nhaliday + tutorial   212

Codepip | Learn to code by playing games
I don't really like frontend stuff tbh so this kind of stuff might be path of least resistance to me learning it
puzzles  games  learning  frontend  web  DSL  programming  javascript  tutorial  init  form-design  dynamic  money-for-time  working-stiff 
4 weeks ago by nhaliday
Ask HN: Learning modern web design and CSS | Hacker News
Ask HN: Best way to learn HTML and CSS for web design?: https://news.ycombinator.com/item?id=11048409
Ask HN: How to learn design as a hacker?: https://news.ycombinator.com/item?id=8182084

Ask HN: How to learn front-end beyond the basics?: https://news.ycombinator.com/item?id=19468043
Ask HN: What is the best JavaScript stack for a beginner to learn?: https://news.ycombinator.com/item?id=8780385
Free resources for learning full-stack web development: https://news.ycombinator.com/item?id=13890114

Ask HN: What is essential reading for learning modern web development?: https://news.ycombinator.com/item?id=14888251
Ask HN: A Syllabus for Modern Web Development?: https://news.ycombinator.com/item?id=2184645

Ask HN: Modern day web development for someone who last did it 15 years ago: https://news.ycombinator.com/item?id=20656411
hn  discussion  design  form-design  frontend  web  tutorial  links  recommendations  init  pareto  efficiency  minimum-viable  move-fast-(and-break-things)  advice  roadmap  multi  hacker  games  puzzles  learning  guide  dynamic  retention  DSL  working-stiff  q-n-a  javascript  frameworks  ecosystem  libraries  client-server  hci  ux  books  chart 
4 weeks ago by nhaliday
58 Bytes of CSS to look great nearly everywhere | Hacker News
Author mentions this took a long time to arrive at.
I recommend "Web Design in 4 Minutes" from the CSS guru behind Bulma:

https://jgthms.com/web-design-in-4-minutes/
[ed.: lottsa sensible criticism of the above in the comments]
https://news.ycombinator.com/item?id=12166687
hn  commentary  techtariat  design  form-design  howto  web  frontend  minimum-viable  efficiency  minimalism  parsimony  move-fast-(and-break-things)  tutorial  multi  mobile  init  advice 
5 weeks ago by nhaliday
How to make a fast command line tool in Python
An overview of why Python programs tend to be slow to start running, and some techniques Bazaar uses to start quickly, such as lazy imports.
techtariat  presentation  howto  objektbuch  tutorial  python  programming  performance  tricks  time  latency-throughput  yak-shaving  build-packaging 
august 2019 by nhaliday
How to work with GIT/SVN — good practices - Jakub Kułak - Medium
best part of this is the links to other guides
Commit Often, Perfect Later, Publish Once: https://sethrobertson.github.io/GitBestPractices/

My Favourite Git Commit: https://news.ycombinator.com/item?id=21289827
I use the following convention to start the subject of commit(posted by someone in a similar HN thread):
...
org:med  techtariat  tutorial  faq  guide  howto  workflow  devtools  best-practices  vcs  git  engineering  programming  multi  reference  org:junk  writing  technical-writing  hn  commentary  jargon  list  objektbuch  examples  analysis 
june 2019 by nhaliday
documentation - Materials for learning TikZ - TeX - LaTeX Stack Exchange
The way I learned all three was basically demand-driven --- "learning by doing". Whenever I needed something "new", I'd dig into the manual and try stuff until either it worked (not always most elegantly), or in desperation go to the examples website, or moan here on TeX-'n-Friends. Occasionally supplemented by trying to answer "challenging" questions here.

yeah I kinda figured that was the right approach. just not worth the time to be proactive.
q-n-a  stackex  latex  list  links  tutorial  guide  learning  yak-shaving  recommendations  programming  visuo  dataviz  prioritizing  technical-writing 
may 2019 by nhaliday
its-not-software - steveyegge2
You don't work in the software industry.

...

So what's the software industry, and how do we differ from it?

Well, the software industry is what you learn about in school, and it's what you probably did at your previous company. The software industry produces software that runs on customers' machines — that is, software intended to run on a machine over which you have no control.

So it includes pretty much everything that Microsoft does: Windows and every application you download for it, including your browser.

It also includes everything that runs in the browser, including Flash applications, Java applets, and plug-ins like Adobe's Acrobat Reader. Their deployment model is a little different from the "classic" deployment models, but it's still software that you package up and release to some unknown client box.

...

Servware

Our industry is so different from the software industry, and it's so important to draw a clear distinction, that it needs a new name. I'll call it Servware for now, lacking anything better. Hardware, firmware, software, servware. It fits well enough.

