Exactly. When you want to show results to a client, for example. >2. Following from 1, notebooks are meant to be shared with output in place, so others can und

Exactly. When you want to show results to a client, for example. >2. Following from 1, notebooks are meant to be shared with output in place, so others can understand without executing code. We wanted to let others execute the code without being overwhelmed by the code, but we also wanted our colleagues not to write an application or parametrize the notebook by adding metadata or tagging cells, so we added a feature named AppBooks[0]. We automatically detect parameters you want to expose, build a form on top of your notebook, and then serve a clean page. The user gets a page with a form with fields corresponding to your parameters, they just change the values and hit Run. The notebook runs with the new parameters and the client/stakeholder gets the result. This solves the problem of: "I have a notebook, how do I not only show my work, but allow domain experts to tinker and run this with specific values". Especially useful when the parameters are pretty domain specific (like instrumentation for nuclear power plants where the client wants to try the model on edge values). These runs are tracked in case you want to share a training AppBook where it produces a better model, for example. - https://iko.ai/docs/appbook https://iko.ai/docs/appbook