diff --git a/README.md b/README.md
index 7090414..82d1a3b 100644
--- a/README.md
+++ b/README.md
@@ -68,7 +68,8 @@ The main AI/ML development is being done in Jupyter.
 
 # Jupyter
 There Jupyter Lab Notebook files in the `notebooks/` subdirectory.
-These are producing usable results.
+These are producing usable results. Voila is used to convert
+Jupyter notebooks into websites.
 
 * `wut.ipynb` --- Machine learning Python script using Tensorflow and Keras in a Jupyter Notebook.
 * `wut-predict.ipynb` --- Make prediction (rating) of observation from pre-existing model.
@@ -116,6 +117,8 @@ The following scripts are in the repo.
 # Installation
 Installation notes...
 
+There's more docs on a few different setups in the `docs/` subdir.
+
 ## Setup
 The scripts use files that are ignored in the git repo.
 So you need to create those directories:
@@ -337,48 +340,6 @@ mkdir download
 rsync -ultav rsync://ml.spacecruft.org/download/ download/
 ```
 
-# TODO / Brainstorms
-This is a first draft of how to do this. The actual machine learning
-process hasn't been looked at at all, except to get it to generate
-an answer. It has a long ways to go. There are also many ways to do
-this besides using Tensorflow and Keras. Originally, I considered
-using OpenCV. Ideas in no particular order below.
-
-## General
-General considerations.
-
-* Use Open CV.
-
-* Use something other than Tensorflow / Keras.
-
-* Do mirror of `network.satnogs.org` and do API calls to it for data.
-
-* Issues are now available here:
-  * https://spacecruft.org/spacecruft/satnogs-wut/issues
-
-## Tensorflow / Keras
-At present Tensorflow and Keras are used.
-
-* Learn Keras / Tensorflow...
-
-* What part of image is being evaluated?
-
-* Re-evaluate each step.
-
-* Right now the prediction output is just "good" or "bad", needs
-  "failed" too.
-
-* Give confidence score in each prediction.
-
-* Visualize what ML is looking at.
-
-* Separate out good/bad/failed by satellite, transmitter, or encoding.
-  This way "good" isn't considering a "good" vetting to be a totally
-  different encoding. Right now, it is considering as good observations
-  that should be bad...
-
-* If it has a low confidence, return "unknown" instead of "good" or "bad".
-
 # Caveats
 This is the first artificial intelligence script I've done,
 I know little about radio and less about satellites,