Kong, Traefik, Caddy, Linkerd, Fabio, Vulcand, and Netflix Zuul seem to be the most common in microservice proxy/gateway solutions. Kubernetes Ingress is often a simple Ngnix, which is difficult to separate the popularity from other things.
This is just a picture of this link from March 2, 2019
Originally, I had included some other solution
| import requests | |
| import sys | |
| import json | |
| def waybackurls(host, with_subs): | |
| if with_subs: | |
| url = 'http://web.archive.org/cdx/search/cdx?url=*.%s/*&output=json&fl=original&collapse=urlkey' % host | |
| else: | |
| url = 'http://web.archive.org/cdx/search/cdx?url=%s/*&output=json&fl=original&collapse=urlkey' % host |
| Code | Title | Duration | Link |
|---|---|---|---|
| Keynote | Andy Jassy Keynote Announcement Recap | 0:01 | https://www.youtube.com/watch?v=TZCxKAM2GtQ |
| Keynote | AWS re:Invent 2016 Keynote: Andy Jassy | 2:22 | https://www.youtube.com/watch?v=8RrbUyw9uSg |
| Keynote | AWS re:Invent 2016 Keynote: Werner Vogels | 2:16 | https://www.youtube.com/watch?v=ZDScBNahsL4 |
| Keynote | [Tuesday Night Live with Jame |
| to check if the server works - https://webrtc.github.io/samples/src/content/peerconnection/trickle-ice | |
| stun: | |
| stun.l.google.com:19302, | |
| stun1.l.google.com:19302, | |
| stun2.l.google.com:19302, | |
| stun3.l.google.com:19302, | |
| stun4.l.google.com:19302, | |
| stun.ekiga.net, | |
| stun.ideasip.com, |
| #!/bin/bash | |
| # generate file containing all URIs to execute to retrieve data from hacker news firebase API | |
| # api docs: https://github.com/HackerNews/API | |
| echo generating file hn-uri.txt | |
| URICOUNT=10000000 | |
| echo file will contain $URICOUNT lines |
A curated list of AWS resources to prepare for the AWS Certifications
A curated list of awesome AWS resources you need to prepare for the all 5 AWS Certifications. This gist will include: open source repos, blogs & blogposts, ebooks, PDF, whitepapers, video courses, free lecture, slides, sample test and many other resources.
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
| '''This script can be used to export data from Pocket (getpocket.com) | |
| Uses include migrating to a different "read it later" service, saving | |
| specific articles to another service, backing up your reading history, | |
| and more. | |
| Currently it can be used to export links and metadata for archived | |
| articles with a given tag, which are more recent than a given timestamp. | |
| An example use case is to export all articles you have tagged as | |
| "to-export", which are newer than 10 days old. The timestamp functionality |
by Bjørn Friese
Beautiful is better than ugly. Explicit is better than implicit.
I frequently deal with collections of things in the programs I write. Collections of droids, jedis, planets, lightsabers, starfighters, etc. When programming in Python, these collections of things are usually represented as lists, sets and dictionaries. Oftentimes, what I want to do with collections is to transform them in various ways. Comprehensions is a powerful syntax for doing just that. I use them extensively, and it's one of the things that keep me coming back to Python. Let me show you a few examples of the incredible usefulness of comprehensions.