Servware is stuff that lives on your own servers. I call it "stuff" advisedly, since it's more than just software; it includes configuration, monitoring systems, data, documentation, and everything else you've got there, all acting in concert to produce some observable user experience on the other side of a network connection.
techtariat  sv  tech  rhetoric  essay  software  saas  devops  engineering  programming  contrarianism  list  top-n  best-practices  applicability-prereqs  desktop  flux-stasis  homo-hetero  trends  games  thinking  checklists  dbs  models  communication  tutorial  wiki  integration-extension  frameworks  api  whole-partial-many  metrics  retrofit  c(pp)  pls  code-dive  planning  working-stiff  composition-decomposition  libraries  conceptual-vocab  amazon  system-design  cracker-prog  tech-infrastructure  blowhards  client-server 
may 2019 by nhaliday
Use and Interpretation of LD Score Regression
LD Score regression distinguishes confounding from polygenicity in genome-wide association studies: https://sci-hub.bz/10.1038/ng.3211
- Po-Ru Loh, Nick Patterson, et al.

https://www.biorxiv.org/content/biorxiv/early/2014/02/21/002931.full.pdf

Both polygenicity (i.e. many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield inflated distributions of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from bias and true signal from polygenicity. We have developed an approach that quantifies the contributions of each by examining the relationship between test statistics and linkage disequilibrium (LD). We term this approach LD Score regression. LD Score regression provides an upper bound on the contribution of confounding bias to the observed inflation in test statistics and can be used to estimate a more powerful correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of test statistic inflation in many GWAS of large sample size.

Supplementary Note: https://images.nature.com/original/nature-assets/ng/journal/v47/n3/extref/ng.3211-S1.pdf

An atlas of genetic correlations across human diseases
and traits: https://sci-hub.bz/10.1038/ng.3406

https://www.biorxiv.org/content/early/2015/01/27/014498.full.pdf

Supplementary Note: https://images.nature.com/original/nature-assets/ng/journal/v47/n11/extref/ng.3406-S1.pdf

https://github.com/bulik/ldsc
ldsc is a command line tool for estimating heritability and genetic correlation from GWAS summary statistics. ldsc also computes LD Scores.
nibble  pdf  slides  talks  bio  biodet  genetics  genomics  GWAS  genetic-correlation  correlation  methodology  bioinformatics  concept  levers  🌞  tutorial  explanation  pop-structure  gene-drift  ideas  multi  study  org:nat  article  repo  software  tools  libraries  stats  hypothesis-testing  biases  confounding  gotchas  QTL  simulation  survey  preprint  population-genetics 
november 2017 by nhaliday
Anatomy of an SQL Index: What is an SQL Index
“An index makes the query fast” is the most basic explanation of an index I have ever seen. Although it describes the most important aspect of an index very well, it is—unfortunately—not sufficient for this book. This chapter describes the index structure in a less superficial way but doesn't dive too deeply into details. It provides just enough insight for one to understand the SQL performance aspects discussed throughout the book.

B-trees, etc.
techtariat  tutorial  explanation  performance  programming  engineering  dbs  trees  data-structures  nibble  caching  metal-to-virtual  abstraction  applications 
september 2017 by nhaliday
Introduction to Scaling Laws
https://betadecay.wordpress.com/2009/10/02/the-physics-of-scaling-laws-and-dimensional-analysis/
http://galileo.phys.virginia.edu/classes/304/scaling.pdf

Galileo’s Discovery of Scaling Laws: https://www.mtholyoke.edu/~mpeterso/classes/galileo/scaling8.pdf
Days 1 and 2 of Two New Sciences

An example of such an insight is “the surface of a small solid is comparatively greater than that of a large one” because the surface goes like the square of a linear dimension, but the volume goes like the cube.5 Thus as one scales down macroscopic objects, forces on their surfaces like viscous drag become relatively more important, and bulk forces like weight become relatively less important. Galileo uses this idea on the First Day in the context of resistance in free fall, as an explanation for why similar objects of different size do not fall exactly together, but the smaller one lags behind.
nibble  org:junk  exposition  lecture-notes  physics  mechanics  street-fighting  problem-solving  scale  magnitude  estimate  fermi  mental-math  calculation  nitty-gritty  multi  scitariat  org:bleg  lens  tutorial  guide  ground-up  tricki  skeleton  list  cheatsheet  identity  levers  hi-order-bits  yoga  metabuch  pdf  article  essay  history  early-modern  europe  the-great-west-whale  science  the-trenches  discovery  fluid  architecture  oceans  giants  tidbits  elegance 
august 2017 by nhaliday
Analysis of variance - Wikipedia
Analysis of variance (ANOVA) is a collection of statistical models used to analyze the differences among group means and their associated procedures (such as "variation" among and between groups), developed by statistician and evolutionary biologist Ronald Fisher. In the ANOVA setting, the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether or not the means of several groups are equal, and therefore generalizes the t-test to more than two groups. ANOVAs are useful for comparing (testing) three or more means (groups or variables) for statistical significance. It is conceptually similar to multiple two-sample t-tests, but is more conservative (results in less type I error) and is therefore suited to a wide range of practical problems.

