They are the utility scripts to start an editor window in Acme. B is async whereas E is sync (so that it can be used for the EDITOR env variable for software like git).
Not sure what they stand for for sure. E is probably editor. B might be buffer?
1,196 karma · joined November 21, 2015
Not sure what they stand for for sure. E is probably editor. B might be buffer?
Finally, I've come to appreciate using a mouse with my editor. Until I completely got rid of using most keyboard shortcuts (other than the most basic ones), I didn't realize how much cognitive taxes I was paying to retain and context-switch among different families of keyboard shortcuts. Mousing is so much more uniform.
As a maintainer, glad to see Fluentd there =) If folks have questions on Fluentd, I'd be happy to answer them here.
Of course, there's time and place for eschewing Anaconda, especially if you are trying to ensure your installation and dependencies are minimal. That said, for most data scientists who are workaday *NIX users at best, Anaconda is such a productivity boost.
EDIT: Actually, my analogy needs further qualifications. You _can_ get into dependencies/missing-header-files hell in Ubuntu, but it usually doesn't happen in the first few hour/days. Both Ubuntu and Anaconda have great "first 5 minutes to 5 days" experience, and that goes a long way in ensuring the adoption of underlying technology.
1. Centralized decision-making
2. Distributed contributions
While forking is important and necessary to experiment/test/prototype and iterate on various ideas, forking of a community is absolutely deadly. Look no further than Emacs/XEmacs or Node.js/io.js. It's very, very important that the ultimate decision making authority is concentrated on a few people with sound judgment (Often the creators, but not always).
On the other hand, development/contributions should be absolutely distributed: it accelerates development and makes it much easier for people to contribute to open source projects.
In fact, understanding this subtle difference between decision-making and development is what made GitHub successful: git was already a great tool for developing in the open. GitHub built a great platform for making decisions on top of it.
For those interested in funny and insight analyses of the data, check out this blog: http://toddwschneider.com/posts/analyzing-1-1-billion-nyc-ta...
America has lost a towering intellectual, and for that, I mourn today.
Wouldn't it be fair for that submitter to get karma points?
The "unsexy" IoT can be really unsexy. One customer wanted to get their data out of SCADA (www.scada.com), and as it turns out, the only way to get telemetry data out of their SCADA boxes was through emails as CSV attachments.
We ended up working with a third party dev shop to build a simple mail server that parses the incoming CSVs and send them to our backend so that the vendor could analyze the data coming from SCADA (which in turn came from heavy industry devices) using SQL, Salesforce, etc.
I never thought that MIME attachments would be a critical part in a "big data", "IoT" analytics pipeline...
Of course, they are also given books in print and interact with them as well, but I feel that the verdict is still out there about how that generation will feel about print when they are our age.
That's a long-winded way of saying that neither of us is young anymore, and we _might_ be on our way to be less indicative of the future and more reminiscent of the past (Just _maybe_).
Paul McCartney and Co. put together a concert to cheer us up (https://en.wikipedia.org/wiki/The_Concert_for_New_York_City), and that was the first time I had a chance to listen to Bowie live.
It turned out to be the last time. RIP.
Prime usually delivers within a day or two, unless heavy snow closes off the only semi-paved road leading up to her place :-)
My mother, who lives in remote mountains in Japan, is a die-hard Amazon Prime fan. Why? Because it manages to deliver any item to her place across many SKUs. It's more convenient -and often faster- for her to order something on Amazon than to drive 30 minutes to shop at the closest electronics shop. I've been amazed with how good Amazon's logistics is even in rural Japan.
The elusive "aha" moment is actually not that complicated in theory: just provide value faster than alternatives while ensuring quality. In Uber/Lyft's case, it's a safe ride from A to Z arriving in a few minutes. In Amazon Prime's case, it's any merchandise on your doorstep. In AWS's case, it's computing resources at your fingertips in minutes.
It's easier said than done =)
At the same time, the New Yorker is indeed known for their over-the-top narratives. There's both pros and cons to this approach. For example, a great work of literary journalism like "In Cold Blood" and "Hiroshima" were first published on the New Yorker because of this editorial tendency. On the other hand, their long-form content makes their subjects less approachable to a large swath of English-reading (especially those who aren't native readers of the language) population.
Unless your startup's core strategy involves machine learning, statistics tends to come handier than machine learning in the early days. Most likely, what moves your company is not a data product built atop machine learning models but the ability to draw less wrong conclusions from your data, which is the very definition of statistics. Also, in the early days of a startup, you experience small/missing data problems: You have very few customers, very incomplete datasets with a lot of gotchas. Interpreting such bad data is no small feat, but it's definitely different from training your Random Forest model against millions of observations.
Excel is by far the world's most popular business intelligence tool. Also, it is probably the world's most popular project management tool. In Japan (where I've done some business in), it also serves as everything from billing software to a mockup tool for digital products.
You might say that Macs have Excel too, but any business analyst types worth their salt know that the two Excels are vastly different.
Case in point: the very graph that shows the rise of Macs (quoted in the article and credited to IDC) seems to have been produced in Excel on Windows =)
Ultimately, I am beginning to think that Dropbox simply does not have the product DNA =/
I must say that a lot of "paid content" pieces are pretty good, considering that companies are paying money to place these articles.
I use Evoluent at home and HP's 3-button mouse on the go (http://www8.hp.com/us/en/products/oas/product-detail.html?oi...): while Evoluent is more ergonomic, it's too bulky to be portable.
I think there is something to be said about using a few (if not one) text-centric tool to get stuff done: it's much lower on your cognitive load and wears you out less.
Microsoft was another popular destination at Stanford at the time. If Facebook was toy-ish and Google was cool, Microsoft was "respected". Many deemed Microsoft to be the McKinsey of tech: spend a couple of years there, build their resume and join or start a start-up. I even knew someone who took a job at Yahoo! over one at Facebook (Needless to say, most everyone who had offers from either Google or Microsoft chose them over Facebook).
Hindsight is 20/20 =)