diff --git a/config.yaml b/config.yaml
index 4a38bdff..2ec854f6 100644
--- a/config.yaml
+++ b/config.yaml
@@ -64,6 +64,7 @@ episodes:
 - chapter3.md
 - chapter4.md
 - chapter6.md
+- algorithm_development.md
 
 # Information for Learners
 learners:
diff --git a/episodes/algorithm_development.md b/episodes/algorithm_development.md
new file mode 100644
index 00000000..99f5e9db
--- /dev/null
+++ b/episodes/algorithm_development.md
@@ -0,0 +1,381 @@
+---
+title: "Algorithm development"
+teaching: 10
+exercises: 2
+---
+
+:::::::::::::::::::::::::::::::::::::: questions
+
+- What do the algorithm tools in vantage6 provide?
+- How do you create a personalized boilerplate using the v6 cli?
+- What is the process for adapting the boilerplate into a simple algorithm?
+- How can you test your algorithm using the mock client?
+- How do you build your algorithm into a docker image?
+- How do you set up a local test environment using the v6 cli (`v6 dev`)?
+- How can you publish your algorithm in the algorithm store?
+- How can you run your algorithm?
+
+::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::: objectives
+
+- Understand the available algorithm tools
+- Create a personalized boilerplate using the v6 cli
+- Adapt the boilerplate into a simple algorithm
+- Test your algorithm using the mock client
+- Build your algorithm into a docker image
+- Set up a local test environment using the v6 cli (`v6 dev`)
+- Publish your algorithm in the algorithm store
+- Run your algorithm in the UI
+- Run your algorithm with the Python client
+
+::::::::::::::::::::::::::::::::::::::::::::::::
+
+## Introduction
+
+The goal of this lesson is to develop a simple average algorithm, and walk through
+all the steps from creating the proper code up until running it in the User Interface
+and via the Python client. We will start by explaining how the algorithm
+interacts with the vantage6 infrastructure. Then, you will start to build your own
+algorithm, then test and run it.
+
+## Algorithm tools
+
+The vantage6 infrastructure provides a set of tools to help you develop your
+algorithm. You can install the algorithm tools with:
+
+```bash
+pip install vantage6-algorithm-tools
+```
+
+The algorithm tools provide the following for you:
+
+- **Algorithm client**: this client can be used to interact with the server, e.g.
+  to create a subtask, retrieve results, or get the organizations participating in the
+  collaboration.
+- **Data**: the tools provide a way to load the data from the node and provide it
+  to the algorithm as a Pandas dataframe.
+- **Input**: the tools read the input from the node and provide it to the arguments
+  of the algorithm function.
+- **Environment variables**: the tools get the environment variables from the node
+  and pass them on to the algorithm
+- **Token**: the algorithm tools ensure that the algorithm uses the security token
+  to be able to get the allowed resources from the server.
+- **Output**: the output from the algorithm functions is written to the proper file, so
+  that the node will send it back to the server.
+
+Many of these functionalites are handled by using Docker file mounts, which are read
+from and written to by the algorithm tools.
+
+For more information about the algorithm tools, please check out the relevant
+[documentation][algo-concepts].
+
+## Create a simple algorithm
+
+Vantage6 requires the functions in the algorithm to be at the base level of a Python
+package that is defined within the Docker image. These requirements can be cumbersome to
+get right if you have to write all the code yourself. Fortunately, vantage6 provides
+tools to create a boilerplate for you, so that you can focus on the development of your
+algorithm functions rather than worry about the infrastructure.
+
+To create a personalized boilerplate, use the vantage6 CLI. If you haven't done so yet,
+install it with:
+
+```bash
+pip install vantage6
+```
+
+Then, create a new boilerplate with:
+
+```bash
+v6 algorithm create
+```
+
+If you later want to modify your answers, you can do so by running:
+
+```bash
+v6 algorithm update --change-answers
+```
+
+This is recommended to do whenever you want to change something like the name of the
+function, as it will ensure that it will be updated in all places it was mentioned.
+
+The update command can also be used to update your algorithm to a new version, even
+after you have implemented your functions. This is helpful when there is new
+functionality or changes in vantage6 that require algorithms to update.
+
+::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge: Create a personalized boilerplate
+
+Create a personalized template to start developing your average algorithm
+
+- We need both a central and a partial function.
+- The central function should get an algorithm client to communicate with the server,
+  but it does not need data.
+- The partial function does not need an algorithm client, but it should get one database
+  from the node.
+- Both functions should have a `column` argument - the average over this column will
+  be calculated.
+- (Optional) Use the advanced options to create a Github pipeline that creates and
+  pushes your Docker image every time you commit to main.
+
+:::::::::::::::::::::::: solution
+
+## Output
+
+The personalized boilerplate should be successfully created.
+
+:::::::::::::::::::::::::::::::::
+::::::::::::::::::::::::::::::::::::::::::::::::
+
+Your personalized boilerplate is now ready to be adapted into a simple algorithm. We
+are now going to implement the average algorithm in several steps. Note that the README
+file in the boilerplate also provides a checklist that you can follow to implement the
+rest of the algorithm; however we are going to guide you through the process in this
+lesson.
+
+### Implement the algorithm functions
+
+We are going to implement the central and partial functions. The easiest is to start
+with the partial function. Using the Pandas dataframe that is provided by the algorithm
+tools, the following should be extracted for the requested column:
+
+- The number of rows that contains a number
+- The sum of all these numbers
+
+The boilerplate code for the central function already a large part of the code that will
+be required to gather the results from the partial functions. To compute the final
+average, we will need to:
+
+- Modify how the subtasks are created - we need to provide the column to the partial
+  functions
+- Combine the results from the partial functions to compute the average
+
+Remember that both functions should return the results as valid JSON serializable
+objects - we recommend returning a Python dictionary.
+
+### Test your algorithm using the mock client
+
+As discussed before, the algorithm tools contain an algorithm client that helps the
+algorithm container to communicate with the server. When testing your algorithm,
+it would be cumbersome to test your algorithm in the real infrastructure on every code
+change, as this requires you to build your algorithm image, ensure all nodes in your
+collaboration are online, etc. To facilitate the testing phase, the algorithm tools
+also provide a mock client. This client can be used to test your algorithm locally
+without having to start up the server and nodes. The mock client provides the same
+functions as the algorithm client, but instead of communicating with the server, it
+simply returns a smart mock response. The mock client does **not** mock the output
+of the algorithm functions, but actually calls them with test data. This way, you can
+easily test your algorithm functions locally without worrying about the infrastructure.
+
+Your personalized template already contains a `test/test.py` file that contains
+most of the code to test your algorithm.
+
+::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge: Implement the functions and test them
+
+Implement your partial and central functions as described above.
+
+Adapt and run `test.py` to test your function implementation:
+
+- Create a Python environment and run `pip install -e .`. This installs the local Python
+  package and also the algorithm tools (which contain the mock client).
+- Adjust `test.py` to compute the average over the **age** column. Do this both
+  for the test of the central and of the partial function
+- Run `test.py` to test your functions.
+
+:::::::::::::::::::::::: solution
+
+## Output
+
+You output of `test.py` should look something like:
+
+```
+[{'id': 0, 'name': 'mock-0', 'domain': 'mock-0.org', 'address1': 'mock', 'address2': 'mock', 'zipcode': 'mock', 'country': 'mock', 'public_key': 'mock', 'collaborations': '/api/collaboration?organization_id=0', 'users': '/api/user?organization_id=0', 'tasks': '/api/task?init_org_id=0', 'nodes': '/api/node?organization_id=0', 'runs': '/api/run?organization_id=0'}, {'id': 1, 'name': 'mock-1', 'domain': 'mock-1.org', 'address1': 'mock', 'address2': 'mock', 'zipcode': 'mock', 'country': 'mock', 'public_key': 'mock', 'collaborations': '/api/collaboration?organization_id=1', 'users': '/api/user?organization_id=1', 'tasks': '/api/task?init_org_id=1', 'nodes': '/api/node?organization_id=1', 'runs': '/api/run?organization_id=1'}]
+info > Defining input parameters
+info > Creating subtask for all organizations in the collaboration
+info > Waiting for results
+info > Mocking waiting for results
+info > Results obtained!
+info > Mocking waiting for results
+[{'average': 34.666666666666664}]
+{'id': 2, 'runs': '/api/run?task_id=2', 'results': '/api/results?task_id=2', 'status': 'completed', 'name': 'mock', 'databases': ['mock'], 'description': 'mock', 'image': 'mock_image', 'init_user': {'id': 1, 'link': '/api/user/1', 'methods': ['GET', 'DELETE', 'PATCH']}, 'init_org': {'id': 0, 'link': '/api/organization/0', 'methods': ['GET', 'PATCH']}, 'parent': None, 'collaboration': {'id': 1, 'link': '/api/collaboration/1', 'methods': ['DELETE', 'PATCH', 'GET']}, 'job_id': 1, 'children': None}
+info > Mocking waiting for results
+[{'sum': 624.0, 'count': 18}, {'sum': 624.0, 'count': 18}]
+```
+
+Hence, the average age is 34.666!
+
+:::::::::::::::::::::::::::::::::
+::::::::::::::::::::::::::::::::::::::::::::::::
+
+## Build your algorithm into a docker image
+
+Your algorithm boilerplate contains a `Dockerfile` in the root folder. You can build
+your algorithm into a docker image by running:
+
+```bash
+docker build -t your-image-name .
+```
+
+in the directory where your `Dockerfile` resides.
+
+Note that if you have selected the advanced options when creating your boilerplate,
+you had the option to include a Github action pipeline that built the Docker image for
+you every time a commit is pushed to main. This is the preferred way of working for
+real-world projects with open-source algorithm implementations.
+
+::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge: Implement the functions and test them
+
+Build an algorithm image with the name: `harbor2.vantage6.ai/workshop/average:<myname>`.
+Push it to the repository.
+
+:::::::::::::::::::::::: solution
+
+## Output
+
+If your name is Jane Smith, you may have done the following in the base directory of
+your algorithm (where the Dockerfile is):
+
+```bash
+docker build -t harbor2.vantage6.ai/workshop/average:jsmith .
+docker push harbor2.vantage6.ai/workshop/average:jsmith
+```
+
+Both commands should execute without errors.
+
+:::::::::::::::::::::::::::::::::
+::::::::::::::::::::::::::::::::::::::::::::::::
