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Ask HN: Favorite note-taking software? | Hacker News
Ask HN: What is your ideal note-taking software and/or hardware?:

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:
- 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 (, Polar (, Roam (looks very promising)

Ask HN: What do you use for you personal note taking activity?:

Ask HN: What are your note-taking techniques?:

Ask HN: How do you take notes (useful note-taking strategies)?:

Ask HN: How to get better at taking notes?:

Ask HN: How do you keep your notes organized?:

Ask HN: How did you build up your personal knowledge base?:
nice comment from math guy on structure and difference between math and CS:
useful comment collating related discussions:
Designing a Personal Knowledge base:
Ask HN: How to organize personal knowledge?:
Do you use a personal 'knowledge base'?:
Ask HN: How do you share/organize knowledge at work and life?:
Managing my personal knowledge base:
The sad state of personal data and infrastructure:
Building personal search infrastructure for your knowledge and code:

How to annotate literally everything:
Ask HN: How do you organize document digests / personal knowledge?:
Ask HN: Good solution for storing notes/excerpts from books?:
Ask HN: What's your cross-platform pdf / ePub reading workflow?:
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?:

other stuff:
plain text:
Tiago Forte:

hn search:

Slant comparison commentary:

good comparison of options here in comments here (and Trilium itself looks good):

stuff from Andy Matuschak and Michael Nielsen on general note-taking:
Software interfaces undervalue peripheral vision! (a thread)
This morning I implemented PageRank to sort backlinks in my prototype note system. Mixed results!
One way to dream up post-book media to make reading more effective and meaningful is to systematize "expert" practices (e.g. How to Read a Book), so more people can do them, more reliably and more cheaply. But… the most erudite people I know don't actually do those things!

the memex essay and comments from various people including Andy on it:

some more stuff specific to Roam below, and cf "Why books don't work":


Knowledge systems which display contextual backlinks to a node open up an interesting new behavior. You can bootstrap a new node extensionally (rather than intensionally) by simply linking to it from many other nodes—even before it has any content.
Curious: what are the most striking public @RoamResearch pages that you know? I'd like to see examples of people using it for interesting purposes, or in interesting ways.
If I weren't doing my own research on questions in knowledge systems (which necessitates tinkering with my own), and if I weren't allergic to doing serious work in webapps, I'd likely use Roam instead!
interesting app:

intriguing but probably not appropriate for my needs:



one comment links to this, mostly on Notion:

Leo Editor (combines tree outlining w/ literate programming/scripting, I think?):


Coda mentioned

maybe not the best source for a review/advice

interesting comment(s) about tree outliners and spreadsheets:

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october 2019 by nhaliday
Choose the best - Slant
I've noticed I fairly often agree w/ the rankings from this (at least when they show up in my search results). more accurate than I would've expected
organization  community  aggregator  data  database  search  review  software  tools  devtools  app  recommendations  ranking  list  top-n  workflow  track-record  saas  tech-infrastructure  consumerism  hardware  sleuthin  judgement  comparison 
october 2019 by nhaliday
The Future of Mathematics? [video] | Hacker News
Kevin Buzzard (the Lean guy)

- general reflection on proof asssistants/theorem provers
- Kevin Hale's formal abstracts project, etc
- thinks of available theorem provers, Lean is "[the only one currently available that may be capable of formalizing all of mathematics eventually]" (goes into more detail right at the end, eg, quotient types)
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october 2019 by nhaliday
Amazon Products Visualization - YASIV
based off a single test run, this works really well, at least for popular books (all I was interested in at the time)
tools  search  recommendations  consumerism  books  aggregator  exploratory  let-me-see  network-structure  amazon  similarity  graphs  visualization 
july 2019 by nhaliday
Cleaner, more elegant, and harder to recognize | The Old New Thing
Really easy
Writing bad error-code-based code
Writing bad exception-based code

Writing good error-code-based code

Really hard
Writing good exception-based code


Really easy
Recognizing that error-code-based code is badly-written
Recognizing the difference between bad error-code-based code and
not-bad error-code-based code.

Recognizing that error-code-base code is not badly-written

Really hard
Recognizing that exception-based code is badly-written
Recognizing that exception-based code is not badly-written
Recognizing the difference between bad exception-based code
and not-bad exception-based code
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july 2019 by nhaliday
What's the expected level of paper for top conferences in Computer Science - Academia Stack Exchange
Top. The top level.