good pic: https://en.wikipedia.org/wiki/Analysis_of_variance#Motivating_example

tutorial by Gelman: http://www.stat.columbia.edu/~gelman/research/published/econanova3.pdf

so one way to think of partitioning the variance:
y_ij = alpha_i + beta_j + eps_ij
Var(y_ij) = Var(alpha_i) + Var(beta_j) + Cov(alpha_i, beta_j) + Var(eps_ij)
and alpha_i, beta_j are independent, so Cov(alpha_i, beta_j) = 0

can you make this work w/ interaction effects?
data-science  stats  methodology  hypothesis-testing  variance-components  concept  conceptual-vocab  thinking  wiki  reference  nibble  multi  visualization  visual-understanding  pic  pdf  exposition  lecture-notes  gelman  scitariat  tutorial  acm  ground-up  yoga 
july 2017 by nhaliday
Latin spelling and pronunciation - Wikipedia
https://twitter.com/tcjfs/status/899398522528821249
From Solodow's "Latin Alive": Classical Latin (for literature as opposed to common tongue) was formed out of crucible of nativist anxieties

The earliest continuous Latin texts we have date from the late third and early second centuries B.C.E., beginning with the comedies of Plautus. The Latin in these and the other texts that follow them for the next century displays a certain amount of variety, as we might expect: a large and expressive vocabulary, some freedom with genders, declensions, and conjugations, a certain diversity in inflections and syntax. But in the first half of the first century B.C.E.;., this changed quickly and definitively. A group of men set about to find and fix a suitable form for the language. Their goal was to settle the language once and for all, and, in an important sense, they succeeded. These men, of whom the two most familiar are Caesar (100-44 B.C.E.) and Cicero (106-43 B.C.E.), did not constitute an academy of the Latin language, like those established in modern times for French and Spanish. Instead, by their own conscious practice they shaped the language into a form that seemed pure and worthy.

Their concerted effort to give the Latin language a fixed form was driven in part by the linguistic unsettledness and disorder they perceived around them. Language - actual spoken language - perhaps always appears messy to the ears and eyes of some, but at that time and place the messiness may have been very marked. Rome from its beginnings had been a city of immigrants, and the conquests abroad and other social upheavals of the preceding century had brought into the capital a swarm of people who did not speak Latin as their native language or were not familiar with the variety characteristic of the city. Some men consequently feared the disappearance of authentic, correct Latin. In his history of Roman oratory, Cicero links the deplorable linguistic situation of his day with social changes: “In those days [a century earlier] nearly everybody who had lived in this city and not been corrupted by home-bred provincialism spoke correctly. But the passage of time unquestionably changed the situation for the worse, no less at Rome than in Greece. Many people from different places who spoke a debased language poured into Athens and into this city. The language therefore needs to be purified” (Brutus 258).

Another impetus was the recognition that the linguistic situation, if grave, was not irremediable. Here the model of the Greek language played an important part. As Cicero draws a parallel between the problems at Rome and those at Athens, so he and his contemporaries looked to the latter for guidance in finding a solution. The dialect of Athens, known as Attic, which had established itself among the various Greek dialects as the one most prestigious and most suitable for refined speech and writing, had itselfpassed through a period of conscious purification; this purified Attic Greek served the Romans as an example. And at the same time that Attic offered a model to imitate, Greek rhetoricians were extolling the virtues of language that was logical, unambiguous, and otherwise clear.

Goaded by the current unhappy state of Latin and drawn by a vision of how it might be bettered, Caesar, Cicero, and others set about the task of purifying Latin. They shunned rusticitas “rusticity,” anything that smacked of the countryside. They strove for urbanitas “urbanity, refinement,” and in the sphere of language this was synonymous with Latinitas “(genuine) Latin-ness”; this equation is evident in the passage quoted from Cicero, who identifies as the genuine and desirable variety of Latin the one that had been spoken in the city of Rome by native Romans.
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june 2017 by nhaliday
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