stiff

There is in fact a default timeout of 5 minutes and you can change it: https://rclone.org/docs/#timeout-time

I shorten it to prevent lockups like you are describing.

William Cleveland did some research along those lines and he wrote two good books:

    - The elements of graphing data

    - Visualizing data

I think this is no longer true:

https://www.baeldung.com/java-17-random-number-generators

https://openjdk.org/jeps/356

The book has eight voluminous chapters and only one is about hypothesis testing. Much of it is about design of experiments and statistical process control, think something like optimizing the workings of a factory. Hypothesis testing has been under attack in psychology/economics/etc., as part of I think a broader problem those disciplines have drawing reliable conclusions in general, since it is difficult to control all the variables. This book is about engineering and industrial applications which are closer to physics.

Programming is applied algebra just like tying your shoes is applied knot theory :)

Here are a few popular science books:

  One Two Three . . . Infinity: Facts and Speculations of Science by George Gamow
  Infinity and the Mind: The Science and Philosophy of the Infinite by Rudy Rucker
  Yearning for the Impossible: The Surprising Truths of Mathematics by John Stillwell
  Roads to Infinity by John Stillwell

Stanford has ENGR108 [1] based on freely available book "Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares" [2] by Stephen Boyd, with video lectures [3] available. EE263 [4] is sort of a continuation of this at a more advaneced level, it originally also was developed by Boyd and also has video lectures available [5]

[1] https://stanford.edu/class/engr108/

[2] https://web.stanford.edu/~boyd/vmls/

[3] https://www.youtube.com/watch?v=oR6G1MUMveE

[4] https://ee263.stanford.edu/

[5] https://www.youtube.com/playlist?list=PL06960BA52D0DB32B

I think these people other than Feynman transcribed and edited the lectures into book form. This seems to have been the process with most (all?) books of his, "QED" and "The character of physical law" were also delivered as lectures and even "Surely You're Joking, Mr. Feynman!" was an interview originally that was later transcribed and edited.

The "Feynman lectures on physics" books are based on actual lectures to Caltech students. You can listen to audio tapes of the lectures here:

https://www.feynmanlectures.caltech.edu/flptapes.html

Perhaps this was one of the talks by Radia Perlman?

Peter Norvig wrote a good article on this:

"Warning Signs in Experimental Design and Interpretation" http://norvig.com/experiment-design.html

Paul Halmos wrote a book I like a lot titled "Linear Algebra Problem Book" that teaches you linear algebra through a sequence of problems - its different from the one posted here in that the problems are often preceded by 2-3 pages of discussion/motivation and almost all of them are conceptual and interesting.

You can use jupytext to maintain dual .py/.ipynb representation of notebooks and keep both versions in sync:

https://github.com/mwouts/jupytext/blob/main/docs/paired-not...

It works both ways, it can update the .py file each time you save the notebook, or you can edit the .py file and have the jupytext command line tool update the .ipynb.

Production failures due to bugs in both TCP-based servers and clients happen all the time, long time ago I invested a little time to learn basic socket programming in C and the ROI on this has really been spectacular. "Unix Network Programming" by Stevens is a great book on this topic, in particular it carefully discusses all the different ways things can go wrong. Wish there was something more up to date that could be recommended, but I don't know anything of comparable quality, so I think you still have to read it and later work out all the Linux-specific details, more modern APIs like epoll etc.

Vim can't highlight Markdown correctly because it does highlighting via regexps. For correct parsing and thus for correct highlighting of Markdown you need a real parser. See e.g. this:

https://github.com/tpope/vim-markdown/issues/4

For the people who did not notice, in the "Open Access" tab you can download the PDF of the whole book legally and for free.

There is also a second book by the same author which is a bit harder and more comprehensive, and also open access:

https://mitpress.mit.edu/books/art-insight-science-and-engin...

