Connect AnnounceKit and GPT-trainer to Build Intelligent Automations
Ready to use AnnounceKit and GPT-trainer automations
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Actions and Triggers
When this happensTriggers
A trigger is an event that starts a workflow.
New Comment Added
Runs when new comment is added
New Post Is Created
Runs when new post is created
New Reaction Is Added
Runs when new reaction is added
Do thisActions
Action is the task that follows automatically within your AnnounceKit integrations.
Create Post
Create a new announcement in your active AnnounceKit project.
Create QA Source
Create a QA source for a chatbot specified by chatbot uuid
Create URL Source
Creates a URL source for a chatbot specified by chatbot uuid.
Create Source Tag
Create a source tag for a chatbot.
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Frequently Asked Questions
To start, connect both your AnnounceKit and GPT-trainer accounts to viaSocket. Once connected, you can set up a workflow where an event in AnnounceKit triggers actions in GPT-trainer (or vice versa).
Absolutely. You can customize how AnnounceKit data is recorded in GPT-trainer. This includes choosing which data fields go into which fields of GPT-trainer, setting up custom formats, and filtering out unwanted information.
The data sync between AnnounceKit and GPT-trainer typically happens in real-time through instant triggers. And a maximum of 15 minutes in case of a scheduled trigger.
Yes, viaSocket allows you to add custom logic or use built-in filters to modify data according to your needs.
Yes, you can set conditional logic to control the flow of data between AnnounceKit and GPT-trainer. For instance, you can specify that data should only be sent if certain conditions are met, or you can create if/else statements to manage different outcomes.
About AnnounceKit
AnnounceKit is a powerful tool for managing and distributing product updates and announcements. It helps businesses keep their users informed with real-time notifications and changelogs, enhancing user engagement and satisfaction.
Learn MoreAbout GPT-trainer
GPT Trainer is a platform designed to enhance and customize the training of AI models, particularly those based on the GPT architecture. It provides tools and resources for developers and researchers to fine-tune and optimize AI models for specific tasks and applications.
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