feat: add Gumloop flow trigger pipe

[divanshu-go]

name: pull request
about: submit changes to the project
title: "[pr] Add Gumloop flow trigger pipe"
labels: ''
assignees: ''

---

description

This pull request introduces a new pipe to trigger Gumloop flows. you can configure the pipeline to execute Gumloop flows based on configured intervals, daily, or hourly schedules. The pipe send the data captured by Screenpipe to the Gumloop API for processing.
you can create a gumloop flow on your own for any type of flow making or Gumloop pipeline

related issue: #365
/claim #365

type of change

• bug fix
• new feature
• breaking change
• documentation update

how to test

1. Ensure that the pipe.ts script is running with the new Gumloop flow trigger tasks.
2. Verify that the scheduler tasks trigger correctly based on the configured intervals (e.g., every minute, hourly, or daily).
3. Confirm that the flow is successfully triggered by checking the logs and verifying the data sent to the Gumloop API.
4. Test the behavior with various configurations in pipe.json (such as different frequencies and flow times).

if relevant add screenshots or screen captures to prove that this PR works to save us time.

checklist

• MOST IMPORTANT: this PR will require less than 30 min to review, merge, and release to production and not crash in the hand of thousands of users
• i have read the CONTRIBUTING.md file
• i have updated the documentation if necessary
• my changes generate no new warnings
• i have added tests that prove my fix is effective or that my feature works

additional notes

[louis030195]

how are logs created?

[louis030195]

@divanshu-go nice!!

so what is this doing? how did you use it?

was wondering if we should make it usable in two ways:

either send all raw data to gumloop (mic & screen)

pros:

• do whatever from gumloop with the data

cons:

• data privacy risks
• more pricy?

or have a local AI that capture specific details of your day (logs) and then send this to gumloop

pros:

• more data privacy

cons:

• potential loss of data, less quality, need to be more specific about the use case
• more resource usage

[divanshu-go]

#365 (comment)

[Anshgrover23]