πŸ”Iteration

When people use learning to remix data.

In starting this project we knew a few things and hypothesized about many others. We also realized that all of those ideas, assumptions, and hypotheses might be completely wrong (e.g. the Use Cases we set out) and know the only potential outcomes from that reality are:

  1. Allow the project to fail by locking ourselves into something with no Feedback;

  2. Start slowly with something that can be changed and then add features or tweaks over time.

We are taking the latter approach, which will be an iterative process of rolling out different Elements of the project over time (see Feature Timeline) and then engaging Contributors through the Workflow Overview process to ensure we are sustainable.

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