diff --git a/plan.md b/plan.md
new file mode 100644
index 0000000000000000000000000000000000000000..14c719e73d6e71de8c92e22ce8e690be2a397522
--- /dev/null
+++ b/plan.md
@@ -0,0 +1,15 @@
+# Adaptive
+
+We propose to use local criteriums for sampling combined with global updates.
+This defines a family of straightforwardly parallelizable algorithms which are useful for intermediary cost simulations.
+
+When your function evaluation is very expensive, full-scale Bayesian sampling will perform better, however, there is a broad class of simulations that are in the right regime for Adaptive to be beneficial.
+
+We can include things like:
+* Asymptotically complexity of algorithms
+* Setting of the problem, which classes of problems can be handled with Adaptive
+* Loss-functions examples (maybe include [Adaptive quantum dots](https://chat.quantumtinkerer.tudelft.nl/chat/channels/adaptive-quantum-dots))
+* Trials, statistics (such as measuring timings)
+* Line simplification algorithm as a general criterium
+* Desirable properties of loss-functions
+* List potential applications