nhaliday + links   530

Introduction · CTF Field Guide
also has some decent looking career advice and links to books/courses if I ever get interested in infosec stuff
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6 weeks ago by nhaliday
Ask HN: Getting into NLP in 2018? | Hacker News
syllogism (spaCy author):
I think it's probably a bad strategy to try to be the "NLP guy" to potential employers. You'd do much better off being a software engineer on a project with people with ML or NLP expertise.

NLP projects fail a lot. If you line up a job as a company's first NLP person, you'll probably be setting yourself up for failure. You'll get handed an idea that can't work, you won't know enough about how to push back to change it into something that might, etc. After the project fails, you might get a chance to fail at a second one, but maybe not a third. This isn't a great way to move into any new field.

I think a cunning plan would be to angle to be the person who "productionises" models.

Basically, don't just work on having more powerful solutions. Make sure you've tried hard to have easier problems as well --- that part tends to be higher leverage.

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9 weeks ago by nhaliday
Has Australia Really Had a 28-Year Expansion? (Yes!) - Marginal REVOLUTION
The bottom line is that however you measure it, Australian performance looks very good. Moreover RER are correct that one of the reasons for strong Australian economic performance is higher population growth rates. It’s not that higher population growth rates are masking poorer performance in real GDP per capita, however, it’s more in my view that higher population growth rates are contributing to strong performance as measured by both real GDP and real GDP per capita.
Control+F "China"
0 results.
China gets a 40 year expansion relying heavily on commodities. Australia squeezes 30 years out of it by happily selling to the Chinese.

econotariat  marginal-rev  commentary  links  summary  data  analysis  economics  growth-econ  econ-metrics  wealth  china  asia  anglo  anglosphere  trade  population  demographics  increase-decrease 
12 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 
october 2019 by nhaliday
Ask HN: Favorite note-taking software? | Hacker News
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, sketches, 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, Skim, and iBooks/e-readers (esp. Kobo), 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)

- 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 do you keep your notes organized?: https://news.ycombinator.com/item?id=21810400

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
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
Managing my personal knowledge base: https://news.ycombinator.com/item?id=22000791
The sad state of personal data and infrastructure: https://beepb00p.xyz/sad-infra.html
Building personal search infrastructure for your knowledge and code: https://beepb00p.xyz/pkm-search.html

How to annotate literally everything: https://beepb00p.xyz/annotating.html
Ask HN: How do you organize document digests / personal knowledge?: https://news.ycombinator.com/item?id=21642289
Ask HN: Good solution for storing notes/excerpts from books?: https://news.ycombinator.com/item?id=21920143
some related stuff in the reddit links at the bottom of this pin

How to capture information from your browser and stay sane

Ask HN: Best solutions for keeping a personal log?: https://news.ycombinator.com/item?id=21906650

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

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



Roam: https://news.ycombinator.com/item?id=21440289
interesting app: http://www.eastgate.com/Tinderbox/

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


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

Notion: https://news.ycombinator.com/item?id=18904648

accounting: https://news.ycombinator.com/item?id=19833881
Coda mentioned


maybe not the best source for a review/advice

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

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october 2019 by nhaliday
Python Tutor - Visualize Python, Java, C, C++, JavaScript, TypeScript, and Ruby code execution
C++ support but not STL

Ten years and nearly ten million users: my experience being a solo maintainer of open-source software in academia: http://www.pgbovine.net/python-tutor-ten-years.htm
tools  devtools  worrydream  ux  hci  research  project  homepage  python  programming  c(pp)  javascript  jvm  visualization  software  internet  web  debugging  techtariat  state  form-design  multi  reflection  oss  shipping  community  collaboration  marketing  ubiquity  robust  worse-is-better/the-right-thing  links  performance  engineering  summary  list  top-n  pragmatic  cynicism-idealism 
september 2019 by nhaliday
Three best practices for building successful data pipelines - O'Reilly Media
Drawn from their experiences and my own, I’ve identified three key areas that are often overlooked in data pipelines, and those are making your analysis:
1. Reproducible
2. Consistent
3. Productionizable


Science that cannot be reproduced by an external third party is just not science — and this does apply to data science. One of the benefits of working in data science is the ability to apply the existing tools from software engineering. These tools let you isolate all the dependencies of your analyses and make them reproducible.

