Quarter: 2 | Sprint: 5 | 2021

We are changing the nomenclature of these updates to match our internal naming system. The number after the Q represents the quarter we are in, and the number after the S represents the sprint number.


We want to have an alpha version of the first Memri app by the end of this quarter. There's a lot of work that has been done on many fronts and a demo app is a great way to see how it all works together. It's a big challenge to prioritize the essential features (must be ready for the alpha version) from the features we love but are not essential and can be implemented later. These are the most important updates for this sprint:

  • Synchronization with pod: retrieve and upload items from pod, multiple clients can now exchange data and store it in pod.
Getting and uploading items from pod, multiple clients can now exchange data and store it in pod.
  • Fixed restoring state after reloading app
Fixed restoring state after reloading app
  • UI for merging duplicated contacts
UI for merging duplicated contacts
  • Started implementing the onboarding flow
  • Implemented UI for settings in iOS app
  • iOS app is now able to start our template plugin and observe its state and also present the authentication screen from it

Plugins Bounty Program

The bounty program for our developer community is going great. There's a nice group of enthusiasts building really cool plugins that will extend the functionality of the Memri ecosystem. We are super excited about it, and very happy to see so much interaction on our Memri Discord server.

If you want to take on the development of any of the plugins that haven't been assigned yet, join our Discord server and let us know so we can assign it to you and put your name in there (we offer bounties for completing these plugins).

We have also defined and assigned new roles on our wiki: Larry is the first to hold Schema Maintainer Role, and Alp is the first to hold Plugin Reviewer Role.  

Federated ML demo

Our Data Science team is working on a Federated ML demo where we train a sentiment analysis model on WhatsApp data without ever seeing any user data. On the machine learning side, we are using OpenMined's PySyft library (we also collaborate and develop for them). For importing and labeling data, we use the pod and write a few new plugins. Really exciting to see it all work, but there's still a lot of work ahead.


In this sprint, we focused on finalizing the proposal for the new remuneration system we want to create. Our current remuneration system is too simplistic, and where it was suited in the early years, it is no longer adequate now that our team has grown globally.

Creating a new remuneration system is a complex task, and in our case, it's not about simply setting a few standard salary grades, but rather we wanted to create a process that would allow for salary systems that fit the personal needs of all of our employees.

The salary system needs to be fair, transparent, flexible and not allow people to 'game it', and perhaps to put it bluntly, it shouldn't encourage people to do things only for the money, but because they think it's the smart thing to do.

The proposed remuneration system consists of three fundamental parts, the Salary component, the Profit sharing component, and the Safety net component, and allows MEs to be as progressive as they are comfortable. In the coming week more team members will review the proposal, share their perspectives, and we will discuss whether this is the right approach and how we can improve the proposal. We'll definitely need to do more work to finalize it, but it feels like we have made a good step forward in getting to this point. Once the process is complete, we'll write a dedicated blog about how we did it, and how the remuneration system works.