physicsgraph

There are about article 500 submissions per day to arxiv [0]. I don't know what the number of monthly active users is for scirate.com, but the density of comments might be expected to be low.

[0] https://arxiv.org/stats/monthly_submissions

While reputation might be one allure, getting questions about a paper answered (even if not by the author) is helpful. Pointing out fallacies or holes-in-logic in a paper might be a poor investment of time given the number of papers to comment on.

A grander vision of gathering feedback as part of a normal process seems desirable, but I don't know that the incentives for stakeholders are present to enable that.

scirate.com was one of four options I found for comments on arxiv papers [0]; the other three being researchgate.net, pubpeer.com, and arxiv-sanity.com/discussions

Of the four, scirate.com seems the best option (unless you're focused on machine learning -- then arxiv-sanity is the winner).

I agree that comments on arxiv papers are "unlikely to take off" in either a commercial money-making sense or a "grow as large and popular as HackerNews" sense.

[0] https://graphthinking.blogspot.com/2021/12/comments-on-arxiv...

I wasn't able to get a working copy of the Chrome extension https://fermatslibrary.com/librarian; not sure if it's unsupported, unavailable, or there's some other cause. I also do not find "librarian" available in the Chrome listing of extensions https://chrome.google.com/webstore/category/extensions.

"you can negotiate anything" by Herb Cohen

https://en.m.wikipedia.org/wiki/You_Can_Negotiate_Anything

Semantic enrichment of scientific literature seems promising as a way of unlocking the huge amount of knowledge in published documents (e.g., articles, textbooks). However, getting the enrichment process right takes a lot of effort and the enriched data doesn't provide sufficient value differentiation to 1) be worth the investment and 2) be commercially viable.

This author focuses bio-sciences; I've been collecting a list of similar efforts in Physics [0]. Those who can't do teach, and those who can't teach decorate data with semantic features.

[0] https://derivationmap.net/other_projects

I'm not sure I follow what you're proposing, but in case it is tangentially related to a project I work on, I'll describe what I did.

Write down every quantitative expression in Mathematical Physics. Assign a unique numerical ID to each expression. Identify the relations between each of the expressions to create a graph. Does that graph feature any patterns? Is there a path from any expression to every other expression?

The proposal seems to be that software design guidance correspond to existing hierarchical organization structure. A formalization of Conway's Law [0]. The more people in the hierarchy that coordination touches, the looser the coupling.

[0] https://en.wikipedia.org/wiki/Conway%27s_law

Smart people are good at pattern identification and making predictions based on the perceived patterns. The problem is when pattern identification is not based on evidence. The evidence gathering process is where curiosity is more relevant.

The Wikipedia page [0] mentions the collection of 5,300 mustards. I was underwhelmed by the 30 presented in this article.

[0] https://en.wikipedia.org/wiki/National_Mustard_Museum

Thanks for the links. Sounds like an audacious effort.

Pivots away from the current approach are very difficult. Even minor deviations like https://arxiv.org/ take decades to gain "normal" status in the science community.

Enabling reproducibility is a costly investment, but the cost of doing unreproducible work is even higher. This is a calculation I rarely see organization make.

More frequently everything is a one-off, with failure recurring randomly until a working (though not reproducible) approach is found. I'm not sure how to spin this evolutionary approach positively, as it can result in fragile outcomes.

There is software infrastructure that enables reproducibility (virtual machines, and more recently containerization), but this concept rarely makes it to written documentation.

For people not familiar with Rickover, videos of his interviews like [0] are useful for understanding his personality.

[0] https://vimeo.com/56270169

Similar pages: [0,1,2] and my own list, [3]

[0] https://github.com/LappleApple/awesome-leading-and-managing

[1] https://github.com/kdeldycke/awesome-engineering-team-manage...

[2] https://github.com/ankitjaininfo/awesome-managers

[3] https://graphthinking.blogspot.com/2021/04/reading-list-for-...

Seems to be of the "awesome list" variety [0]. Or is this specific to HNews?

[0] https://github.com/topics/awesome-list

The Dockerization of your samples makes the repo more useful. I don't want to have to install every language on my laptop.