Dependencies fall into three categories:
1. Analysis code ...
2. Data sources ...
3. Algorithmic randomness ...


Establishing consistency in data

There are generally two ways of establishing the consistency of data sources. The first is by checking-in all code and data into a single revision control repository. The second method is to reserve source control for code and build a pipeline that explicitly depends on external data being in a stable, consistent format and location.

Checking data into version control is generally considered verboten for production software engineers, but it has a place in data analysis. For one thing, it makes your analysis very portable by isolating all dependencies into source control. Here are some conditions under which it makes sense to have both code and data in source control:
Small data sets ...
Regular analytics ...
Fixed source ...

Productionizability: Developing a common ETL

1. Common data format ...
2. Isolating library dependencies ...

Rigorously enforce the idempotency constraint
For efficiency, seek to load data incrementally
Always ensure that you can efficiently process historic data
Partition ingested data at the destination
Rest data between tasks
Pool resources for efficiency
Store all metadata together in one place
Manage login details in one place
Specify configuration details once
Parameterize sub flows and dynamically run tasks where possible
Execute conditionally
Develop your own workflow framework and reuse workflow components

more focused on details of specific technologies:

techtariat  org:com  best-practices  engineering  code-organizing  machine-learning  data-science  yak-shaving  nitty-gritty  workflow  config  vcs  replication  homo-hetero  multi  org:med  design  system-design  links  shipping  minimalism  volo-avolo  causation  random  invariance  structure  arrows  protocol-metadata  interface-compatibility  project-management 
august 2019 by nhaliday
Information Processing: Beijing 2019 Notes
Trump, the trade war, and US-China relations came up frequently in discussion. Chinese opinion tends to focus on the long term. Our driver for a day trip to the Great Wall was an older man from the countryside, who has lived only 3 years in Beijing. I was surprised to hear him expressing a very balanced opinion about the situation. He understood Trump's position remarkably well -- China has done very well trading with the US, and owes much of its technological and scientific development to the West. A recalibration is in order, and it is natural for Trump to negotiate in the interest of US workers.

China's economy is less and less export-dependent, and domestic drivers of growth seem easy to identify. For example, there is still a lot of low-hanging fruit in the form of "catch up growth" -- but now this means not just catching up with the outside developed world, but Tier 2 and Tier 3 cities catching up with Tier 1 cities like Beijing, Shanghai, Shenzhen, etc.

China watchers have noted the rapidly increasing government and private sector debt necessary to drive growth here. Perhaps this portends a future crisis. However, I didn't get any sense of impending doom for the Chinese economy. To be fair there was very little inkling of what would happen to the US economy in 2007-8. Some of the people I met with are highly placed with special knowledge -- they are among the most likely to be aware of problems. Overall I had the impression of normalcy and quiet confidence, but perhaps this would have been different in an export/manufacturing hub like Shenzhen. [ Update: Today after posting this I did hear something about economic concerns... So situation is unclear. ]

Innovation is everywhere here. Perhaps the most obvious is the high level of convenience from the use of e-payment and delivery services. You can pay for everything using your mobile (increasingly, using just your face!), and you can have food and other items (think Amazon on steroids) delivered quickly to your apartment. Even museum admissions can be handled via QR code.

A highly placed technologist told me that in fields like AI or computer science, Chinese researchers and engineers have access to in-depth local discussions of important arXiv papers -- think StackOverflow in Mandarin. Since most researchers here can read English, they have access both to Western advances, and a Chinese language reservoir of knowledge and analysis. He anticipates that eventually the pace and depth of engineering implementation here will be unequaled.

IVF and genetic testing are huge businesses in China. Perhaps I'll comment more on this in the future. New technologies, in genomics as in other areas, tend to be received more positively here than in the US and Europe.


Note Added: In the comments AG points to a Quora post by a user called Janus Dongye Qimeng, an AI researcher in Cambridge UK, who seems to be a real China expert. I found these posts to be very interesting.

