Simon Willison has a bunch of examples of scraping sites with GHA and storing the results in a repo. But you can use the same technique without the storing part if need be.
3,859 karma · joined December 16, 2012
Former Django core developer.
josh.smeaton@kraken.tech
Simon Willison has a bunch of examples of scraping sites with GHA and storing the results in a repo. But you can use the same technique without the storing part if need be.
https://docs.github.com/en/actions/using-github-hosted-runne...
4 core | 16gb RAM | 150gb SSD is the next size up which is just about what I need.
Edit: September 1 was the announcement, so I was probably due to check back in: https://github.blog/changelog/2022-09-01-github-actions-larg...
Without the tooling it's a nightmare. My previous Django project with approx 500k LOC had linting and some typing and that was a mess.
[0] https://github.com/octoenergy/conventions/blob/master/patter... [1] https://github.com/seddonym/import-linter
Each of the data, config, and cache envvars would all be set to ~/.data in this setup (though “data” isn’t a name I’d use)
“I imagine it’ll suck but I haven’t used it, and they’re breaking the UI for the wrong reasons”.
Such a quick transition from imagining someone doing something and then getting angry at your own imagination.
Honestly though my only experience with RabbitMQ has been as a backend for Celery (background task processor for Python) and I think my real issues are mostly to do with how Celery uses RabbitMQ in a very poor default setup.
Message confirmations are off by default and turning them on caused our queues to grind to a halt. Queues being single threaded and clusters don't much help with that without using some kind of sharding plugin. It seemed like getting to a good spot required a lot of arcane knowledge that wasn't so easy to find.
"Configuring RabbitMQ to be a performant message queue for background task systems" would be an excellent blog post I would share widely!
Lock in doesn’t make sense to me. The main competition is Pycharm which has its own set of tooling.
I can somewhat understand the remote extensions being closed source considering they need to interface with a server. It makes no sense for Python.
My guess is that Pylance is also used within Visual Studio proper, and the VS product owners are pushing for closed addons.
To be fair, pylance is orders of magnitude better with regards to performance. But I just don’t get what the reasoning was to make pylance closed source. It’s baffling.
Not ones that ship to production. Some developers have a terrible time trying to get through the mypy errors. But from my observations, I haven't seen any typing related errors in a very long time.
It's a platform as a service. It'll take your docker application (usually a web application) and run it on the internet. It'll also allow you to run it in many locations around the world transparently (using the same IP address / anycast) so that users closer to those locations will get better/lower latency.
It's a CDN for your application.
(There's more to it than this, but as far as elevator pitches go, sufficient?)
I’m coming at this from the perspective of a traditional django app that has .using(“write”) when wanting to write data. Otherwise you’re replaying requests at a completely different app server.
Am I understanding this correctly?