+
+## Set up a local test environment
+
+When the algorithm image is built, it is recommended to test locally if it also works
+with an actual server and nodes - not just using the mock client. The easiest way to
+set up a server and a few nodes locally with:
+
+```bash
+v6 dev create-demo-network
+```
+
+This command creates a vantage6 server configuration, and then registers a
+collaboration with 3 organizations in it. It registers a node for each organization and
+finally, it creates the vantage6 node configuration for each node with the correct API
+key.
+
+The other available commands are:
+
+```bash
+# Start the server and nodes
+v6 dev start-demo-network
+
+# Stop the server and nodes
+v6 dev stop-demo-network
+
+# Remove the server and nodes
+v6 dev remove-demo-network
+```
+
+In the previous lesson, you have learned how to run an algorithm using the Python
+client. Now, you can run your own algorithm using the Python client!
+
+<!-- TODO link to lesson 5 where Python client is discussed -->
+
+::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge: Test your algorithm on a local vantage6 network
+
+Create and start a local vantage6 network with the `v6 dev` commands. Then, run your
+algorithm using the Python client. You can find the command to run your algorithm in
+the `test.py` file, since the mock client has exactly the same syntax as the real client.
+
+<!-- TODO an alternative is to do this all locally, but that only works if vdev has UI + algorithm store -->
+
+:::::::::::::::::::::::: solution
+
+## Output
+
+You can find the revised JSON file on the page with the algorithm details
+
+::::::::::::::::::::::::::::::::::::::::::::::::
+:::::::::::
+## Publish your algorithm in the algorithm store
+
+In previous lessons, we have discussed how to run algorithms from the algorithm store.
+Now, it is time to publish your own algorithm in the algorithm store. This is required
+if you want to run your algorithm in the user interface: the user interface gathers
+information about how to run the algorithm from the algorithm store. For example, this
+helps the UI to construct a dropdown of available functions, and to know what arguments
+the function expects.
+
+<!--
+TODO link in first sentence to lesson where algorithm store is discussed (lesson 3) -->
+
+The boilerplate you create should already contain an `algorithm_store.json` file that
+contains a JSON description of your algorithm - how many databases each function uses,
+for example.
+
+You can put the algorithm in the store by selecting the algorithm store in the UI, then
+clicking on the "Add algorithm" button. You can then upload the `algorithm_store.json`
+file in the top. After uploading it, you can change the details of the algorithm before
+submitting it.
+
+::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge: Add your algorithm to the algorithm store
+
+Upload your algorithm to the algorithm store. Submit the `algorithm_store.json`, check
+the filled in details, and submit the algorithm.
+
+Then, can you download the revised JSON file so you can update it in your algorithm
+repository?
+
+:::::::::::::::::::::::: solution
+
+## Output
+
+You can find the revised JSON file on the page with the algorithm details
+
+::::::::::::::::::::::::::::::::::::::::::::::::
+::::::::
+## Next steps
+
+Congratulations! You have successfully developed your first algorithm. You have learned
+how to create a personalized boilerplate, implement the algorithm functions, and run the
+algorithm using the Python client and the UI. The resulting algorithm, however, is not
+suitable yet for real-world use. For instance, if a node contains only a single data
+point for a given column, there are no guards implemented that prevent that such
+sensitive data is shared with the server. Here, we describe a few next steps that are
+usually important to take before your algorithm is ready for real-world use:
+
+- **Privacy filters**: implement privacy filters to ensure that sensitive data is not
+  shared with the server.
+- **Error handling**: implement error handling to ensure that the algorithm does not
+  crash when unexpected input is provided. Note that there are [custom vantage6 errors][v6-errors]
+  that you can raise to provide more information about what went wrong.
+- **Documentation**: document your algorithm so that others can understand how to use
+  it.
+
+Other next steps could be to extend the algorithm with more functionality, such as
+allowing to calculate the average over multiple columns, or to add a `group_by`
+argument to compute the average per group. Alternatively, you can have a look at other
+algorithms in the algorithm store to see if you can understand and/or improve them.
+
+In the final lesson of this course, you will have the opportunity to work on your own
+projects. Maybe you can also use that opportunity to further develop your algorithm!
+
+::::::::::::::::::::::::::::::::::::: keypoints
+
+- Use `v6 algorithm create` to create a personalized boilerplate
+- Implement the central and partial functions
+- Build your algorithm into a docker image
+- Test it with the mock client and generate a local test environment with `v6 dev`
+- Publish your algorithm in the algorithm store to run it in the UI
+
+::::::::::::::::::::::::::::::::::::::::::::::::
+
+[algo-concepts]: https://docs.vantage6.ai/en/main/algorithms/concepts.html
+[v6-errors]: https://docs.vantage6.ai/en/main/function-docs/_autosummary/vantage6.algorithm.tools.exceptions.html#module-vantage6.algorithm.tools.exceptions
diff --git a/index.md b/index.md
index af662764..6cfbc4eb 100644
--- a/index.md
+++ b/index.md
@@ -2,8 +2,7 @@
 site: sandpaper::sandpaper_site
 ---
 
