- jaimieDoes it though? I mean I'm still teaching thread-safety and recursion to my interns... a solid foundation is a solid foundation.
- This was a very well written retrospective on web development. Thank you for sharing!
- The world of the Digital Humanities is a lot of fun (and one I've been a part of, teaching programming to Historians and Philosophers of Science!) It uses computation to provide new types of evidence for historical or rhetorical arguments and data-driven critiques. There's an art to it as well, showing evidence for things like multiple interpretations of a text through the stochasticity of various text extraction models.
From the author's about page:
> I discovered digital humanities (“humanities computing,” as it was then called) while I was a graduate student at the University of Virginia in the mid-nineties. I found the whole thing very exciting, but felt that before I could get on to things like computational text analysis and other kinds of humanistic geekery, I needed to work through a set of thorny philosophical problems. Is there such a thing as “algorithmic” literary criticism? Is there a distinct, humanistic form of visualization that differs from its scientific counterpart? What does it mean to “read” a text with a machine? Computational analysis of the human record seems to imply a different conception of hermeneutics, but what is that new conception?
- They all can be done through Meetup - I think the point here is that multiple channels avoids vendor lock-in and increases the likelihood that a user will overlap with one of the 4 communication strategies.
- The domain of Artificial Life is highly related and has had an ongoing conference series and journal going, might be worth mining for more inspiration:
https://en.wikipedia.org/wiki/Artificial_life https://direct.mit.edu/artl https://alife.org
- Signs that I've been reading too many retro-computing articles: I parsed the title as a browser for Windows 1.0!
- DCA and IAD have their work-load shared due to regulatory action:
> The Perimeter Rule is a federal regulation established in 1966 when jet aircraft began operating at Reagan National. The initial Perimeter Rule limited non-stop service to/from Reagan National to 650 statute miles, with some exceptions for previously existing service. By the mid-1980s, Congress had expanded Reagan National non-stop service to 1,250 statute miles (49 U.S. Code § 49109). Ultimately, Reagan National serves primarily as a "short-haul" airport while Washington Dulles International Airport serves as the region's "long-haul" growth airport.
> Congress must propose and approve federal legislation to allow the U.S. Department of Transportation to issue "beyond-perimeter" exemptions which allows an airline to operate non-stop service to cities outside the perimeter. As a result of recent federal exemptions, non-stop service is now offered between Reagan National and the following cities: Austin, Denver, Las Vegas, Los Angeles, Phoenix, Salt Lake City, San Francisco, San Juan, Seattle and Portland, Ore.
https://www.flyreagan.com/about-airport/aircraft-noise-infor...
- I updated my M2 MacBook Pro and the USB-C hub on my Dell U3219Q monitor no longer works... it's been a substantial disruption to my workflow with external peripherals and I don't have a USB-A to C converter to even partially get back on my feet with my external mouse/keyboard (that was the monitor hub's job!)
- See my comment on the parent for a study I did on these questions regarding reading strategies. Of particular relevance, Darwin kept detailed records of the books he read and wished to read in two notebooks [CUL-DAR119, CUL-DAR118], which spanned from his return aboard the Beagle until a few months after The Origin of Species was published.
http://darwin-online.org.uk/EditorialIntroductions/vanWyhe_n...
- This idea is very, very closely related to what I did for my PhD dissertation, with our analysis of Darwin's Readings being published in Cognition and available at https://arxiv.org/abs/1509.07175.
Summary: Darwin kept a series of reading notebooks recording everything he read for a very convenient time-span: from his return aboard the Beagle until a few months after The Origin of Species was published. (CUL-DAR119, CUL-DAR128, http://darwin-online.org.uk/EditorialIntroductions/vanWyhe_n...) I used the bibliographic entries to look up 97% of the English, non-fiction works in his library and trained an LDA topic model over it. We observed clear behavioral shifts in his reading patterns over time, which corresponded with other biographical events.
We tied this into a theory of "information foraging" - that as people are researching they are either exploiting existing topics or exploring new areas. For example, early in the reading history, one reading largely followed the next - there were few jumps in subjects between readings. As he was writing the Origin and synthesizing resource, the topic shifts became much more exploratory.