My experience on program committees for STOC, FOCS, ITCS, SODA, SOCG, etc., is that there are FAR more submissions of publishable quality than can be accepted into the conference. By "publishable quality" I mean a well-written presentation of a novel, interesting, and non-trivial result within the scope of the conference.


There are several questions that come up over and over in the FOCS/STOC review cycle:

- How surprising / novel / elegant / interesting is the result?
- How surprising / novel / elegant / interesting / general are the techniques?
- How technically difficult is the result? Ironically, FOCS and STOC committees have a reputation for ignoring the distinction between trivial (easy to derive from scratch) and nondeterministically trivial (easy to understand after the fact).
- What is the expected impact of this result? Is this paper going to change the way people do theoretical computer science over the next five years?
- Is the result of general interest to the theoretical computer science community? Or is it only of interest to a narrow subcommunity? In particular, if the topic is outside the STOC/FOCS mainstream—say, for example, computational topology—does the paper do a good job of explaining and motivating the results to a typical STOC/FOCS audience?
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june 2019 by nhaliday
How much would it cost to crawl 1 billion sites using rented AWS servers/bandwidth? - Quora
The best way IMHO to do such a crawl would be to recruit a group of say 100-1000 of your friends, and their friends, and write a simple distributed app running in background on their machines, when they sit idle or are lightly used. This way you will be amortizing their monthly broadband bills, with their monthly quotas (e.g. Comcast 250GB) largely unused anyway. I would think that you can get dozens of Mbps of cross bandwidth in such a network, which could do the job in a matter of months.

BTW, if you really meant 1 billion sites, as opposed to pages, multiply the above bills by 100x (average number of pages per site).


There is no need for you to crawl. Someone has already done the job for you. Common Crawl is a periodic crawl of the internet, and the results are stored in Amazon S3. You can directly use the results without any charge for any kink of analysis you want to do.
q-n-a  qra  quixotic  programming  engineering  search  minimum-viable  internet  web  huge-data-the-biggest  howto  init  advice  money  cost-benefit  strategy  scaling-tech  system-design  move-fast-(and-break-things) 
may 2019 by nhaliday
Delta debugging - Wikipedia
good overview of with examples:

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:
WHY PROGRAMS FAIL: A Guide to Systematic Debugging\
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may 2019 by nhaliday
Perseus Digital Library
This is actually really useful.

- Load English translation side-by-side if available.
- Click on any word and see the best guess for definition+inflection given context.

this interface allows search by lemma/POS:
tools  reference  history  iron-age  mediterranean  the-classics  canon  foreign-lang  linguistics  database  quixotic  stoic  syntax  lexical  exocortex  aggregator  search  multi 
february 2019 by nhaliday
Citizendium, the Citizens' Compendium
That wikipedia alternative by the nerdy spurned co-founder of Jimmy Wales (Larry Sanger). Unfortunately looks rather empty.
wiki  reference  database  search  comparison  organization  duplication  socs-and-mops  the-devil  god-man-beast-victim  guilt-shame 
november 2018 by nhaliday
Theories of humor - Wikipedia
There are many theories of humor which attempt to explain what humor is, what social functions it serves, and what would be considered humorous. Among the prevailing types of theories that attempt to account for the existence of humor, there are psychological theories, the vast majority of which consider humor to be very healthy behavior; there are spiritual theories, which consider humor to be an inexplicable mystery, very much like a mystical experience.[1] Although various classical theories of humor and laughter may be found, in contemporary academic literature, three theories of humor appear repeatedly: relief theory, superiority theory, and incongruity theory.[2] Among current humor researchers, there is no consensus about which of these three theories of humor is most viable.[2] Proponents of each one originally claimed their theory to be capable of explaining all cases of humor.[2][3] However, they now acknowledge that although each theory generally covers its own area of focus, many instances of humor can be explained by more than one theory.[2][3][4][5] Incongruity and superiority theories, for instance, seem to describe complementary mechanisms which together create humor.[6]


Relief theory
Relief theory maintains that laughter is a homeostatic mechanism by which psychological tension is reduced.[2][3][7] Humor may thus for example serve to facilitate relief of the tension caused by one's fears.[8] Laughter and mirth, according to relief theory, result from this release of nervous energy.[2] Humor, according to relief theory, is used mainly to overcome sociocultural inhibitions and reveal suppressed desires. It is believed that this is the reason we laugh whilst being tickled, due to a buildup of tension as the tickler "strikes".[2][9] According to Herbert Spencer, laughter is an "economical phenomenon" whose function is to release "psychic energy" that had been wrongly mobilized by incorrect or false expectations. The latter point of view was supported also by Sigmund Freud.

