| #!/bin/zsh | |
| # this requires imagemagick to run | |
| # brew install imagemagick && brew install ghostscript | |
| # on Mac, download then run `mv ~/Downloads/emojify /user/local/bin/emojify` | |
| # emojify file [output-name] | |
| # examples | |
| # emojify ~/Downloads/img.png winky-face | |
| # emojify ~/Downloads/winky-face.jpg |
This example is a slight tweak on the best-practices example for working with Go modules as development dependencies.
The downside of the existing example is that someone who git clones your module to make a contribution, would not be able to run go generate (without extra work).
$ go generate
painkiller.go:5: running "stringer": exec: "stringer": executable file not found in $PATH
This is a collection of the things I believe about software development. I have worked for years building backend and data processing systems, so read the below within that context.
Agree? Disagree? Feel free to let me know at @JanStette. See also my blog at www.janvsmachine.net.
Keep it simple, stupid. You ain't gonna need it.
| package main | |
| import ( | |
| "context" | |
| "flag" | |
| "fmt" | |
| "log" | |
| "net/http" | |
| "os" | |
| "os/signal" |
Notes moved from graph-gophers/graphql-go#15
Graphql-js can parse directives like @live just fine -
parsed.definitions[0].selectionSet.selections[0].selectionSet.selections[0].directives[0] =
{ kind: 'Directive',
name: { kind: 'Name', value: 'live', loc: { start: 26, end: 30 } },
arguments: [],
loc: { start: 25, end: 30 } }
FWIW: I (@rondy) am not the creator of the content shared here, which is an excerpt from Edmond Lau's book. I simply copied and pasted it from another location and saved it as a personal note, before it gained popularity on news.ycombinator.com. Unfortunately, I cannot recall the exact origin of the original source, nor was I able to find the author's name, so I am can't provide the appropriate credits.
JD Maturen, 2016/07/05, San Francisco, CA
As has been much discussed, stock options as used today are not a practical or reliable way of compensating employees of fast growing startups. With an often high strike price, a large tax burden on execution due to AMT, and a 90 day execution window after leaving the company many share options are left unexecuted.
There have been a variety of proposed modifications to how equity is distributed to address these issues for individual employees. However, there hasn't been much discussion of how these modifications will change overall ownership dynamics of startups. In this post we'll dive into the situation as it stands today where there is very near 100% equity loss when employees leave companies pre-exit and then we'll look at what would happen if there were instead a 0% loss rate.
What we'll see is that employees gain nearly 3-fold, while both founders and investors – particularly early investors – get dilute
| machine: | |
| node: | |
| version: 4.1 | |
| pre: | |
| - sudo add-apt-repository -y ppa:ubuntu-toolchain-r/test && sudo apt-get update && sudo apt-get install gcc-4.9 g++-4.9 | |
| post: | |
| - sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.9 50 --slave /usr/bin/g++ g++ /usr/bin/g++-4.9 |
Hello, visitors! If you want an updated version of this styleguide in repo form with tons of real-life examples… check out Trellisheets! https://github.com/trello/trellisheets
“I perfectly understand our CSS. I never have any issues with cascading rules. I never have to use !important or inline styles. Even though somebody else wrote this bit of CSS, I know exactly how it works and how to extend it. Fixes are easy! I have a hard time breaking our CSS. I know exactly where to put new CSS. We use all of our CSS and it’s pretty small overall. When I delete a template, I know the exact corresponding CSS file and I can delete it all at once. Nothing gets left behind.”
You often hear updog saying stuff like this. Who’s updog? Not much, who is up with you?
Often it is useful to have access to the median value for fields of a data stream since they are more robust with respect to outliers. The median is defined as the value of a dataset such that, when sorted, 50% of the data is smaller than the value and 50% of the data is larger then the value. Ordinarily this is difficult to calculate on a stream because it requires the collection and sorting of all data.
The median of a data stream can be approximated with a technique called stochastic averaging. To approximate the median value of a data stream one could use the following approach:
Given the current estimate of the median M. If the next observed value in the stream is larger than M, increase the current estimate by r (= the learning rate). If it is smaller, decrease the estimate by r. When M is close to the median, it increases as often as it decreases, and therefore it stabilizes.
This approach was taken from the book "Real-time Analytics -