-This is a new lesson built with [The Carpentries Workbench][workbench]. 
-
+This is a new lesson built with [The Carpentries Workbench][workbench].
 
 [workbench]: https://carpentries.github.io/sandpaper-docs
 
diff --git a/notes.md b/notes.md
index f442e738..5944b884 100644
--- a/notes.md
+++ b/notes.md
@@ -1,32 +1,42 @@
 ## Target Audience
+
 ### Newbies
+
 Want to run basic federated data analyses
+
 #### Prerequisites
+
 - Know how to do data analysis in a centralized setting
 
 ### Data scientist
+
 Knows what the newbie knows. Want more control over their analyses. Potentially develop their own
 federated algorithms.
 
 #### Prerequisites
+
 - Basic (!) python knowledge
 - Experience with command line
 - Knows how to do federated analysis using the vantage6 UI
 
 ### Sys admin (not targeted)
+
 People who install vantage6. We are probably not going to cover this topic.
 
 #### Prerequisites
+
 - Lots of prerequisites
 
 ## Objectives (we need max. 8)
+
 ### Newbie objectives
+
 1. Understand the basic concepts of PET
    - Understand PET, FL, MPC, homomorphic encryption, differential privacy
    - Understand how different PET techniques relate
    - Understand scenarios where PET could be applied
    - Understand horizontal vs vertical partitioning
-   - Decompose a simple analysis in a federated way 
+   - Decompose a simple analysis in a federated way
    - Understand that there is paperwork to be done (DPIA etc.)
 2. Understanding v6
    - List the high-level infrastructure components of v6 (server, client, node)
@@ -45,13 +55,22 @@ People who install vantage6. We are probably not going to cover this topic.
    - Understand the relation between the administrative entities of v6 (e.g. users, orgs, collaborations)
    - Understand the permission system of v6
    - be able to manage a collaboration using the UI
-        - Be able to register a new organization
-        - Ad a user to the new organization
-        - Reset an api key
+     - Be able to register a new organization
+     - Ad a user to the new organization
+     - Reset an api key
 
 ### Data scientist objectives
+
 Preparation (might require setup session):
-- Install v6 packages
+
+- Install v6 packages (which version?)
+
+```bash
+pip install vantage6
+pip install vantage6-algorithm-tools
+pip install vantage6-client
+```
+
 - Install docker
 
 5. Run PET analysis using the python client
@@ -81,6 +100,6 @@ Preparation (might require setup session):
    - Know some examples/starting points
 
 ### Rejected objectives
+
 - setting up v6 infrastructure (very difficult, requires different prerequisites)
 - how to do the paperwork
-