| import { SelectQueryBuilder } from "kysely"; | |
| import { DB } from "db"; | |
| /* | |
| // From kysely-codegen | |
| export type Generated<T> = T extends ColumnType<infer S, infer I, infer U> | |
| ? ColumnType<S, I | undefined, U> | |
| : ColumnType<T, T | undefined, T>; | |
| export type Int8 = ColumnType<string, bigint | number | string, bigint | number | string>; |
This script fetches all of the files from the currently deployed version of a Firebase Hosting site. You must be signed in via the Firebase CLI and have "Site Viewer" permission on the site in question to be able to properly run the script.
npx https://gist.github.com/mbleigh/9c8680cf319ace2f506f57380da66e7d <site_name>
| """ | |
| Created by: @Andrew-Chen-Wang | |
| Parallel calling OpenAI with rate limit handling. | |
| Required packages: | |
| - openai | |
| - tenacity (optional if you remove the decorators. Useful for 500 errors) | |
| Usage: | |
| You must call with with_raw_response to get rate limit headers like: |
| from bs4 import BeautifulSoup | |
| # remove all attributes | |
| def _remove_all_attrs(soup): | |
| for tag in soup.find_all(True): | |
| tag.attrs = {} | |
| return soup | |
| # remove all attributes except some tags | |
| def _remove_all_attrs_except(soup): |
When I first set up Listmonk it was to use with Amazon SES. At the time Amazon would give you free 62,000 emails/mo if you sent them from an EC2 instance. So EC2 was the best server to use. In mid 2023 Amazon ended that, so now you can use whatever server you like, which makes things much easier. It shouldn't be too hard to convert these directions to another server host of your choice.
I used Hetzner with another build, and once my free EC2 year ended the AWS t2.micro cost me $14/mo. Hetzner has better specs and costs me $5/mo, so I added an nginx vhost and moved listmonk to the same server. Here's a $20 credit for Hetzner.
There is also the possibility to use the 1-click installers for their featured hosts: https://listmonk.app/ - listed under "Hosting providers". I'm not familiar with any of them but there are lots of new guides
| # Verify at https://github.com/Andrew-Chen-Wang/factory-boy-django-async | |
| import inspect | |
| import factory | |
| from asgiref.sync import sync_to_async | |
| from django.db import IntegrityError | |
| from factory import errors | |
| from factory.builder import BuildStep, StepBuilder, parse_declarations |
For full code example using an ORM and all SQL queries shown, please visit: https://github.com/Andrew-Chen-Wang/role-based-fine-grained-access-control
This is an example implementation of relational database tables for fine-grained access control with custom permissions and roles, similar to AWS IAM roles and policies/permissions.
The example demonstration utilizes Django models as an easier way to interpret. It is also because I ripped it off my current application for others to inspect.
At Lazify, we allow roles to be dynamically created for our enterprise customers. We assume permissions are not allowed to be generated. This is because permissions, in our application, are baked into our backend logic. If dynamic permissions are allowed, prefer the method described in point 1 using a Permission table and an M2M between Permission and your User; just note that the below Python implementation does not demonstrate this.
| sudo apt-get update -y | |
| sudo apt-get install -y \ | |
| ca-certificates \ | |
| curl \ | |
| gnupg \ | |
| lsb-release | |
| sudo mkdir -p /etc/apt/keyrings | |
| curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg | |
| echo \ | |
| "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \ |