For those who still doesn't follow closely Lawrence Paulson's blog (shame on you!) he reviews Turing Machines, some other equivalent formalisms and the history of their development: https://lawrencecpaulson.github.io/2022/07/06/Turing_Machines.html

https://www.youtube.com/watch?v=ou4xmq4C7uI

Для низкобюджетного "домашнего" проекта — удивительно хорошо!

https://blog.brownplt.org/2022/06/28/static-python.html

> This work focuses on the Static Python language built by the Instagram team at Meta.
🧐

> In particular, we find that the design holds up the intent of soundness well, but the act of modeling it uncovered several bugs (including one that produced a segmentation fault), all of which have now been fixed.
🤔

> As an aside, this paper is a collaboration that was born entirely thanks to Twitter and most probably would never have occurred withtout it.
😁

https://arrdem.com/2022/07/04/superficial_simplicity/

A reflection on "Simple Made Easy", "Growing a Language", macros and complexity management. Points out (once again) the tension between user-extensibility and tool-analysability.

https://github.com/readme/featured/functional-programming
«Functional programming is finally going mainstream»

Well, if GitHub themselves say so then who are we to disagree?

https://nickchk.com/causalgraphs.html
Causal inference! With animations! 😄

The post explains and illustrates basic notions and methods of causal inference with examples from econometrics. And animated plots, yep.

#causalinference #statistics

Если уж считаете меня клоуном, то хотя бы грустным клоуном!

https://medium.com/@Reisen_0/a-review-of-the-case-against-education-bacc120cb8cd

A very interesting and thoughtful piece on Human Capital vs. Signalling in Education. Pretty fascinating topic, many academic references, great read.

https://imgur.com/a/rnATR7D

A flowchart guiding a regression model construction out of bunch of variables. Touches subtle causal issues.

#statistics #datascience

https://nickch-k.github.io/DataVizChecklist/

A concise "obvious" but totally useful #data #visualization #checklist

Actually I'd say it does not emphasize enough the need for a meaningful story before you try to visualize it. And if you're preparing visualization for an academic publication does not emphasize enough the importance of large enough readable titles and captions. 😄

https://lost-stats.github.io/
"LOST is a Rosetta Stone for statistical software"

Or "Rosetta Code". Useful reference either way.

#statistics #datascience

https://econml.azurewebsites.net/spec/spec.html
The EconML Python SDK, developed by the ALICE team at MSR New England, incorporates individual machine learning steps into interpretable causal models.

Pretty cool. Docs feature introduction into the topic and the methods.

#datascience #causalinference #machinelearning

#machinelearning for 4-graders (~10 years old)
https://orangedatamining.com/blog/2022/2022-06-01-blog-minions-new/

Most important points IMO:
- Single simple task: classification with decision trees
- Guide pupils to invent the method themselves on simplified visual and familiar synthetic data
- Show automation on data pupils collected themselves, manually retrace the generated tree
- Discuss issues with data and problems they generate down the line

https://www.singularity-data.com/blog/building-a-cloud-database-from-scratch-why-we-moved-from-cpp-to-rust/

(Another?) Real-World RIIR. Actually pretty sober evaluation of pros and cons after the fact with some sensible advise.

And to entirely different (old) news. Lawrence Paulson is as sharp as ever:

I get the impression (if Twitter is at all reliable) that they see their rivals as set theorists, though I’d be surprised if any set theorists were even aware of their work.

https://lawrencecpaulson.github.io/2022/07/13/Isabelle_influences.html

I hadn’t understood that in intuitionistic type theory, if you wanted a thing, and you didn’t fancy Russell’s “honest toil”, it was perfectly okay to consult your intuition and simply add the thing you wanted. Of course, you had to have the right intuition or your addition would never get the official imprimatur.

He's undeniably old and sarcastic but hits very close to the base. 😏

https://www.kickstarter.com/projects/retrodigital/stalker-shadow-of-the-zone-fan-film/

This might become the best Stalker movie since Tarkovsky. Or even the very best one depending on your tastes.

https://aviatesk.github.io/JET.jl/dev/

(Finally!) A static type-checker for the #julia programming language (based on Abstract Interpretation).

Another better-than-C++ language from the insides of Google: https://github.com/carbon-language/carbon-lang

They pretty much readily admit it's "worse than Rust" but the "killer feature" is bidirectional interoperability with existing C++ code akin to Java-Kotlin or JavaScript-TypeScript. I guess that puts a bar on how much safety they can attain. 😏

But at least they implement sensible modules/packages system, generics à la Rust, type-level programming without templates metaprogramming craziness.

I wonder how much it differs from D? 🤔

Anyway they already contributed Carbon support into Compiler Explorer (aka Godbolt) so anyone can play with it without even installing: https://carbon.compiler-explorer.com/

https://www.youtube.com/watch?v=2d0iJ4lFKFQ

This one is good. A glimpse of Rutger Hauer's brilliance once more...

https://nickch-k.github.io/SomeThoughts/posts/2022-01-23-overdebunked/

How NOT to critique #statistics especially when you know it barely enough to make yourself look stupid (like I do 😁).

https://mixtape.scunning.com/

Another book on #causalinference ! Looks like a hot topic. 😄

Judging by the cover it predominantly describes "classical" methods like matching, regression discontinuity, difference-in-differences and alike. They promise examples and exercises in R (and Stata, but who cares about that?).