diff --git a/examples/spouse/README.md b/examples/spouse/README.md
index 78727c686..65308cb67 100644
--- a/examples/spouse/README.md
+++ b/examples/spouse/README.md
@@ -96,6 +96,8 @@ deepdive do articles
 DeepDive will output an execution plan, which will pop up in your default text editor;
 save and exit to accept, and DeepDive will run, creating the table and then fetching & loading the data!
 
+<todo>show example tuples with `deepdive query`</todo>
+
 ### 1.2. Adding NLP markups
 Next, we'll use Stanford's [CoreNLP](http://stanfordnlp.github.io/CoreNLP/) natural language processing (NLP) system to add useful markups and structure to our input data.
 This step will split up our articles into sentences, and their component _tokens_ (roughly, the words).
@@ -148,6 +150,8 @@ deepdive compile
 deepdive do sentences
 ```
 
+<todo>show example tuples with `deepdive query`</todo>
+
 Note that the previous steps—here, loading the articles—will _not_ be re-run unless we specify that they should be, using, e.g.:
 
 ```bash
@@ -211,6 +215,9 @@ Again, to run, just compile & execute as in previous steps:
 deepdive compile && deepdive do person_mention
 ```
 
+<todo>show example tuples with `deepdive query`</todo>
+
+
 #### Mentions of spouses (pairs of people)
 Next, we'll take all pairs of **non-overlapping person mentions that co-occur in a sentence with less than 5 people total,** and consider these as the set of potential ('candidate') spouse mentions.
 We thus filter out sentences with large numbers of people for the purposes of this tutorial; however these could be included if desired.
@@ -247,6 +254,9 @@ Again, to run, just compile & execute as in previous steps.
 deepdive compile && deepdive do spouse_candidate
 ```
 
+<todo>show example tuples with `deepdive query`</todo>
+
+
 ### 1.4. Extracting features for each candidate
 Finally, we will extract a set of **features** for each candidate:
 
@@ -305,6 +315,8 @@ Again, to run, just compile & execute as in previous steps.
 deepdive compile && deepdive do spouse_feature
 ```
 
+<todo>show example tuples with `deepdive query`</todo>
+
 Now we have generated what looks more like the standard input to a machine learning problem—a set of objects, represented by sets of features, which we want to classify (here, as true or false mentions of a spousal relation).
 However, we **don't have any supervised labels** (i.e., a set of correct answers) for a machine learning algorithm to learn from!
 In most real world applications, a sufficiently large set of supervised labels is _not_ in fact available.
@@ -315,7 +327,6 @@ With DeepDive, we take the approach sometimes referred to as _distant supervisio
 
 
 
-
 ## 2. Distant supervision with data & rules
 In this section, we'll use _distant supervision_ (or '_data programming_') to provide a noisy set of labels to supervise our candidate relation mentions, based on which we can train a machine learning model.
 
@@ -374,6 +385,9 @@ Notice that for DeepDive to load the data to the corresponding database table th
 deepdive do spouses_dbpedia
 ```
 
+<todo>show example tuples with `deepdive query`</todo>
+
+
 #### Supervising spouse candidates with DBpedia data
 First we'll declare a new table where we'll store the labels (referring to the spouse candidate mentions), with an integer value (`True=1, False=-1`) and a description (`rule_id`):
 
@@ -481,6 +495,7 @@ deepdive compile && deepdive do has_spouse
 Recall that `deepdive do` will execute all upstream tasks as well, so this will execute all of the previous steps!
 
 
+<todo>show example tuples with `deepdive query`</todo>
 
 
 
@@ -546,18 +561,17 @@ has_spouse(p1_id, p2_id) => has_spouse(p1_id, p3_id) :-
 ```
 
 
-
+<todo>show learning/inference steps, example results</todo>
 
 
 
 
 
 ## 4. Error analysis & debugging
-_**TODO**_
 
-### Corpus exploration with Mindbender
+### 4.1. Browsing data with Mindbender
 
-This part of the tutorial is optional and focuses on how the user can browse through the input corpus via an automatically generated web-interface. The reader can safely skip this part.
+<todo>write</todo>
 
 #### DDlog annotations for browsing data
 
@@ -571,35 +585,29 @@ articles(
 ).
 ```
 
-#### Installing Mindbender
-**_TODO: Put in proper way to do this!?_**
-Given that `DEEPDIVE_ROOT` is a variable containing the path to the root of the deepdive repo, if you are on linux run:
-
-```bash
-wget -O ${DEEPDIVE_ROOT}/dist/stage/bin/mindbender https://github.com/HazyResearch/mindbender/releases/download/v0.2.1/mindbender-v0.2.1-Linux-x86_64.sh
-```
-
-for other versions see [the releases page](https://github.com/HazyResearch/mindbender/releases).  Then make sure that this location is on your path:
+### 4.2. Estimating precision with Mindtagger
 
-```bash
-export PATH=${DEEPDIVE_ROOT}/dist/stage/bin:$PATH
-```
+<todo>write</todo>
 
-#### Running Mindbender
-First, generate the input for mindtagger.  You can edit the template for the data generated by editing `generate-input.sql`, and the template for displaying the data in `mindtagger.conf` and `template.html` (for more detail, see the [documentation](http://deepdive.stanford.edu/doc/basics/labeling.html))then run:
+#### Running Mindtagger
+First, generate the input for mindtagger.
+You can edit the template for the data generated by editing `generate-input.sql`, and the template for displaying the data in `mindtagger.conf` and `template.html` (for more detail, see the [documentation](http://deepdive.stanford.edu/doc/basics/labeling.html)) then run:
 
 ```bash
 cd mindtagger
-psql -d deepdive_spouse -f generate-input.sql > input.csv
+deepdive sql <generate-input.sql >input.csv
 ```
 
 Next, start mindtagger:
 
 ```bash
-PORT=$PORT ./start-mindtagger.sh
+mindbender tagger mindtagger.conf
 ```
 
 Then navigate to the URL displayed in your browser.
 
-2. Describe how to setup mindbender.
-3. Describe which commands to run to get the mindbender environment up and running.
+
+
+### 4.3. Monitoring statistics with Dashboard
+
+<todo>write</todo>