. If the user can't use a feature, or se a result, due to performance, the should be requested by the PM it self, no? Reliability as well, should fall under the same logic.

I'm a bit disappointed as I didn't see some of my favorites here :D One of them has already been mentioned, but I'll add it anyway.

- Harvard Stat 110: awesome and somewhat challenging lecture series on probability. It goes into all the probability basics, but also goes into problem solving skill very often, so the problem sets tended to be hard as I recall it. But the nice thing is that a lot of it you can find the solutions which are very well written -- and for the exams as well. Also, the lecturer Joe Blitzstein won best professor at Harvard if I'm not mistaken. https://www.youtube.com/playlist?list=PL2SOU6wwxB0uwwH80KTQ6...

- Statistical Rethinking by Richard Malkreath: man this one will make you relearn statistics. And with a heavy bayesian flavor, which if you hadn't had the chance to learn, will bend your mind as well. You will learn to build models that can describe a lot of situations in the real world, and estimate the parameters from data. Cool stuff if you ask me. https://www.youtube.com/watch?v=BYUykHScxj8&list=PLDcUM9US4X...

- Frank Harrel's Bioistatistics for biomedical reasearch: Frank Harrel is the go to guy to understand how to use data in clinical trials and diagnostics research. His book Regression Modeling strategies is a gem that every data scientist should read. This lecture series is aimed at biomedical researches, ie. people without a strong background in theoretical statistics. In the lectures he talks about the best practices and pitfalls you'll come accross when doing and reading research, and also explain some R code to do a better job. Harrel also wrote some very important R packages i.e. Hmisc and rms. https://www.youtube.com/@bbrcourse6203/videos

- calling bullshit in the era of big data: this is a last year course so it is very laid back in the discussions. I didn't go through the whole thing. But what I watched I remember it was really nice and thought provoking. https://www.youtube.com/watch?v=A2OtU5vlR0k&list=PLPnZfvKID1...

- statistical learning by Hastie and Tibshirani: these are the guys that wrote the two main books on statistical learning. If one wants to get into DS, this is the place to start. https://www.youtube.com/watch?v=LvySJGj-88U&list=PLoROMvodv4...

- Discrete Differential Geometry by Keenan Crane: ok, I didn't see the whole thing, because it was above my understanding. But the graphics and images are so eye catching I almost wanted to just sit there watching. I'm pretty sure this and his computer graphics lectures are aso engaging as hell and hidden gems of the internet. https://www.youtube.com/watch?v=mas-PUA3OvA&list=PL9_jI1bdZm...

just started reading Anathem. Not sure how I feel about it, nor if this was a good book to start reading Neal Stephenson. But time will tell :D

Nice. How are the toggles controlled? Is there a web interface? Or simply a file checked into git?

wow, this is really cool :D

Hello! I'm also on a similar boat. I'm based in Brazil, but do speak spanish as I work at Rappi. Would love to chat about this type of endeavor we got going on. Let me know if there is any prefered contact method!

Cheers and good luck!

Solid advice. One thing that I also became aware of is to not care a lot about workflows, and don't let workflows from the engineers leek into the system.

The reason why I say that is that at some point I worked on a system (first to design and implement it) that for me was very nice and ergonomic, but in the end was full of some quirks and patterns that mostly worked for my workflow. When other engineers joined I clashed with them a lot over those decisions and me imposing a hard structure onto others work/workflow.

When I became a manager and had the opportunity get familiar and re-implement another system, those types of choices and clashes, now from the opposite perspective (I didn't know the code and disagree with all of their implementation choices) really showned me I) the importance of the system design to guide improvements/features and II) how useless it is to dictate how someone should do their job.

I essentially learned that micromanaging sucks, but you get the point :D

But that only goes so far. Some of the key combination do have a nice mneumonic attached to it. But it definetly can't scale. ZZ is one of those, which is the case with most key combinations :/

> 7 days of cold pressed vegetable juices Is that a thing? Like an actual program? Or is that the recomendation you are giving? Becuase if it's an actual program/book/protocol, I'd love to know more about it :D If it's the recommendation it self, how do you portion it in a way that is enough for your metabolism? Do you perform exercises normally? Increase or decrease how hard you train/exercise during those juice only days?

Ill mention a few I tend to go back for sheer joy. I'm a data scientist, so the ones I really like tend to be statistics focused since (I don't do DL):

- Statistical Models for Earthquake Occurrences and Residual Analysis for Point Processes [1] Brought to me by [this](https://simplystatistics.org/posts/2017-09-04-deep-dive-ogat...) blogpost a while ago, for me it shows what a really good statistical analysis based on a model developed for the task at hand looks like. Great stuff.

- Conducting highly principled data science: A statistician’s job and joy [2] Meng is such a good writter. This one really puts into perspective the job of good principles when tackling any kind of task that a data scientist will face. Principles, theoretical principles, are what avoids messing up a task.

1. https://amstat.tandfonline.com/doi/abs/10.1080/01621459.1988... 2. https://www.sciencedirect.com/science/article/pii/S016771521...