This original model had no goal-directed behavior, it was only a description of what he read, but still found these behavioral shifts when he marked he was beginning the Origin. In a later study, we began to look at the "zeitgeist" question you mention and how Darwin's drafts of the Origin diverged from the culture, as represented by the books he was reading (https://arxiv.org/abs/1802.09944) In this second study, we used the reading model and sampled his writings into that model space.
Both this library and his "to be read" notebooks define a nice "adjacent possible" - could he have read other things that may have been closer to his thoughts in the Origin? We use permutations of what he DID read as a null for studying his reading behavior - could he have read them in a more "optimal" manner? (With a lot of qualification on different "optimal" learning strategies...)
Final note: the time scales of his record keeping were not rare for the time - many people of letters kept "commonplace books" that journaled these reading histories. There was once a project to collect these reading histories, but I don't have the link at hand now.
- Strange to say Apple doesn't have productivity tools when Pages, Sheets, and Keynote exist on every Mac. I get the scale arguments, but Handoff and iCloud integration are a sleeper IF you've bought into the ecosystem...
Also hard to overstate just how much more valuable the enterprise market is over the consumer market when comparing Microsoft vs. Google as one-stop anything shops.
I don't see Google as having the obvious dominant position to make the argument it's their race to lose, considering Microsoft has a stake in chatGPT and is actively integrating it into their browser and productivity suites.
- There is a massive amount of history between the two companies, with Jobs being the largest individual shareholder of Disney due to the Pixar acquisition and Iger sitting on the board of Apple until Disney+ was launched.
There's a Wikipedia page dedicated to the subject with extensive references: https://en.wikipedia.org/wiki/Potential_acquisition_of_Disne...
- Exactly. There is huge variability in how many cows can be supported by the land. For a deep dive, check out: https://www.farmbrite.com/post/how-many-cows-can-i-keep-per-...
- This was the best feature of Indiana University’s Scheme-based curriculum - it leveled the playing field and caused us to start to think about programming not as engineering, but as a type of mathematics over data.
- Ironically, Dybvig uses vim.
- Interestingly, Alexa was founded by Brewster Kahle, who founded the Internet Archive contemporaneously (both in 1996). Interesting flow of ideas between the two projects - one to figure out what is getting traffic (commercial) and one to figure out how to preserve it (non-profit).
- Unplug. Exercise. My only good research was done during the time when I was physically and mentally fit. There is a tendency (especially in CS programs) to think that more hours will help you solve the problem. This is not always the case.
The other thing is to prioritize long-term tasks first. The biggest problem with a dissertation is that it is a large, complicated project, on a scale most students haven’t experienced before, even in a communal setting like software engineering. You really do have to wake up and start writing in order to get the work done. I found the old productivity blogs that championed 3 Most Important Things to be a decent way to frame the world.
For tooling, I found Todoist and Trello useful. Zotero for citation management. Having a lab that uses Slack (or another collaborative space) really accelerated my work.
Final note: don’t avoid your advisor if you don’t have the work done. Be up front about what you do and don’t have done. Your success is their success. When you succeed, you prove that they made a good selection. No one wants you to fail. Research is a moving target and you’re not always going to make deadlines. Figure out how to keep everyone in the loop so that you keep a mindset of learning at the front, rather than a mindset of “falling behind”.
- I really like your header. As someone that makes hiring decisions, starting by showing your curiosity and reasoning as clearly as the Linux example, then immediately casting your mentorship and current position as an opportunity shows that you will be an engaged coworker that can push things to the next level. The resume itself showing that creative thinking.
It's not so much the way the content is presented, as much as the content itself!
- I feel fortunate to have learned this lesson right before grad school… I used to run Gentoo with all kinds of optimization flags. The day of a conference deadline, I mucked up my /etc/ pretty bad. Switched to Debian and never looked back - sometimes you just need something that works.
- This is a fun exercise. Back in 1963, Fred Mosteller and David L. Wallace wrote a piece in the Journal of the American Statistical Association titled "Inference in an Authorship Problem: A comparative study of discrimination methods applied to the authorship of the disputed Federalist papers" [0]. It describes another technique for analyzing the authorship using a Bayesian model of word distributions.
One interesting thing about this is the claim that there is a ground truth for all but 12 of the papers, meaning that supervised learning could also be used.
For discussion, I often think that unsupervised methods are preferred to supervised methods, given a reasonably low error rate by the unsupervised method, as it will be able to generalize more readily.