Nobody Understands REST or HTTP: https://news.ycombinator.com/item?id=2724488
Some REST best practices: https://news.ycombinator.com/item?id=8618243
REST was never about CRUD: https://news.ycombinator.com/item?id=17563851
Post-REST: https://news.ycombinator.com/item?id=18485978

https://stackoverflow.com/questions/16360351/get-http-request-payload
https://stackoverflow.com/questions/978061/http-get-with-request-body

Ask HN: Were you happy moving your API from REST to GraphQL?: https://news.ycombinator.com/item?id=17565508
From REST to GraphQL: https://news.ycombinator.com/item?id=10365555
REST in Peace. Long Live GraphQL: https://news.ycombinator.com/item?id=14839576
GraphQL Didn't Kill REST: https://news.ycombinator.com/item?id=17572154
https://www.freecodecamp.org/news/five-common-problems-in-graphql-apps-and-how-to-fix-them-ac74d37a293c/

Ask HN: What is your ideal note-taking software and/or hardware?: https://news.ycombinator.com/item?id=13221158

my wishlist as of 2019:
- web + desktop macOS + mobile iOS (at least viewing on the last but ideally also editing)
- sync across all those
- open-source data format that's easy to manipulate for scripting purposes
- flexible organization: mostly tree hierarchical (subsuming linear/unorganized) but with the option for directed (acyclic) graph (possibly a second layer of structure/linking)
- can store plain text, LaTeX, diagrams, and raster/vector images (video prob not necessary except as links to elsewhere)
- full-text search
- somehow digest/import data from Pinboard, Workflowy, Papers 3/Bookends, and Skim, ideally absorbing most of their functionality
- so, eg, track notes/annotations side-by-side w/ original PDF/DjVu/ePub documents (to replace Papers3/Bookends/Skim), and maybe web pages too (to replace Pinboard)
- OCR of handwritten notes (how to handle equations/diagrams?)
- various forms of NLP analysis of everything (topic models, clustering, etc)
- maybe version control (less important than export)

candidates?:
- Evernote prob ruled out do to heavy use of proprietary data formats (unless I can find some way to export with tolerably clean output)
- Workflowy/Dynalist are good but only cover a subset of functionality I want
- org-mode doesn't interact w/ mobile well (and I haven't evaluated it in detail otherwise)
- TiddlyWiki/Zim are in the running, but not sure about mobile
- idk about vimwiki but I'm not that wedded to vim and it seems less widely used than org-mode/TiddlyWiki/Zim so prob pass on that
- Quiver/Joplin/Inkdrop look similar and cover a lot of bases, TODO: evaluate more
- Trilium looks especially promising, tho read-only mobile and for macOS desktop look at this: https://github.com/zadam/trilium/issues/511
- RocketBook is interesting scanning/OCR solution but prob not sufficient due to proprietary data format
- TODO: many more candidates, eg, TreeSheets, Gingko, OneNote (macOS?...), Notion (proprietary data format...), Zotero, Nodebook (https://nodebook.io/landing), Polar (https://getpolarized.io), Roam (looks very promising)

Ask HN: What do you use for you personal note taking activity?: https://news.ycombinator.com/item?id=15736102

Ask HN: What are your note-taking techniques?: https://news.ycombinator.com/item?id=9976751

Ask HN: How do you take notes (useful note-taking strategies)?: https://news.ycombinator.com/item?id=13064215

Ask HN: How to get better at taking notes?: https://news.ycombinator.com/item?id=21419478

Ask HN: How did you build up your personal knowledge base?: https://news.ycombinator.com/item?id=21332957
nice comment from math guy on structure and difference between math and CS: https://news.ycombinator.com/item?id=21338628
useful comment collating related discussions: https://news.ycombinator.com/item?id=21333383
highlights:
Designing a Personal Knowledge base: https://news.ycombinator.com/item?id=8270759
Ask HN: How to organize personal knowledge?: https://news.ycombinator.com/item?id=17892731
Do you use a personal 'knowledge base'?: https://news.ycombinator.com/item?id=21108527
Ask HN: How do you share/organize knowledge at work and life?: https://news.ycombinator.com/item?id=21310030

other stuff:
plain text: https://news.ycombinator.com/item?id=21685660

https://www.getdnote.com/blog/how-i-built-personal-knowledge-base-for-myself/
Tiago Forte: https://www.buildingasecondbrain.com

