boffbowsh
AI-Generated South Park
AI poses national security threat, warns UK terror watchdog
Third party Twitter apps partially working again
Building a website like it's 1999 in 2022
Nixpacks beat Heroku buildpacks by almost two minutes
The people who implemented this process are the same type of "IT/devs" or "Devops" you mention. There isn't a whole lot of throwing things over the wall to Ops, or for that matter putting up arduous processes against "normal" engineers.
Fintech Monzo launches new buy now pay later service
Show HN: Searchable index of your Ruby projects' Gemfile.locks
Unix-ti.me – A friendlier online Unix Timestamp converter
Make was the perfect tool for the task at the beginning, but it's getting a little unwieldy and will probably need rewriting in something else soon, probably Python.
I'm not entirely sure I understand what you're asking though. We've so far only Dockerized the main Python application, and not many of the supporting applications. We don't think we'd go down the route of running Postgres, ElasticSearch, Redis etc under Docker.
To give you an example of times taken, Pull Requests take about 10 minutes to test, a full master build (including uploading build artifacts) takes around 15 minutes, and a deployment to 8 Dockerized web hosts is about 5 minutes, including a smoke test on each host.
EDIT: Just realised what you're asking. If you're running many processes on each host then yes it can take a while to deploy. We deploy our Celery workers under separate Docker containers and this can take a while to stop and start. For the website though we run uWSGIs in forked mode, so it's only one container per host. This will probably change in the future, we're evaluating Mesos + Marathon for a more PaaS-like deployment where we can say "14 web processes please!"
Hope that answers your question.