Infrastructure development in poor regions of China

Size of Chinese internet social network platforms

Can the US derail China 2025? (Core technology stacks in and outside China)

Huawei smartphone technology stack and impact of US entity list interdiction (software and hardware!)

Agriculture at Massive Scale

US-China AI competition

More recommenations: Bruno Maçães is one of my favorite modern geopolitical thinkers. A Straussian of sorts (PhD under Harvey Mansfield at Harvard), he was Secretary of State for European Affairs in Portugal, and has thought deeply about the future of Eurasia and of US-China relations. He spent the last year in Beijing and I was eager to meet with him while here. His recent essay Equilibrium Americanum appeared in the Berlin Policy Journal. Podcast interview -- we hope to have him on Manifold soon :-)
hsu  scitariat  china  asia  thucydides  tech  technology  ai  automation  machine-learning  trends  the-bones  links  reflection  qra  q-n-a  foreign-policy  world  usa  trade  nationalism-globalism  great-powers  economics  research  journos-pundits  straussian 
july 2019 by nhaliday
Call graph - Wikipedia
I've found both static and dynamic versions useful (former mostly when I don't want to go thru pain of compiling something)

best options AFAICT:

C/C++ and maybe Go: https://github.com/gperftools/gperftools

static: https://github.com/Vermeille/clang-callgraph
I had to go through some extra pain to get this to work:
- if you use Homebrew LLVM (that's slightly incompatible w/ macOS c++filt, make sure to pass -n flag)
- similarly macOS sed needs two extra backslashes for each escape of the angle brackets

another option: doxygen

Go: https://stackoverflow.com/questions/31362332/creating-call-graph-in-golang
both static and dynamic in one tool

Java: https://github.com/gousiosg/java-callgraph
both static and dynamic in one tool

more up-to-date forks: https://github.com/daneads/pycallgraph2 and https://github.com/YannLuo/pycallgraph
old docs: https://pycallgraph.readthedocs.io/en/master/
I've had some trouble getting nice output from this (even just getting the right set of nodes displayed, not even taking into account layout and formatting).
- Argument parsing syntax is idiosyncratic. Just read `pycallgraph --help`.
- Options -i and -e take glob patterns (see pycallgraph2/{tracer,globbing_filter}.py), which are applied the function names qualified w/ module paths.
- Functions defined in the script you are running receive no module path. There is no easy way to filter for them using the -i and -e options.
- The --debug option gives you the graphviz for your own use instead of just writing the final image produced.

static: https://github.com/davidfraser/pyan
more up-to-date fork: https://github.com/itsayellow/pyan/
one way to good results: `pyan -dea --format yed $MODULE_FILES > output.graphml`, then open up in yEd and use hierarchical layout

various: https://github.com/jrfonseca/gprof2dot

I believe all the dynamic tools listed here support weighting nodes and edges by CPU time/samples (inclusive and exclusive of descendants) and discrete calls. In the case of the gperftools and the Java option you probably have to parse the output to get the latter, tho.

IIRC Dtrace has probes for function entry/exit. So that's an option as well.

old pin: https://github.com/nst/objc_dep
Graph the import dependancies in an Objective-C project
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july 2019 by nhaliday
Home is a small, engineless sailboat (2018) | Hacker News
Her deck looked disorderly; metal pipes lying on either side of the cabin, what might have been a bed sheet or sail cover (or one in the same) bunched between oxidized turnbuckles and portlights. A purple hula hoop. A green bucket. Several small, carefully potted plants. At the stern, a weathered tree limb lashed to a metal cradle – the arm of a sculling oar. There was no motor. The transom was partially obscured by a wind vane and Alexandra’s years of exposure to the elements were on full display.


Sean is a programmer, a fervent believer in free open source code – software programs available to the public to use and/or modify free of charge. His only computer is the Raspberry Pi he uses to code and control his autopilot, which he calls pypilot. Sean is also a programmer for and regular contributor to OpenCPN Chart Plotter Navigation, free open source software for cruisers. “I mostly write the graphics or the way it draws the chart, but a lot more than that, like how it draws the weather patterns and how it can calculate routes, like you should sail this way.”

from the comments:
Have also read both; they're fascinating in different ways. Paul Lutus has a boat full of technology (diesel engine, laptop, radio, navigation tools, and more) but his book is an intensely - almost uncomfortably - personal voyage through his psyche, while he happens to be sailing around the world. A diary of reflections on life, struggles with people, views on science, observations on the stars and sky and waves, poignant writing on how being at sea affect people, while he happens to be sailing around the world. It's better for that, more relatable as a geek, sadder and more emotional; I consider it a good read, and I reflect on it a lot.
Captain Slocum's voyage of 1896(?) is so different; he took an old clock, and not much else, he lashes the tiller and goes down below for hours at a time to read or sleep without worrying about crashing into other boats, he tells stories of mouldy cheese induced nightmares during rough seas or chasing natives away from robbing him, or finding remote islands with communites of slightly odd people. Much of his writing is about the people he meets - they often know in advance he's making a historic voyage, so when he arrives anywhere, there's a big fuss, he's invited to dine with local dignitaries or captains of large ships, gifted interesting foods and boat parts, there's a lot of interesting things about the world of 1896. (There's also quite a bit of tedious place names and locations and passages where nothing much happens, I'm not that interested in the geography of it).
hn  commentary  oceans  books  reflection  stories  track-record  world  minimum-viable  dirty-hands  links  frontier  allodium  prepping  navigation  oss  hacker 
july 2019 by nhaliday
Computer latency: 1977-2017
If we look at overall results, the fastest machines are ancient. Newer machines are all over the place. Fancy gaming rigs with unusually high refresh-rate displays are almost competitive with machines from the late 70s and early 80s, but “normal” modern computers can’t compete with thirty to forty year old machines.


If we exclude the game boy color, which is a different class of device than the rest, all of the quickest devices are Apple phones or tablets. The next quickest device is the blackberry q10. Although we don’t have enough data to really tell why the blackberry q10 is unusually quick for a non-Apple device, one plausible guess is that it’s helped by having actual buttons, which are easier to implement with low latency than a touchscreen. The other two devices with actual buttons are the gameboy color and the kindle 4.

After that iphones and non-kindle button devices, we have a variety of Android devices of various ages. At the bottom, we have the ancient palm pilot 1000 followed by the kindles. The palm is hamstrung by a touchscreen and display created in an era with much slower touchscreen technology and the kindles use e-ink displays, which are much slower than the displays used on modern phones, so it’s not surprising to see those devices at the bottom.


Almost every computer and mobile device that people buy today is slower than common models of computers from the 70s and 80s. Low-latency gaming desktops and the ipad pro can get into the same range as quick machines from thirty to forty years ago, but most off-the-shelf devices aren’t even close.

If we had to pick one root cause of latency bloat, we might say that it’s because of “complexity”. Of course, we all know that complexity is bad. If you’ve been to a non-academic non-enterprise tech conference in the past decade, there’s a good chance that there was at least one talk on how complexity is the root of all evil and we should aspire to reduce complexity.

Unfortunately, it's a lot harder to remove complexity than to give a talk saying that we should remove complexity. A lot of the complexity buys us something, either directly or indirectly. When we looked at the input of a fancy modern keyboard vs. the apple 2 keyboard, we saw that using a relatively powerful and expensive general purpose processor to handle keyboard inputs can be slower than dedicated logic for the keyboard, which would both be simpler and cheaper. However, using the processor gives people the ability to easily customize the keyboard, and also pushes the problem of “programming” the keyboard from hardware into software, which reduces the cost of making the keyboard. The more expensive chip increases the manufacturing cost, but considering how much of the cost of these small-batch artisanal keyboards is the design cost, it seems like a net win to trade manufacturing cost for ease of programming.