hn search: https://hn.algolia.com/?query=notetaking&type=story

Slant comparison commentary: https://news.ycombinator.com/item?id=7011281

good comparison of options here in comments here (and Trilium itself looks good): https://news.ycombinator.com/item?id=18840990

https://en.wikipedia.org/wiki/Comparison_of_note-taking_software

wikis:
https://www.slant.co/versus/5116/8768/~tiddlywiki_vs_zim
https://www.wikimatrix.org/compare/tiddlywiki+zim
http://tiddlymap.org/
https://www.zim-wiki.org/manual/Plugins/BackLinks_Pane.html
https://zim-wiki.org/manual/Plugins/Link_Map.html

apps:
Roam: https://news.ycombinator.com/item?id=21440289

intriguing but probably not appropriate for my needs: https://www.sophya.ai/

Inkdrop: https://news.ycombinator.com/item?id=20103589

Joplin: https://news.ycombinator.com/item?id=15815040
https://news.ycombinator.com/item?id=21555238

https://wreeto.com/

Leo Editor (combines tree outlining w/ literate programming/scripting, I think?): https://news.ycombinator.com/item?id=17769892

Frame: https://news.ycombinator.com/item?id=18760079

https://www.reddit.com/r/TheMotte/comments/cb18sy/anyone_use_a_personal_wiki_software_to_catalog/
https://archive.is/xViTY
Notion: https://news.ycombinator.com/item?id=18904648

https://www.reddit.com/r/slatestarcodex/comments/ap437v/modified_cornell_method_the_optimal_notetaking/
https://archive.is/e9oHu
https://www.reddit.com/r/slatestarcodex/comments/bt8a1r/im_about_to_start_a_one_month_journaling_test/
https://www.reddit.com/r/slatestarcodex/comments/9cot3m/question_how_do_you_guys_learn_things/
https://archive.is/HUH8V
https://www.reddit.com/r/slatestarcodex/comments/d7bvcp/how_to_read_a_book_for_understanding/
https://archive.is/VL2mi

Anki:
https://www.reddit.com/r/Anki/comments/as8i4t/use_anki_for_technical_books/
https://www.freecodecamp.org/news/how-anki-saved-my-engineering-career-293a90f70a73/
https://www.reddit.com/r/slatestarcodex/comments/ch24q9/anki_is_it_inferior_to_the_3x5_index_card_an/
https://archive.is/OaGc5
maybe not the best source for a review/advice

interesting comment(s) about tree outliners and spreadsheets: https://news.ycombinator.com/item?id=21170434

tablet:
https://www.inkandswitch.com/muse-studio-for-ideas.html
https://www.inkandswitch.com/capstone-manuscript.html
https://news.ycombinator.com/item?id=20255457

If I had to guess, I'd say I probably work around hundreds of bugs in an average week, and thousands in a bad week. It's not unusual for me to run into a hundred new bugs in a single week. But I often get skepticism when I mention that I run into multiple new (to me) bugs per day, and that this is inevitable if we don't change how we write tests. Well, here's a log of one week of bugs, limited to bugs that were new to me that week. After a brief description of the bugs, I'll talk about what we can do to improve the situation. The obvious answer to spend more effort on testing, but everyone already knows we should do that and no one does it. That doesn't mean it's hopeless, though.

...

Here's where I'm supposed to write an appeal to take testing more seriously and put real effort into it. But we all know that's not going to work. It would take 90k LOC of tests to get Julia to be as well tested as a poorly tested prototype (falsely assuming linear complexity in size). That's two person-years of work, not even including time to debug and fix bugs (which probably brings it closer to four of five years). Who's going to do that? No one. Writing tests is like writing documentation. Everyone already knows you should do it. Telling people they should do it adds zero information1.

Given that people aren't going to put any effort into testing, what's the best way to do it?

Property-based testing. Generative testing. Random testing. Concolic Testing (which was done long before the term was coined). Static analysis. Fuzzing. Statistical bug finding. There are lots of options. Some of them are actually the same thing because the terminology we use is inconsistent and buggy. I'm going to arbitrarily pick one to talk about, but they're all worth looking into.

...

There are a lot of great resources out there, but if you're just getting started, I found this description of types of fuzzers to be one of those most helpful (and simplest) things I've read.

John Regehr has a udacity course on software testing. I haven't worked through it yet (Pablo Torres just pointed to it), but given the quality of Dr. Regehr's writing, I expect the course to be good.

