• Day 1: Opticon
  • Day 2: Opticode

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  • Autotrader's Product and Engineering teams were ahead of the curve many years ago when they built a home-grown solution for leveraging feature flags to support server-side testing. Over the years, the industry eventually caught up and surpassed this proprietary tooling and the team had a choice to make: Re-invest into the local solution or completely retool. In this case study, Scott Povlot, Principal Technical Architect, and Seth Stuck, Director of R&D Analytics, will discuss their journey in selecting and then migrating to their next generation of experimentation tooling. They will discuss selection criteria, pros and cons, and outline how they were able to make the migration to Optimizely successful and lessons learned along the way.

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  • In April 2020, GitHub announced a new Free for Teams plan. Behind the scenes, the engineering team was also setting up an experiment pipeline and an integration with Optimizely. In this session, we will take a peek at the process of setting up the integration, learning about the behavior of this new Free for Teams customer segment, and the next steps for this experiment pipeline.

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  • Personalized product recommendations are a powerful way to increase user engagement and revenue for your business. However, getting the product recommendations right can be the hardest part. In this hands on workshop, you will deploy Amazon Personalize’s machine learning product recommendations with Optimizely’s experimentation platform to optimize product recommendations in a sandbox & sample environment, the Retail Demo Store.

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  • Optimizely’s Developer Relations team is excited to host a virtual world and mini-game experience (inside Minetest) for Opticode attendees! Minetest is an open-source voxel game where you can build anything you want or play games others have created on hosted servers. You play by installing the Minetest Client on your computer, then by connecting to a Minetest Server, to play in a specific “world” with other players.

    We encourage you to install the game client before the conference and play the game to familiarize yourself with Minetest and to have a little fun 😀!

    Here’s some instructions to get started:

    1.) Install Brew

    2.) Install the game client on your computer:

    • Windows - Download the install zip and open /bin, click the executable
    • Mac - Use terminal to run ‘brew install minetest’ (if you don’t have brew, cmd + spacebar to search this mac for ‘minetest’, find (colored icon)

    3.) Connect to Optiworld

    • Open Minetest
    • In address box type ‘
    • Port: 30000
    • Remember your password, you’ll need it again!

    4.) Game controls to know:

    Optiworld Quest: Gather crystals and bring them to the obelisk to start Optimizely Agent

    Button Action
    Look around Move mouse
    Move W, A, S, D
    Jump / move up Space
    Fast mode J
    Pick up crystal Point, left click
    Put down crystal Q

    Explore: Ride a motorbike 🏍, pet the animals 🐖, swim and be free

    Button Action
    Get on motorbike Right click
    Get off motorbike W
    Fast mode J
    Sneak / move down Shift

    The Game

    On day two of Opticon, the DevRel team will be hosting an interactive break after lunch, when we will start a game inside Optiworld for anyone who would like to participate. Of course you can feel free to take the break time for yourself, or play with Minetest on your own!

    Details for the game during the break will be posted in the #Opticode slack leading up to the game.

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  • Join Optimizely co-founder, Pete Koomen, in this hands-on workshop to learn how to uncover insights in data using newly introduced Optimizely tools. Pete will show you how to transform data to inform and drive product-decisions that create compelling user experiences.

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  • A/B testing is an essential element in any product managers playbook. However having the freedom and flexibility to customize testing based on what the data is saying often requires a lot of time and effort, particularly when it comes to engineering resources. Optimizely offers a flexible approach to experimentation through the use of feature testing, which provides more customization options without the additional development effort typically required to implement these feature optimizations. Megan Bubley, a Senior Product Manager at The Zebra, will share her experience working with Optimizely’s feature tests to create a results page where users can compare multiple auto insurance options driven by actual user needs, as well as her experience customizing the experience based on device platform.

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  • Growing from an early stage startup to a national leader in financial literacy is no small feat, and there are a ton of lessons that we have learned at Greenlight as we have grown. Long gone are the days where we would ship something and cross our fingers hoping that it makes some kind of impact on our customers. Now we’re in a world where we can learn ahead of time how much impact a feature will have on the business, before we even launch! In today’s conversation, we’ll discuss how we use Optimizely’s feature flags in our microservice architecture using Optimizely Agent while keeping user IDs and context synchronized.

    This session will cover:

    • How we set up Optimizely Agent and use it in a kubernetes deployment
    • How we created a user-aliasing service
    • How we access Optimizely both on the frontend and in the backend services.
    • How to build a full stack feature
    • How to manage the rollout using Optimizely’s feature flags
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  • Mystique CLI is an Atlassian developed CLI for Optimizely Web. It is a multi-phase project that is currently focusing on improving the development cycle for growth engineers. Currently, Mystique is the standard for developing web experiments at Atlassian, and is capable of a wide variety of operations utilizing Optimizely's REST API. This includes creating, updating, testing, and duplicating experiments/personalization campaigns, as well as "promoting" these entities between Optimizely projects for different environments (e.g. from QA => Prod). It has significantly reduced manual overhead and decreased development time by up to 95% for particular actions.

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