If you want a reference to compare the kindle against, a moderately quick page turn in a physical book appears to be about 200 ms.

almost everything on computers is perceptually slower than it was in 1983
techtariat  dan-luu  performance  time  hardware  consumerism  objektbuch  data  history  reflection  critique  software  roots  tainter  engineering  nitty-gritty  ui  ux  hci  ios  mobile  apple  amazon  sequential  trends  increase-decrease  measure  analysis  measurement  os  systems  IEEE  intricacy  desktop  benchmarks  rant  carmack  system-design  degrees-of-freedom  keyboard  terminal  editors  links  input-output  networking  world  s:**  multi  twitter  social  discussion  tech  programming  web  internet  speed  backup  worrydream  interface  metal-to-virtual  latency-throughput  workflow  form-design  interface-compatibility 
july 2019 by nhaliday
Mutation testing - Wikipedia
Mutation testing involves modifying a program in small ways.[1] Each mutated version is called a mutant and tests detect and reject mutants by causing the behavior of the original version to differ from the mutant. This is called killing the mutant. Test suites are measured by the percentage of mutants that they kill. New tests can be designed to kill additional mutants.
wiki  reference  concept  mutation  selection  analogy  programming  checking  formal-methods  debugging  random  list  libraries  links  functional  haskell  javascript  jvm  c(pp)  python  dotnet  oop  perturbation  static-dynamic 
july 2019 by nhaliday
Why is Google Translate so bad for Latin? A longish answer. : latin
> All it does its correlate sequences of up to five consecutive words in texts that have been manually translated into two or more languages.
That sort of system ought to be perfect for a dead language, though. Dump all the Cicero, Livy, Lucretius, Vergil, and Oxford Latin Course into a database and we're good.

We're not exactly inundated with brand new Latin to translate.
> Dump all the Cicero, Livy, Lucretius, Vergil, and Oxford Latin Course into a database and we're good.
What makes you think that the Google folks haven't done so and used that to create the language models they use?
> That sort of system ought to be perfect for a dead language, though.
Perhaps. But it will be bad at translating novel English sentences to Latin.
foreign-lang  reddit  social  discussion  language  the-classics  literature  dataset  measurement  roots  traces  syntax  anglo  nlp  stackex  links  q-n-a  linguistics  lexical  deep-learning  sequential  hmm  project  arrows  generalization  state-of-art  apollonian-dionysian  machine-learning  google 
june 2019 by nhaliday
package writing - Where do I start LaTeX programming? - TeX - LaTeX Stack Exchange
I think there are three categories which need to be mastered (perhaps not all in the same degree) in order to become comfortable around TeX programming:

1. TeX programming. That's very basic, it deals with expansion control, counters, scopes, basic looping constructs and so on.

2. TeX typesetting. That's on a higher level, it includes control over boxes, lines, glues, modes, and perhaps about 1000 parameters.

3. Macro packages like LaTeX.
q-n-a  stackex  programming  latex  howto  nitty-gritty  yak-shaving  links  list  recommendations  books  guide  DSL 
may 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
Basic Error Rates
This page describes human error rates in a variety of contexts.

Most of the error rates are for mechanical errors. A good general figure for mechanical error rates appears to be about 0.5%.

Of course the denominator differs across studies. However only fairly simple actions are used in the denominator.

The Klemmer and Snyder study shows that much lower error rates are possible--in this case for people whose job consisted almost entirely of data entry.

The error rate for more complex logic errors is about 5%, based primarily on data on other pages, especially the program development page.
org:junk  list  links  objektbuch  data  database  error  accuracy  human-ml  machine-learning  ai  pro-rata  metrics  automation  benchmarks  marginal  nlp  language  density  writing  dataviz  meta:reading  speedometer 
may 2019 by nhaliday
Is backing up a MySQL database in Git a good idea? - Software Engineering Stack Exchange
*no: list of alternatives*

Top 2 answers contradict each other but both agree that you should at least version the schema and other scripts.

My impression is that the guy linked in the accepted answer is arguing for a minority practice.
q-n-a  stackex  programming  engineering  dbs  vcs  git  debate  critique  backup  best-practices  flux-stasis  nitty-gritty  gotchas  init  advice  code-organizing  multi  hmm  idk  contrarianism  rhetoric  links  system-design 
may 2019 by nhaliday
When to use C over C++, and C++ over C? - Software Engineering Stack Exchange
You pick C when
- you need portable assembler (which is what C is, really) for whatever reason,
- your platform doesn't provide C++ (a C compiler is much easier to implement),
- you need to interact with other languages that can only interact with C (usually the lowest common denominator on any platform) and your code consists of little more than the interface, not making it worth to lay a C interface over C++ code,
- you hack in an Open Source project (many of which, for various reasons, stick to C),
- you don't know C++.
In all other cases you should pick C++.


