16th Week of 2021
- Improvement: Add woop awesome quantified self resources to the research list.
- New: Add project to migrate software bug tracker to a vendor free one like
Create new seed project to be able to group and silence the notifications under a custom logic. For example:
- If I want to focus on a task, only show the most important ones.
- Only show alerts once every X minutes. Or define that I want to receive them the first 10 minutes of every hour.
- If I'm not working, silence all work alerts.
Instead of reading the email, github, gitlab, discourse, reddit notifications, aggregate all in one place and show them to the user in a nice command line interface.
For the aggregator server, my first choice would be gotify.
- New: Add seedling project to create factoryboy factories from pydantic models automatically.
Correction: Update the git repository.
The existent repository has been archived in favor of this one
New: Explain how to patch the extended_default_ignore error for versions > 3.9.0.
Add to your your
[tool.flakeheaven] extended_default_ignore= # add this
New: Add apprise to the interesting libraries to explore.
apprise: Allows you to send a notification to almost all of the most popular notification services available to us today such as: Linux, Telegram, Discord, Slack, Amazon SNS, Gotify, etc. Look at all the supported notifications
New: Add kivi and kivimd to the interesting libraries to explore.
New: Introduce the AWS SDK library and explain how to test it.
Boto3 is the AWS SDK for Python to create, configure, and manage AWS services, such as Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Simple Storage Service (Amazon S3). The SDK provides an object-oriented API as well as low-level access to AWS services.
New: Explain how to test ec2, route53, s3, and rds resources.
- New: Explain how to test vpc and auto scaling group resources.
Improvement: Explain how to extract the instance when testing autoscaling groups.
Also track the issue to add support to launch templates.
New: Explain how to log python program exceptions better than to a file.
loggingto write write exceptions and breadcrumbs to a file might not be the best solution because unless you look at it directly most errors will pass unnoticed.
To actively monitor and react to code exceptions use an application monitoring platform like sentry.
In the article I explain what are the advantages of using this solution and do a comparison between Sentry and GlitchTip.
Improvement: Remove advice to use my fork instead.
The original one has already merged my PR
＼\ ٩( ᐛ )و /／. Beware though as the
regexpare not enabled by default (against my will). You need to use the
use_regexp=Trueas an argument to
faker-optionalis a custom faker provider that acts as a wrapper over other Faker providers to return their value or
None. Useful to create data of type
- New: Explain how to log exceptions to sentry.
- New: Explain how to inject a testing configuration in the tests.
Last time I used this solution, when I added the library on a
setup.pythe direct dependencies weren't installed :S
You can create a new object with the new data using the
updateargument of the
New: Introduce the python cli builder library and it's progress bar.
Rich is a Python library for rich text and beautiful formatting in the terminal.
Check out the beautiful progress bar:
pip install rich python -m rich.progress
Improvement: Suggest to use ruyaml instead of ruamel.yaml.
As it's maintained by the community and versioned with git.
Use I'm well when referring to being ill, use I'm good for the rest.
New: Explain how to select a random choice from
New: Improve the periodic tasks and application metrics monitoring.