For more on my perspective on testing, there's this.

Everything's broken and nobody's upset: https://www.hanselman.com/blog/EverythingsBrokenAndNobodysUpset.aspx
https://news.ycombinator.com/item?id=4531549

https://hypothesis.works/articles/the-purpose-of-hypothesis/
From the perspective of a user, the purpose of Hypothesis is to make it easier for you to write better tests.

From my perspective as the primary author, that is of course also a purpose of Hypothesis. I write a lot of code, it needs testing, and the idea of trying to do that without Hypothesis has become nearly unthinkable.

But, on a large scale, the true purpose of Hypothesis is to drag the world kicking and screaming into a new and terrifying age of high quality software.

Software is everywhere. We have built a civilization on it, and it’s only getting more prevalent as more services move online and embedded and “internet of things” devices become cheaper and more common.

Software is also terrible. It’s buggy, it’s insecure, and it’s rarely well thought out.

This combination is clearly a recipe for disaster.

The state of software testing is even worse. It’s uncontroversial at this point that you should be testing your code, but it’s a rare codebase whose authors could honestly claim that they feel its testing is sufficient.

Much of the problem here is that it’s too hard to write good tests. Tests take up a vast quantity of development time, but they mostly just laboriously encode exactly the same assumptions and fallacies that the authors had when they wrote the code, so they miss exactly the same bugs that you missed when they wrote the code.

Preventing the Collapse of Civilization [video]: https://news.ycombinator.com/item?id=19945452
- Jonathan Blow

NB: DevGAMM is a game industry conference

- loss of technological knowledge (Antikythera mechanism, aqueducts, etc.)
- hardware driving most gains, not software
- software's actually less robust, often poorly designed and overengineered these days
- *list of bugs he's encountered recently*:
https://youtu.be/pW-SOdj4Kkk?t=1387
- knowledge of trivia becomes more than general, deep knowledge
- does at least acknowledge value of DRY, reusing code, abstraction saving dev time

So what if the speed of light isn’t the same when moving toward or away from us? Are there any observable consequences? Not to the limits of observation so far. We know, for example, that any one-way speed of light is independent of the motion of the light source to 2 parts in a billion. We know it has no effect on the color of the light emitted to a few parts in 1020. Aspects such as polarization and interference are also indistinguishable from standard relativity. But that’s not surprising, because you don’t need to assume isotropy for relativity to work. In the 1970s, John Winnie and others showed that all the results of relativity could be modeled with anisotropic light so long as the two-way speed was a constant. The “extra” assumption that the speed of light is a uniform constant doesn’t change the physics, but it does make the mathematics much simpler. Since Einstein’s relativity is the simpler of two equivalent models, it’s the model we use. You could argue that it’s the right one citing Occam’s razor, or you could take Newton’s position that anything untestable isn’t worth arguing over.

SPECIAL RELATIVITY WITHOUT ONE-WAY VELOCITY ASSUMPTIONS:
https://sci-hub.bz/https://www.jstor.org/stable/186029
https://sci-hub.bz/https://www.jstor.org/stable/186671

corrects several typos in original English translation

The nominal GPS configuration consists of a network of 24 satellites in high orbits around the Earth, but up to 30 or so satellites may be on station at any given time. Each satellite in the GPS constellation orbits at an altitude of about 20,000 km from the ground, and has an orbital speed of about 14,000 km/hour (the orbital period is roughly 12 hours - contrary to popular belief, GPS satellites are not in geosynchronous or geostationary orbits). The satellite orbits are distributed so that at least 4 satellites are always visible from any point on the Earth at any given instant (with up to 12 visible at one time). Each satellite carries with it an atomic clock that "ticks" with a nominal accuracy of 1 nanosecond (1 billionth of a second). A GPS receiver in an airplane determines its current position and course by comparing the time signals it receives from the currently visible GPS satellites (usually 6 to 12) and trilaterating on the known positions of each satellite[1]. The precision achieved is remarkable: even a simple hand-held GPS receiver can determine your absolute position on the surface of the Earth to within 5 to 10 meters in only a few seconds. A GPS receiver in a car can give accurate readings of position, speed, and course in real-time!