For interviews and programming puzzles you only need to know notation for basic mathematical logic, basic set theory and the summation notation and maybe some bits and pieces from number theory:

https://en.wikipedia.org/wiki/Logical_connective#Common_logi...

https://en.wikipedia.org/wiki/Quantifier_(logic)#Notation

https://en.wikipedia.org/wiki/Set_theory#Basic_concepts_and_...

https://en.wikipedia.org/wiki/Summation#Capital-sigma_notati...

https://en.wikipedia.org/wiki/Modular_arithmetic#Congruence

If Wikipedia is too hard to follow, you can learn this from early chapters of a discrete mathematics textbook.

Knuth is one of the founders of analysis of algorithms as an academic discipline and the original publication of TAOCP played a role in founding the field.

This is a very good book, fairly mathematical: https://archive.org/details/ofmathemahistory00todhrich

Ian Hacking has two books, he is a philosopher, accordingly they have a broader outlook: "The Taming of Chance" "The Emergence of Probability"

Stephen Stigler's "The History of Statistics" seems to be the most popular history of statistics.

Looks like Git has its own string type:

https://github.com/git/git/blob/master/strbuf.h

https://github.com/git/git/blob/master/strbuf.c

See this for the story of why strncpy/strncat are insecure:

https://en.wikipedia.org/wiki/C_string_handling#Replacements

This is one of Lem's most approachable, fun to read books.

I also much recommend his books starring Ijon Tichy, the only books that make me laugh out loud - "The Star Diaries" and "The Futurological Congress":

https://en.wikipedia.org/wiki/Ijon_Tichy

I watched the TV series some weeks or months after having seen the Gary Oldman movie, I enjoyed both, but the TV series is great while the movie I thought was just good. Both have excellent cast and are worth watching just for this reason, but the story with all the nuance seems better suited for the mini-series format than for a 2 hour movie.

If we are speaking movies, the old BBC TV mini-series "Tinker Tailor Soldier Spy" and "Smiley's People" with Alec Guinness as Smiley are absolutely great.

IIRC the book "How to measure anything" contains some advice like this: https://www.amazon.com/dp/1118539273/

The author also offers webinars, so maybe it was from him: https://www.howtomeasureanything.com

I disagree, it's trivial these days to get a list of anything (books, movies, software etc.) simply from google, so if you are going to manually compose a list it better be A) really strictly selected based on true personal experience B) contain any kind of explanation why the item made the list. It's not supposed to turn into an encylopedia, but e.g. I find annotated bibliographies with 2-3 sentences of comments by someone knowledgeable in the field much more valuable than just a list of books.

The article is titled "Avoiding Problem Paralysis". The HN submission title is rather unfortunate, I think.

A dishonest scientist can mine a dataset for statistically significant hypotheses and for a long time no institutional protection against it was in place:

https://en.wikipedia.org/wiki/Data_dredging

https://www.xkcd.com/882/

Machine learning makes it easier to test great many hypothesis, but even going fully "by hand" it is very easy to deviate from what the statistical framework of hypothesis testing would demand. There are now some discussions about counter-measures, e.g. about preregistration of studies:

http://www.sciencemag.org/news/2018/09/more-and-more-scienti...

You can see this as another chapter in the long debate about the correct way to test scientific hypotheses:

https://en.wikipedia.org/wiki/Statistical_hypothesis_testing...

For learning to get things done with Unix, I recommend the two old books "Unix Programming Environment" and "The AWK Programming Language". There are many resources to learn the various commands etc., but there is still no better place than those books to learn the "unix philosophy". This series is also good:

https://sanctum.geek.nz/arabesque/series/unix-as-ide/

They changed a decades-old behavior many people rely on, and it must have been obvious from the start people will loose work because of it.

There are some good lecture videos with Dijkstra online that give a better introduction to his way of thinking than anything he wrote:

"Reasoning about programs": https://www.youtube.com/watch?v=GX3URhx6i2E

"The power of counting arguments": https://www.youtube.com/watch?v=0kXjl2e6qD0

"Structured programming": https://www.youtube.com/watch?v=72RA6Dc7rMQ

"NU lecture": https://www.youtube.com/watch?v=qNCAFcAbSTg