diff --git a/HISTORY.md b/HISTORY.md
index 7d037b4c0..ba51105d4 100644
--- a/HISTORY.md
+++ b/HISTORY.md
@@ -1,3 +1,9 @@
+1.0.0 / 2020-03-26
+------------------
+- Fiducial volume specification (#64)
+- added default cS1 cut (#63)
+- Cleanup and optimizations (#63, #64, #65)
+
 0.5.0 / 2020-01-31
 ------------------
 - Autographed Hessian; use Hessian in the optimizer (#62)
diff --git a/README.md b/README.md
index b3cb28c25..128cc187c 100644
--- a/README.md
+++ b/README.md
@@ -27,7 +27,3 @@ This has several advantages:
   - Each event has its "private" detector model computation at the observed (x, y, z, time), so it is easy and cheap to add  time- and position dependences to the likelihood.
   - Since the likelihood for a dataset takes O(seconds) to compute, we can do this at each of optimizer's proposed points during inference. We thus remove a histogram precomputation step exponential in the number of parameters, and can thus fit a great deal more parameters.
   - By implementing the signal model in tensorflow, the likelihood becomes differentiable. Using the gradient during fitting drastically reducing the number of needed interactions for a fit or profile likelihood.
-  
-Note this is still under construction / development, so it probably has some bugs and little documentation.
-
-