Superiority theory
The superiority theory of humor traces back to Plato and Aristotle, and Thomas Hobbes' Leviathan. The general idea is that a person laughs about misfortunes of others (so called schadenfreude), because these misfortunes assert the person's superiority on the background of shortcomings of others.[10] Socrates was reported by Plato as saying that the ridiculous was characterized by a display of self-ignorance.[11] For Aristotle, we laugh at inferior or ugly individuals, because we feel a joy at feeling superior to them.[12]

Incongruous juxtaposition theory
The incongruity theory states that humor is perceived at the moment of realization of incongruity between a concept involved in a certain situation and the real objects thought to be in some relation to the concept.[10]

Since the main point of the theory is not the incongruity per se, but its realization and resolution (i.e., putting the objects in question into the real relation), it is often called the incongruity-resolution theory.[10]


Detection of mistaken reasoning
In 2011, three researchers, Hurley, Dennett and Adams, published a book that reviews previous theories of humor and many specific jokes. They propose the theory that humor evolved because it strengthens the ability of the brain to find mistakes in active belief structures, that is, to detect mistaken reasoning.[46] This is somewhat consistent with the sexual selection theory, because, as stated above, humor would be a reliable indicator of an important survival trait: the ability to detect mistaken reasoning. However, the three researchers argue that humor is fundamentally important because it is the very mechanism that allows the human brain to excel at practical problem solving. Thus, according to them, humor did have survival value even for early humans, because it enhanced the neural circuitry needed to survive.

Misattribution theory
Misattribution is one theory of humor that describes an audience's inability to identify exactly why they find a joke to be funny. The formal theory is attributed to Zillmann & Bryant (1980) in their article, "Misattribution Theory of Tendentious Humor", published in Journal of Experimental Social Psychology. They derived the critical concepts of the theory from Sigmund Freud's Wit and Its Relation to the Unconscious (note: from a Freudian perspective, wit is separate from humor), originally published in 1905.

Benign violation theory
The benign violation theory (BVT) is developed by researchers A. Peter McGraw and Caleb Warren.[47] The BVT integrates seemingly disparate theories of humor to predict that humor occurs when three conditions are satisfied: 1) something threatens one's sense of how the world "ought to be", 2) the threatening situation seems benign, and 3) a person sees both interpretations at the same time.

From an evolutionary perspective, humorous violations likely originated as apparent physical threats, like those present in play fighting and tickling. As humans evolved, the situations that elicit humor likely expanded from physical threats to other violations, including violations of personal dignity (e.g., slapstick, teasing), linguistic norms (e.g., puns, malapropisms), social norms (e.g., strange behaviors, risqué jokes), and even moral norms (e.g., disrespectful behaviors). The BVT suggests that anything that threatens one's sense of how the world "ought to be" will be humorous, so long as the threatening situation also seems benign.


Sense of humor, sense of seriousness
One must have a sense of humor and a sense of seriousness to distinguish what is supposed to be taken literally or not. An even more keen sense is needed when humor is used to make a serious point.[48][49] Psychologists have studied how humor is intended to be taken as having seriousness, as when court jesters used humor to convey serious information. Conversely, when humor is not intended to be taken seriously, bad taste in humor may cross a line after which it is taken seriously, though not intended.[50]

Philosophy of humor bleg:

Inside Jokes:
humor as reward for discovering inconsistency in inferential chain
People of all ages and cultures respond to humour. Most people are able to experience humour—be amused, smile or laugh at something funny—and thus are considered to have a sense of humour. The hypothetical person lacking a sense of humour would likely find the behaviour inducing it to be inexplicable, strange, or even irrational.


Ancient Greece
Western humour theory begins with Plato, who attributed to Socrates (as a semi-historical dialogue character) in the Philebus (p. 49b) the view that the essence of the ridiculous is an ignorance in the weak, who are thus unable to retaliate when ridiculed. Later, in Greek philosophy, Aristotle, in the Poetics (1449a, pp. 34–35), suggested that an ugliness that does not disgust is fundamental to humour.


