Update Readme

Author: meggart

.github/workflows/CI.yml

@@ -41,6 +41,8 @@ jobs:
       - uses: julia-actions/julia-runtest@v1
       - uses: julia-actions/julia-processcoverage@v1
       - uses: codecov/codecov-action@v3
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
       - uses: coverallsapp/github-action@master
         with:
           github-token: ${{ secrets.GITHUB_TOKEN }}

README.md

@@ -3,7 +3,7 @@
 [![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://github.com/meggart/DiskArrayEngine.jl/blob/main/LICENSE)
 <!-- [![](https://img.shields.io/badge/docs-stable-blue.svg)](https://meggart.github.io/DiskArrayEngine.jl/stable) -->
 <!-- [![](https://img.shields.io/badge/docs-dev-blue.svg)](https://meggart.github.io/DiskArrayEngine.jl/dev) -->
-[![CI](https://github.com/meggart/DiskArrayEngine.jl/actions/workflows/ci.yml/badge.svg)](https://github.com/meggart/DiskArrayEngine.jl/actions/workflows/ci.yml)
+[![CI](https://github.com/meggart/DiskArrayEngine.jl/actions/workflows/CI.yml/badge.svg)](https://github.com/meggart/DiskArrayEngine.jl/actions/workflows/CI.yml)
 [![Codecov](https://codecov.io/gh/meggart/DiskArrayEngine.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/meggart/DiskArrayEngine.jl)
 <!-- [![Aqua.jl Quality Assurance](https://img.shields.io/badge/Aquajl-%F0%9F%8C%A2-aqua.svg)](https://github.com/JuliaTesting/Aqua.jl) -->
 [![Downloads](https://shields.io/endpoint?url=https://pkgs.genieframework.com/api/v1/badge/DiskArrayEngine&label=Downloads)](https://pkgs.genieframework.com?packages=DiskArrayEngine)
@@ -24,4 +24,18 @@ Before starting to jump into this package it is worth checking if it is actually
 3. Your data is too large to fit into the memory of all your workers when distributed among them (otherwise try [DistributedArrays.jl](https://github.com/JuliaParallel/DistributedArrays.jl))
 4. You want to process *all* or almost all of your data and not just a small subset. Otherwise just read the subset of interest into memory and do your processing based on this one
 
-If you are still here 
+If you are still here you should also note that this package is not intended to be used by end-users directly, but the plan is to wrap functionality from this package 
+in other packages, in particular YAXArrays.jl, DimensionalData.jl or PyramidSchemes.jl that provide more user-friendly interfaces for the end users. 
+
+## Status of the package
+
+This package is still under active development and should be considered experimental. Expect things to break and to already be broken. In particular, extensive documentation and tests are still missing. However, some core functionality of the package is already used by e.g. [PyramidScheme.jl](https://github.com/JuliaDataCubes/PyramidScheme.jl) which is why we decided to already register this package while still under active development. 
+
+## Basic package usage
+
+To be done, describe the generalized moving window concept, how to define user functions, lazy interface and which runner options exist 
+
+## EngineArrays
+
+The simplest way to use some the machinery in DiskArrayEngine is to wrap any existing DiskArray into an EngineArray by calling `engine(mydiskarray)`. Afterwards, many operations like mapslices, mapreduce, broadcast and simple statistics like mean, median, max/min etc will be dispatch using DiskArrayEngine instead of the simple 
+DiskArrays.jl implementation and might give significant speedups. However, currently we still default to using the `LocalRunner`, which will only use a single process, we will experiment with defaulting to `DaggerRunner` as soon as multiple processes are available.