More sophisticated techniques, like Differential GPS (DGPS) and Real-Time Kinematic (RTK) methods, deliver centimeter-level positions with a few minutes of measurement. Such methods allow use of GPS and related satellite navigation system data to be used for high-precision surveying, autonomous driving, and other applications requiring greater real-time position accuracy than can be achieved with standard GPS receivers.

To achieve this level of precision, the clock ticks from the GPS satellites must be known to an accuracy of 20-30 nanoseconds. However, because the satellites are constantly moving relative to observers on the Earth, effects predicted by the Special and General theories of Relativity must be taken into account to achieve the desired 20-30 nanosecond accuracy.

Because an observer on the ground sees the satellites in motion relative to them, Special Relativity predicts that we should see their clocks ticking more slowly (see the Special Relativity lecture). Special Relativity predicts that the on-board atomic clocks on the satellites should fall behind clocks on the ground by about 7 microseconds per day because of the slower ticking rate due to the time dilation effect of their relative motion [2].

Further, the satellites are in orbits high above the Earth, where the curvature of spacetime due to the Earth's mass is less than it is at the Earth's surface. A prediction of General Relativity is that clocks closer to a massive object will seem to tick more slowly than those located further away (see the Black Holes lecture). As such, when viewed from the surface of the Earth, the clocks on the satellites appear to be ticking faster than identical clocks on the ground. A calculation using General Relativity predicts that the clocks in each GPS satellite should get ahead of ground-based clocks by 45 microseconds per day.

The combination of these two relativitic effects means that the clocks on-board each satellite should tick faster than identical clocks on the ground by about 38 microseconds per day (45-7=38)! This sounds small, but the high-precision required of the GPS system requires nanosecond accuracy, and 38 microseconds is 38,000 nanoseconds. If these effects were not properly taken into account, a navigational fix based on the GPS constellation would be false after only 2 minutes, and errors in global positions would continue to accumulate at a rate of about 10 kilometers each day! The whole system would be utterly worthless for navigation in a very short time.

The vibration of any given washer is able to transmit its energy only to another washer with exactly the same frequency. Since the length of a pendulum determines its frequency of vibration, each pendulum can only set another pendulum vibrating if it has the same length.

Resonance occurs when a system is able to store and easily transfer energy between two or more different storage modes (such as kinetic energy and potential energy in the case of a simple pendulum). However, there are some losses from cycle to cycle, called damping. When damping is small, the resonant frequency is approximately equal to the natural frequency of the system, which is a frequency of unforced vibrations. Some systems have multiple, distinct, resonant frequencies.

lol, gwern: https://www.reddit.com/r/slatestarcodex/comments/6ghsxf/biweekly_rational_feed/diqr0rq/
> What sort of person thinks “oh yeah, my beliefs about these coefficients correspond to a Gaussian with variance 2.5″? And what if I do cross-validation, like I always do, and find that variance 200 works better for the problem? Was the other person wrong? But how could they have known?
> ...Even ignoring the mode vs. mean issue, I have never met anyone who could tell whether their beliefs were normally distributed vs. Laplace distributed. Have you?
I must have spent too much time in Bayesland because both those strike me as very easy and I often think them! My beliefs usually are Laplace distributed when it comes to things like genetics (it makes me very sad to see GWASes with flat priors), and my Gaussian coefficients are actually a variance of 0.70 (assuming standardized variables w.l.o.g.) as is consistent with field-wide meta-analyses indicating that d>1 is pretty rare.

- "Bonferroni approach" is just union bound
- so Pr(|hat p_i - p_i| > ε for any i) <= 2k e^{-ε^2 n} = δ
- ε = sqrt(ln(2k/δ)/n)
- Bonferroni approach should work for case of any dependent Bernoulli r.v.s

When Einstein abandoned an emission theory of light, he had also to abandon the hope that electrodynamics could be made to conform to the principle of relativity by the normal sorts of modifications to electrodynamic theory that occupied the theorists of the second half of the 19th century. Instead Einstein knew he must resort to extraordinary measures. He was willing to seek realization of his goal in a re-examination of our basic notions of space and time. Einstein concluded his report on his youthful thought experiment:

"One sees that in this paradox the germ of the special relativity theory is already contained. Today everyone knows, of course, that all attempts to clarify this paradox satisfactorily were condemned to failure as long as the axiom of the absolute character of time, or of simultaneity, was rooted unrecognized in the unconscious. To recognize clearly this axiom and its arbitrary character already implies the essentials of the solution of the problem."

can't measure in two bases simultaneously