dlib

In this specific case it is also because Absinthe/graphQL subscriptions are built upon Phoenix channels so if you plan to use subscriptions, Phoenix is the easiest route.

Phoenix also is pretty minimal layer over Plug, especially if you don't include the html library.

Famously, something akin to this was done by Voltaire in 1729 (including the crowdsourcing). See https://books.google.com/books?id=BBusxgu8-AAC&lpg=PA259&vq=...

Very interesting, definitely gonna try this out as I have seen similar use-cases.

Any change Github is at anytime going to show the specific merge-conflicts for a PR that cannot be merged?

Got this book as well, it's fun to read through and I can impress friends with simple yet tasty recipes. Bought it mostly because I'm a huge fan of seriouseats and Kenji's posts especially, so I try to show my support.

I second this, also I'd like to see an overview of the (files with) merge conflicts if any instead of a message saying the PR can't be merged. Fixing the merge conflicts might be another thing in the web interface would be cool but I guess I'll stick to the CLI for this.

For completeness I would include the closed form solution in a discussion of linear regression. In matrix notation is fairly simple and it's easy to follow how it falls apart when the assumptions underlying linear regression are not present in the data you're working with (homoskedasticity, independence of errors etc.)

Playing the devil's advocate but isn't Apple hindering web standards a strategy to keep people using the apps from the AppStore. On mobile, where the iOS market share is huge and it's impossible to use any other rendering engine it would be hugely beneficial to stifle progress as native apps and thus the lock-in they provide, will keep the upper hand.

Unintended side-effects like these are why I prefer to stick to normal fixtures. They get the job done and save me from adding another dependency to my projects. Fixtures are also pretty straightforward and keep me from learning yet another DSL. I'm sure FactoryGirl has valid usecases but it's not for me.

Passenger is open source and has an enterprise tier. Though written in C++ they very much serve the Ruby world.

Nonetheless, I agree with your point that this model may not be suitable for every business.

The Strava route builder incorporates the popularity of other users routes when you plan your own. So, in an extension to the normal find-shortest-path algorithm between two points, penalties are given to paths not used by other users or bonus given to those that are popular. The result is that you end up with the better roads to ride on. Especially on the bike this is really useful as I can plan new routes in unknown areas and they are pretty good.

For running I found it remarkable how these heatmaps show the inclination to run along water bodies or in forests. It can almost perfect predict where I would or wouldn't run.

No wrong choice I think, mostly personal preference. If you know Python I'd stick with that, iPython notebooks with pandas etc. is a solid choice. R might be a little harder to start with, sapply/lapply can be confusing but there's plenty of info and libraries on the web to make your life easier. For plotting, ggplot still wins over matplotlib in my opinion but Python has other strengths.

Indeed, when I come across empty lines, white space at the end of the lines etc. I remove it all, commit it and then start actually changing the code. White space in diffs is distracting from the actual changes.

They would also be able to move operations to places where energy is cheaper or sea water temperature is cooler. In general, it could be used to minimize costs looking at a variety of factors. I'm still curious how they will connect to the mainland. A fibre connection would hamper the flexibility of having a boat. Employees would rather live on the mainland than on a boat. So perhaps they the boat is stationary and will enjoy the ample amount of cooling water and potential energy the waves create.

Indeed, there's some incredible work put into the Python data analysis toolkit. Pandas is very impressive. However, no GGplot was holding me back from switching but this and improvements to other graphing libraries really make me want to use Python in my next projects.

It was a quote by Eric Schmidt.