We can create a small python service that uses a Large Language Model (LLM) to detect if a message is spam or not. Using this service, we can tie it into our Rails application so that any comment created will be evaluated for being spam or not. We explore a few different routes on handling any messages flagged as spam.

In a previous episode, we had created a custom ActionCable channel to assist in broadcasting updates from a background job. With the introduction of Turbo, we can simplify this process as we look at a few different approaches in displaying a progress bar with real time updates from background jobs.

In this episode, we look at creating an audio transcription service which allows files uploaded from Active Storage to be transcribed with Artificial Intelligence. However, there are a lot of considerations around the approach from both a performance and thread safety perspectives.

Learn to use custom error pages to provide a similar look to your application. Get notified on Slack as errors occur to act on them before they're reported.

Using a third-party add-on, sidekiq-cron, we can create periodic tasks to extend the functionality of sidekiq.