Confucianist Neo-Confucian orthodoxy, with its emphasis on ritual and propriety, has traditionally looked down upon humour as subversive or unseemly. The Confucian "Analects" itself, however, depicts the Master as fond of humorous self-deprecation, once comparing his wanderings to the existence of a homeless dog.[10] Early Daoist philosophical texts such as "Zhuangzi" pointedly make fun of Confucian seriousness and make Confucius himself a slow-witted figure of fun.[11] Joke books containing a mix of wordplay, puns, situational humor, and play with taboo subjects like sex and scatology, remained popular over the centuries. Local performing arts, storytelling, vernacular fiction, and poetry offer a wide variety of humorous styles and sensibilities.


Physical attractiveness
90% of men and 81% of women, all college students, report having a sense of humour is a crucial characteristic looked for in a romantic partner.[21] Humour and honesty were ranked as the two most important attributes in a significant other.[22] It has since been recorded that humour becomes more evident and significantly more important as the level of commitment in a romantic relationship increases.[23] Recent research suggests expressions of humour in relation to physical attractiveness are two major factors in the desire for future interaction.[19] Women regard physical attractiveness less highly compared to men when it came to dating, a serious relationship, and sexual intercourse.[19] However, women rate humorous men more desirable than nonhumorous individuals for a serious relationship or marriage, but only when these men were physically attractive.[19]

Furthermore, humorous people are perceived by others to be more cheerful but less intellectual than nonhumorous people. Self-deprecating humour has been found to increase the desirability of physically attractive others for committed relationships.[19] The results of a study conducted by McMaster University suggest humour can positively affect one’s desirability for a specific relationship partner, but this effect is only most likely to occur when men use humour and are evaluated by women.[24] No evidence was found to suggest men prefer women with a sense of humour as partners, nor women preferring other women with a sense of humour as potential partners.[24] When women were given the forced-choice design in the study, they chose funny men as potential … [more]
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april 2018 by nhaliday
The Hanson-Yudkowsky AI-Foom Debate - Machine Intelligence Research Institute
How Deviant Recent AI Progress Lumpiness?:
I seem to disagree with most people working on artificial intelligence (AI) risk. While with them I expect rapid change once AI is powerful enough to replace most all human workers, I expect this change to be spread across the world, not concentrated in one main localized AI system. The efforts of AI risk folks to design AI systems whose values won’t drift might stop global AI value drift if there is just one main AI system. But doing so in a world of many AI systems at similar abilities levels requires strong global governance of AI systems, which is a tall order anytime soon. Their continued focus on preventing single system drift suggests that they expect a single main AI system.

The main reason that I understand to expect relatively local AI progress is if AI progress is unusually lumpy, i.e., arriving in unusually fewer larger packages rather than in the usual many smaller packages. If one AI team finds a big lump, it might jump way ahead of the other teams.

However, we have a vast literature on the lumpiness of research and innovation more generally, which clearly says that usually most of the value in innovation is found in many small innovations. We have also so far seen this in computer science (CS) and AI. Even if there have been historical examples where much value was found in particular big innovations, such as nuclear weapons or the origin of humans.

Apparently many people associated with AI risk, including the star machine learning (ML) researchers that they often idolize, find it intuitively plausible that AI and ML progress is exceptionally lumpy. Such researchers often say, “My project is ‘huge’, and will soon do it all!” A decade ago my ex-co-blogger Eliezer Yudkowsky and I argued here on this blog about our differing estimates of AI progress lumpiness. He recently offered Alpha Go Zero as evidence of AI lumpiness:


In this post, let me give another example (beyond two big lumps in a row) of what could change my mind. I offer a clear observable indicator, for which data should have available now: deviant citation lumpiness in recent ML research. One standard measure of research impact is citations; bigger lumpier developments gain more citations that smaller ones. And it turns out that the lumpiness of citations is remarkably constant across research fields! See this March 3 paper in Science:

I Still Don’t Get Foom:
All of which makes it look like I’m the one with the problem; everyone else gets it. Even so, I’m gonna try to explain my problem again, in the hope that someone can explain where I’m going wrong. Here goes.

“Intelligence” just means an ability to do mental/calculation tasks, averaged over many tasks. I’ve always found it plausible that machines will continue to do more kinds of mental tasks better, and eventually be better at pretty much all of them. But what I’ve found it hard to accept is a “local explosion.” This is where a single machine, built by a single project using only a tiny fraction of world resources, goes in a short time (e.g., weeks) from being so weak that it is usually beat by a single human with the usual tools, to so powerful that it easily takes over the entire world. Yes, smarter machines may greatly increase overall economic growth rates, and yes such growth may be uneven. But this degree of unevenness seems implausibly extreme. Let me explain.