At the same time, I have to say that @Toll's answers (for one obvious example) have things just about backwards in most respects. Reasonably written C++ will generally be at least as fast as C, and often at least a little faster. Readability is generally much better, if only because you don't get buried in an avalanche of all the code for even the most trivial algorithms and data structures, all the error handling, etc.


As it happens, C and C++ are fairly frequently used together on the same projects, maintained by the same people. This allows something that's otherwise quite rare: a study that directly, objectively compares the maintainability of code written in the two languages by people who are equally competent overall (i.e., the exact same people). At least in the linked study, one conclusion was clear and unambiguous: "We found that using C++ instead of C results in improved software quality and reduced maintenance effort..."


(Side-note: Check out Linus Torvads' rant on why he prefers C to C++. I don't necessarily agree with his points, but it gives you insight into why people might choose C over C++. Rather, people that agree with him might choose C for these reasons.)


Why would anybody use C over C++? [closed]: https://stackoverflow.com/questions/497786/why-would-anybody-use-c-over-c
Joel's answer is good for reasons you might have to use C, though there are a few others:
- You must meet industry guidelines, which are easier to prove and test for in C.
- You have tools to work with C, but not C++ (think not just about the compiler, but all the support tools, coverage, analysis, etc)
- Your target developers are C gurus
- You're writing drivers, kernels, or other low level code
- You know the C++ compiler isn't good at optimizing the kind of code you need to write
- Your app not only doesn't lend itself to be object oriented, but would be harder to write in that form

In some cases, though, you might want to use C rather than C++:
- You want the performance of assembler without the trouble of coding in assembler (C++ is, in theory, capable of 'perfect' performance, but the compilers aren't as good at seeing optimizations a good C programmer will see)
- The software you're writing is trivial, or nearly so - whip out the tiny C compiler, write a few lines of code, compile and you're all set - no need to open a huge editor with helpers, no need to write practically empty and useless classes, deal with namespaces, etc. You can do nearly the same thing with a C++ compiler and simply use the C subset, but the C++ compiler is slower, even for tiny programs.
- You need extreme performance or small code size, and know the C++ compiler will actually make it harder to accomplish due to the size and performance of the libraries
- You contend that you could just use the C subset and compile with a C++ compiler, but you'll find that if you do that you'll get slightly different results depending on the compiler.

Regardless, if you're doing that, you're using C. Is your question really "Why don't C programmers use C++ compilers?" If it is, then you either don't understand the language differences, or you don't understand compiler theory.


- Because they already know C
- Because they're building an embedded app for a platform that only has a C compiler
- Because they're maintaining legacy software written in C
- You're writing something on the level of an operating system, a relational database engine, or a retail 3D video game engine.
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may 2019 by nhaliday
AFL + QuickCheck = ?
Adventures in fuzzing. Also differences between testing culture in software and hardware.
techtariat  dan-luu  programming  engineering  checking  random  haskell  path-dependence  span-cover  heuristic  libraries  links  tools  devtools  software  hardware  culture  formal-methods  local-global  golang  correctness  methodology 
may 2019 by nhaliday
Delta debugging - Wikipedia
good overview of with examples: https://www.csm.ornl.gov/~sheldon/bucket/Automated-Debugging.pdf

Not as useful for my usecases (mostly contest programming) as QuickCheck. Input is generally pretty structured and I don't have a long history of code in VCS. And when I do have the latter git-bisect is probably enough.

good book tho: http://www.whyprogramsfail.com/toc.php
WHY PROGRAMS FAIL: A Guide to Systematic Debugging\
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may 2019 by nhaliday
Links 3/19: Linkguini | Slate Star Codex
How did the descendants of the Mayan Indians end up in the Eastern Orthodox Church?

Does Parental Quality Matter? Study using three sources of parental variation that are mostly immune to genetic confounding find that “the strong parent-child correlation in education is largely causal”. For example, “the parent-child correlation in education is stronger with the parent that spends more time with the child”.

