separate the workflow description from its execution.

And because it's real CSS, rather than some camelCased quotes-everywhere impostor, we can take advantage of the 'tweak in devtools, paste back into our source code' workflow, which I personally couldn't live without.

Yarn also has an RFC process which may offer a better discussion platform compared to this GitHub issue.

Many changes, including bug fixes and documentation improvements can be implemented and reviewed via the normal GitHub pull request workflow. Some changes though are "substantial", and we ask that these be put through a bit of a design process and produce a consensus among the Yarn core team. The "RFC" (request for comments) process is intended to provide a consistent and controlled path for new features to enter the project.

Ultimately though, I'd say the file structure that makes the most sense for your i18n workflow is the best one.

Archiving and downloading annotations Annotation viewing and export, from Hypothesis Labs Link: https://jonudell.info/h/facet Screencast: https://jonudell.net/h/facet.mp4 Description:  View annotations by user, group, URL, or tag. Export results to HTML, CSV, text, or Markdown.

However, it did require us to think differently about the creation, maintenance, and sustainment of our components moving forward — an integrative process we call "storybook-driven development."

These protocols, formal structures, plans, procedures, and schemes can be con-ceived of asmechanismsin the sense that they (1) are objectified in some way(explicitly stated, represented in material form), and (2) are deterministic or at leastgive reasonably predictable results if applied properly. And they aremechanisms ofinteractionin the sense that they reduce the complexity of articulating cooperativework.

Therefore, instead of pursuing the elusive aim of devising organizational modelsthat are not limited abstractions and thus in principle brittle when confronted withthe inexhaustible multiplicity of reality, organizational models in CSCW applica-tions should be conceived of asresourcesfor competent and responsible workers.

Exceptions are normal in work processes.

Re-open libraries for exploration I use in-ns to jump into library namespaces and re-define their vars. I insert bits of println statements to help understand how data flows through a library. These monkey-patches only exist in the running REPL. I usually put them inside a comment form. On a REPL restart, the library is back at its pristine state. In this example below, I re-open clj-http.headers to add tracing before the header transformation logic: [source] ;; set us up for re-opening libraries (require 'clj-http.headers) (in-ns 'clj-http.headers) (defn- header-map-request [req] (let [req-headers (:headers req)] (if req-headers (do (println "HEADERS: " req-headers) ;; <-- this is my added print (-> req (assoc :headers (into (header-map) req-headers) :use-header-maps-in-response? true))) req))) ;; Go back to to the user namespace to test the change (in-ns 'user) (require '[clj-http.client :as http]) (http/get "http://www.example.com") ;; This is printed in the REPL: ;; HEADERS: {accept-encoding gzip, deflate} An astute observer will notice this workflow is no different from the regular clojure workflow. Clojure gets out of your way and allows you to shape & experiment in the code in the REPL. You can use this technique to explore clojure.core too!

We'll first look at modelling the problem space - what is common to, and what differs between, thedifferent product variants. Then we'll consider one possible approach for realising product variants in the solutionspace using C++ as an example. Finally we'll look at how Application Engineering is performed by using theproblem and solution space models to create a product variant.

In reality, this linear flow is rarely found in practice.Product Lines usually evolve continuously, even after the first product variants have been defined and deliveredto customers.

Data becomes dark because no one is paying attention. There is little professional reward structure for scientists to preserve and disseminate raw data. Scientists are rewarded for creating high-density versions of their data in statistics, tables, and graphs in scholarly journals and at conferences. These publications in some ways are the sole end product of scientific inquiry. These products, while valuable, may not be as useful as some authors hope.

Traders continue to monitor their open positions and look for any more opportunities.

If everything is working properly, traders start scanning the markets for potential trading opportunities.

After reading about events and making note of what the analysts are saying, traders head to their workstations, turn on their computers and monitors and open up their analysis and trading platforms.

Traders will also review economic calendars to find out which market-moving financial reports – such as the weekly petroleum status report – are due that day.

This involves reading stories from various newspapers and financial websites, as well as listening to updates from financial news networks, such as CNBC and Bloomberg.

Who Owns Digital Science?

“workflow is the new content.”