If we count by economic value, humans now do most of the mental tasks worth doing. Evolution has given us a brain chock-full of useful well-honed modules. And the fact that most mental tasks require the use of many modules is enough to explain why some of us are smarter than others. (There’d be a common “g” factor in task performance even with independent module variation.) Our modules aren’t that different from those of other primates, but because ours are different enough to allow lots of cultural transmission of innovation, we’ve out-competed other primates handily.

We’ve had computers for over seventy years, and have slowly build up libraries of software modules for them. Like brains, computers do mental tasks by combining modules. An important mental task is software innovation: improving these modules, adding new ones, and finding new ways to combine them. Ideas for new modules are sometimes inspired by the modules we see in our brains. When an innovation team finds an improvement, they usually sell access to it, which gives them resources for new projects, and lets others take advantage of their innovation.


In Bostrom’s graph above the line for an initially small project and system has a much higher slope, which means that it becomes in a short time vastly better at software innovation. Better than the entire rest of the world put together. And my key question is: how could it plausibly do that? Since the rest of the world is already trying the best it can to usefully innovate, and to abstract to promote such innovation, what exactly gives one small project such a huge advantage to let it innovate so much faster?


In fact, most software innovation seems to be driven by hardware advances, instead of innovator creativity. Apparently, good ideas are available but must usually wait until hardware is cheap enough to support them.

Yes, sometimes architectural choices have wider impacts. But I was an artificial intelligence researcher for nine years, ending twenty years ago, and I never saw an architecture choice make a huge difference, relative to other reasonable architecture choices. For most big systems, overall architecture matters a lot less than getting lots of detail right. Researchers have long wandered the space of architectures, mostly rediscovering variations on what others found before.

Some hope that a small project could be much better at innovation because it specializes in that topic, and much better understands new theoretical insights into the basic nature of innovation or intelligence. But I don’t think those are actually topics where one can usefully specialize much, or where we’ll find much useful new theory. To be much better at learning, the project would instead have to be much better at hundreds of specific kinds of learning. Which is very hard to do in a small project.

What does Bostrom say? Alas, not much. He distinguishes several advantages of digital over human minds, but all software shares those advantages. Bostrom also distinguishes five paths: better software, brain emulation (i.e., ems), biological enhancement of humans, brain-computer interfaces, and better human organizations. He doesn’t think interfaces would work, and sees organizations and better biology as only playing supporting roles.


Similarly, while you might imagine someday standing in awe in front of a super intelligence that embodies all the power of a new age, superintelligence just isn’t the sort of thing that one project could invent. As “intelligence” is just the name we give to being better at many mental tasks by using many good mental modules, there’s no one place to improve it. So I can’t see a plausible way one project could increase its intelligence vastly faster than could the rest of the world.

Takeoff speeds:
Futurists have argued for years about whether the development of AGI will look more like a breakthrough within a small group (“fast takeoff”), or a continuous acceleration distributed across the broader economy or a large firm (“slow takeoff”).

I currently think a slow takeoff is significantly more likely. This post explains some of my reasoning and why I think it matters. Mostly the post lists arguments I often hear for a fast takeoff and explains why I don’t find them compelling.

(Note: this is not a post about whether an intelligence explosion will occur. That seems very likely to me. Quantitatively I expect it to go along these lines. So e.g. while I disagree with many of the claims and assumptions in Intelligence Explosion Microeconomics, I don’t disagree with the central thesis or with most of the arguments.)
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april 2018 by nhaliday
Forgotten Books
"read old books"

they have a copy of G.M. Cookson's Aeschylus translations
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november 2017 by nhaliday
Peer review is younger than you think - Marginal REVOLUTION
I’d like to see a detailed look at actual journal practices, but my personal sense is that editorial review was the norm until fairly recently, not review by a team of outside referees.  In 1956, for instance, the American Historical Review asked for only one submission copy, and it seems the same was true as late as 1970.  I doubt they made the photocopies themselves. Schmidt seems to suggest that the practices of government funders nudged the academic professions into more formal peer review with multiple referee reports.
econotariat  marginal-rev  commentary  data  gbooks  trends  anglo  language  zeitgeist  search  history  mostly-modern  science  meta:science  institutions  academia  publishing  trivia  cocktail  links 
september 2017 by nhaliday
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