Before and after pictures of tech leaders like Jeff Bezos, Elon Musk, and Sergey Brin suggest they’re taking supplemental testosterone. And though it may help them keep looking young, Palladium points out that there might be other effects from having some of our most powerful businessmen on a hormone that increases risk-taking and ambition. They ask whether the new availability of testosterone supplements is prolonging Silicon Valley businessmen’s “brash entrepreneur” phase well past the point where they would normally become mature respectable elders. But it also hints at an almost opposite take: average testosterone levels have been falling for decades, so at this point these businessmen would be the only “normal” (by 1950s standards) men out there, and everyone else would be unprecedently risk-averse and boring. Paging Peter Thiel and everyone else who takes about how things “just worked better” in Eisenhower’s day.

China’s SesameCredit social monitoring system, widely portrayed as dystopian, has an 80% approval rate in China (vs. 19% neutral and 1% disapproval). The researchers admit that although all data is confidential and they are not affiliated with the Chinese government, their participants might not believe that confidently enough to answer honestly.

I know how much you guys love attacking EAs for “pathological altruism” or whatever terms you’re using nowadays, so here’s an article where rationalist community member John Beshir describes his experience getting malaria on purpose to help researchers test a vaccine.

Some evidence against the theory that missing fathers cause earlier menarche.

John Nerst of EverythingStudies’ political compass.
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march 2019 by nhaliday
Jordan Peterson is Wrong About the Case for the Left
I suggest that the tension of which he speaks is fully formed and self-contained completely within conservatism. Balancing those two forces is, in fact, what conservatism is all about. Thomas Sowell, in A Conflict of Visions: Ideological Origins of Political Struggles describes the conservative outlook as (paraphrasing): “There are no solutions, only tradeoffs.”

The real tension is between balance on the right and imbalance on the left.

In Towards a Cognitive Theory of Polics in the online magazine Quillette I make the case that left and right are best understood as psychological profiles consisting of 1) cognitive style, and 2) moral matrix.

There are two predominant cognitive styles and two predominant moral matrices.

The two cognitive styles are described by Arthur Herman in his book The Cave and the Light: Plato Versus Aristotle, and the Struggle for the Soul of Western Civilization, in which Plato and Aristotle serve as metaphors for them. These two quotes from the book summarize the two styles:

Despite their differences, Plato and Aristotle agreed on many things. They both stressed the importance of reason as our guide for understanding and shaping the world. Both believed that our physical world is shaped by certain eternal forms that are more real than matter. The difference was that Plato’s forms existed outside matter, whereas Aristotle’s forms were unrealizable without it. (p. 61)

The twentieth century’s greatest ideological conflicts do mark the violent unfolding of a Platonist versus Aristotelian view of what it means to be free and how reason and knowledge ultimately fit into our lives (p.539-540)

The Platonic cognitive style amounts to pure abstract reason, “unconstrained” by reality. It has no limiting principle. It is imbalanced. Aristotelian thinking also relies on reason, but it is “constrained” by empirical reality. It has a limiting principle. It is balanced.

The two moral matrices are described by Jonathan Haidt in his book The Righteous Mind: Why Good People Are Divided by Politics and Religion. Moral matrices are collections of moral foundations, which are psychological adaptations of social cognition created in us by hundreds of millions of years of natural selection as we evolved into the social animal. There are six moral foundations. They are:

The first three moral foundations are called the “individualizing” foundations because they’re focused on the autonomy and well being of the individual person. The second three foundations are called the “binding” foundations because they’re focused on helping individuals form into cooperative groups.

One of the two predominant moral matrices relies almost entirely on the individualizing foundations, and of those mostly just care. It is all individualizing all the time. No balance. The other moral matrix relies on all of the moral foundations relatively equally; individualizing and binding in tension. Balanced.

The leftist psychological profile is made from the imbalanced Platonic cognitive style in combination with the first, imbalanced, moral matrix.

The conservative psychological profile is made from the balanced Aristotelian cognitive style in combination with the balanced moral matrix.

It is not true that the tension between left and right is a balance between the defense of the dispossessed and the defense of hierarchies.

It is true that the tension between left and right is between an imbalanced worldview unconstrained by empirical reality and a balanced worldview constrained by it.

A Venn Diagram of the two psychological profiles looks like this:
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july 2018 by nhaliday
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