Whenever I read about a novice practitioner having a Kundalini experience, I wonder how experienced the retreat host(s) are. Or if the person had a mentor with sufficient experience.

While there are many spiritual interpretations, there is also practical advice online for recovering. Walking outside (experiencing nature) is commonly suggested.

Accidentally stumbling into this disorienting experience could be (and in the case of this author, was) traumatic.

Wikipedia has a good summary [0] and [1].

[0] https://en.wikipedia.org/wiki/History_of_KFC#Heublein_and_st...

[1] https://en.wikipedia.org/wiki/Colonel_Sanders#1930%E2%80%931...

How is this different than "wealth begets wealth"? If money to computation costs money, then equity of AI access is a subset of financial equity.

For context, I've started collection a list of books that would be relevant to my coworkers [0] as well as a list of discussion questions [1].

[0] https://graphthinking.blogspot.com/2021/04/reading-list-for-...

[1] https://graphthinking.blogspot.com/2021/07/questions-for-non...

Probably worth triaging which topics are recurring to perform a meta-analysis. See my comment elsewhere in this thread.

I agree that this type of compendium/synthesis is useful. I was amused to open this thread and see comments focused on visual programming rather than the meta-aspect.

As for what HN could be mined for, a big topic is software languages. There are ways to subdivide that: by language, by optimization aspect (e.g., comparative performance). Geographic topics like cost of living and quality of life. Remote work pros and cons.

Another way of identifying topics worth mining would be identify posts where @dang provides a link to previous posts and those previous posts have a sufficient number of comments.

I have a different approach for create a similar outcome: everyone rotates projects on a periodic basis. The period of change is longer than a sprint duration so as to give people time to acclimate to the new-to-them project.

[0] https://graphthinking.blogspot.com/2021/06/periodic-rotation...

"You can negotiate anything" by Herb Cohen.

I learned that every conversation is a negotiation. Seeking to understand the position the other party is starting from, and seeing where everyone wants to go, reframed how I view interactions.

[0] https://en.wikipedia.org/wiki/You_Can_Negotiate_Anything

Knowledge graphs for text (the focus of the article) seem narrowly-scoped since they require "objective" facts and relations to be practical. Capturing the subjective and transient perspective of observations made by multiple observers (which is what we actually have access to) is more complicated.

For example, asking the same person the same question may yield different answers based on their mood or other environmental or situational factors. Who's asking the question can also matter, as does the specific phrasing of the question.

The Overleaf source code is available online [0], so you could even run your own instance.

[0] https://github.com/overleaf/overleaf

This post is a good example of the initial realization that folks experience with graphs as data structures. I can see author exclaiming, "Graphs are an intuitive way to express knowledge!" Graphs as data structures can be more nuanced than is usually discussed.

Hypernode graphs seem to be very applicable to knowledge graphs, though they have different names depending on the discipline -- nested state machines, statecharts [1], UML hierarchically nested states [2].

I recently participated in a lecture series on Applied Category Theory [3] that touched on the underlying math of composition. The course focused on optimization of design choices in complex decision spaces.

[1] https://statecharts.github.io/

[2] https://en.wikipedia.org/wiki/UML_state_machine#Hierarchical...

[3] https://applied-compositional-thinking.engineering/lectures/

I agree with your suggestion conceptually, but the starting point of a decision tree strongly depends on many factors: which version of the software is being used, how the software was compiled, running with what OS, which what patches, on what hardware, under what environmental conditions, in support of what application usage patterns, and what load, etc. Merely capturing the (potentially) relevant input conditions becomes challenging, never mind the process of eliminating irrelevant variables. And that's all premised on the concept that a problem is recurring (rather than some fluke that no one else has encountered).

I think that's why Stack Overflow websites focused on shallow conditions flourish -- the deep dives are usually specific to a given situation.

The block coding interface looks smooth. I'm curious about how the interface scales for complex projects.

The documentation integration is nice.

Great work!

As a counter example to the pessimism in this essay, an application of the Semantic web is the NASA EVA Wiki [0] which uses Semantic MediaWiki. Metrics on utilization are available [1].

[0] https://www.semantic-mediawiki.org/wiki/EVA_Wiki

[1] https://www.mediawiki.org/wiki/5_Year_NASA_Wikiversary