nhalidaybundlesmath   2072
haskell - Using -with-rtsopts ghc option as a pragma - Stack Overflow
When you specify that pragma at the top of the file, this is instead what happens (with ghc --make algo.hs):

ghc -c algo.hs -rtsopts -with-rtsopts=-K32M
ghc -o algo -package somepackage algo.o
The OPTIONS_GHC pragma tells the compiler about options to add when compiling that specific module into an object file. Because -rtsopts is a linker option (it tells GHC to link in a different set of command-line handling stuff), you can't specify it when compiling an object file. You must specify it when linking, and such options cannot be specified in a module header.
q-n-a  stackex  programming  haskell  functional  gotchas  hmm  oly  space-complexity  build-packaging 
19 hours ago by nhaliday
oop - Functional programming vs Object Oriented programming - Stack Overflow
When you anticipate a different kind of software evolution:
- Object-oriented languages are good when you have a fixed set of operations on things, and as your code evolves, you primarily add new things. This can be accomplished by adding new classes which implement existing methods, and the existing classes are left alone.
- Functional languages are good when you have a fixed set of things, and as your code evolves, you primarily add new operations on existing things. This can be accomplished by adding new functions which compute with existing data types, and the existing functions are left alone.

When evolution goes the wrong way, you have problems:
- Adding a new operation to an object-oriented program may require editing many class definitions to add a new method.
- Adding a new kind of thing to a functional program may require editing many function definitions to add a new case.

This problem has been well known for many years; in 1998, Phil Wadler dubbed it the "expression problem". Although some researchers think that the expression problem can be addressed with such language features as mixins, a widely accepted solution has yet to hit the mainstream.

What are the typical problem definitions where functional programming is a better choice?

Functional languages excel at manipulating symbolic data in tree form. A favorite example is compilers, where source and intermediate languages change seldom (mostly the same things), but compiler writers are always adding new translations and code improvements or optimizations (new operations on things). Compilation and translation more generally are "killer apps" for functional languages.
q-n-a  stackex  programming  engineering  nitty-gritty  comparison  best-practices  cost-benefit  functional  data-structures  arrows  flux-stasis  atoms  compilers  examples 
19 hours ago by nhaliday
Preventing the Collapse of Civilization [video] | Hacker News
- 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
- knowledge of trivia becomes more than general, deep knowledge
- does at least acknowledge value of DRY, reusing code, abstraction saving dev time
hn  commentary  video  presentation  techtariat  carmack  pragmatic  contrarianism  pessimism  sv  tech  unix  rhetoric  critique  programming  engineering  pls  worrydream  software  hardware  performance  robust  trends  multiplicative  roots  impact  comparison  history  iron-age  the-classics  mediterranean  conquest-empire  gibbon  technology  the-world-is-just-atoms  flux-stasis  increase-decrease  games  graphics  hmm  idk  systems  os  abstraction  intricacy  worse-is-better/the-right-thing  compilers  build-packaging  microsoft  osx  apple  reflection  assembly  c(pp)  expert-experience  things  knowledge  detail-architecture  thick-thin  trivia  info-dynamics  caching  frameworks  generalization  systematic-ad-hoc  universalism-particularism  analytical-holistic  structure  tainter  libraries  tradeoffs  prepping  threat-modeling  network-structure  writing  risk  local-global  trade  composition-decomposition  coupling-cohesion  parsimony  civilization  complex-systems  system-design 
3 days ago by nhaliday
Information Processing: Moore's Law and AI
Hint to technocratic planners: invest more in physicists, chemists, and materials scientists. The recent explosion in value from technology has been driven by physical science -- software gets way too much credit. From the former we got a factor of a million or more in compute power, data storage, and bandwidth. From the latter, we gained (perhaps) an order of magnitude or two in effectiveness: how much better are current OSes and programming languages than Unix and C, both of which are ~50 years old now?


Of relevance to this discussion: a big chunk of AlphaGo's performance improvement over other Go programs is due to raw compute power (link via Jess Riedel). The vertical axis is ELO score. You can see that without multi-GPU compute, AlphaGo has relatively pedestrian strength.
hsu  scitariat  comparison  software  hardware  performance  sv  tech  trends  ai  machine-learning  deep-learning  deepgoog  google  roots  impact  hard-tech  multiplicative  the-world-is-just-atoms  technology  trivia  cocktail  big-picture  hi-order-bits 
3 days ago by nhaliday
Burrito: Rethinking the Electronic Lab Notebook
Seems very well-suited for ML experiments (if you can get it to work), also the nilfs aspect is cool and basically implements exactly one of the my project ideas (mini-VCS for competitive programming). Unfortunately gnarly installation instructions specify running it on Linux VM: https://github.com/pgbovine/burrito/blob/master/INSTALL. Linux is hard requirement due to nilfs.
techtariat  project  tools  devtools  linux  programming  yak-shaving  integration-extension  nitty-gritty  workflow  exocortex  scholar  software  python  app  desktop  notetaking  state  machine-learning  data-science  nibble  sci-comp  oly  vcs  multi  repo  paste 
4 days ago by nhaliday
Continuous Code Quality | SonarSource
they have cyclomatic complexity rule
$150/year for dev edition (needed for C++ but not Java/Python)
devtools  software  ruby  saas  programming  python  formal-methods  checking  c(pp)  jvm  structure  intricacy  graphs  golang  scala  metrics  javascript 
4 days ago by nhaliday
Abstract: It is well known that the set of correlated equilibrium distributions of an n-player noncooperative game is a convex polytope that includes all the Nash equilibrium distributions. We demonstrate an elementary yet surprising result: the Nash equilibria all lie on the boundary of the polytope.
pdf  nibble  papers  ORFE  game-theory  optimization  geometry  dimensionality  linear-algebra  equilibrium  structure  differential  correlation  iidness  acm  linear-programming  spatial  characterization  levers 
13 days ago by nhaliday
Teach debugging
A friend of mine and I couldn't understand why some people were having so much trouble; the material seemed like common sense. The Feynman Method was the only tool we needed.

1. Write down the problem
2. Think real hard
3. Write down the solution

The Feynman Method failed us on the last project: the design of a divider, a real-world-scale project an order of magnitude more complex than anything we'd been asked to tackle before. On the day he assigned the project, the professor exhorted us to begin early. Over the next few weeks, we heard rumors that some of our classmates worked day and night without making progress.


And then, just after midnight, a number of our newfound buddies from dinner reported successes. Half of those who started from scratch had working designs. Others were despondent, because their design was still broken in some subtle, non-obvious way. As I talked with one of those students, I began poring over his design. And after a few minutes, I realized that the Feynman method wasn't the only way forward: it should be possible to systematically apply a mechanical technique repeatedly to find the source of our problems. Beneath all the abstractions, our projects consisted purely of NAND gates (woe to those who dug around our toolbox enough to uncover dynamic logic), which outputs a 0 only when both inputs are 1. If the correct output is 0, both inputs should be 1. The input that isn't is in error, an error that is, itself, the output of a NAND gate where at least one input is 0 when it should be 1. We applied this method recursively, finding the source of all the problems in both our designs in under half an hour.
techtariat  dan-luu  engineering  programming  debugging  IEEE  reflection  stories  education  higher-ed  checklists  iteration-recursion  divide-and-conquer  thinking  ground-up  nitty-gritty  giants  feynman  error  input-output  structure  composition-decomposition  abstraction  systematic-ad-hoc  reduction  teaching  state 
18 days ago by nhaliday
math 140 tags


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