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A curated summary of the top questions asked on our Slack community, often relating to implementation, functionality, and building better products generally.
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GENERAL

Can Statsig flag likely misconfigurations in feature gate rules to prevent accidental errors?

Date of slack thread: 4/23/24

Anonymous: Hi folks, I had a feature request to float here – wondering if it would be possible to flag likely misconfigurations in feature gate rules to help prevent accidental errors. For example, setting up a rule that checks if Unit ID is “Any of” an empty list but has a non-zero Split % seems very likely to be a bug rather than an intentional configuration.

Tore (Statsig): Excellent feedback, thanks for sharing. We had even talked about not letting you specify an empty “any of” values regardless of split percentage (given is null or is not null is a separate operator).

Tore (Statsig): Thinking a bit more, it would still split 50/50 based on the id type of the gate (which could be different than the criteria here). So I’m not sure the rollout percentage is necessarily a misconfiguration. When you did this, what were you expecting to happen?

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Ancestry Ancestry
At OpenAI, we want to iterate as fast as possible. Statsig enables us to grow, scale, and learn efficiently. Integrating experimentation with product analytics and feature flagging has been crucial for quickly understanding and addressing our users' top priorities.
OpenAI
Dave Cummings
Engineering Manager, ChatGPT
Brex's mission is to help businesses move fast. Statsig is now helping our engineers move fast. It has been a game changer to automate the manual lift typical to running experiments and has helped product teams ship the right features to their users quickly.
Brex
Karandeep Anand
President
At Notion, we're continuously learning what our users value and want every team to run experiments to learn more. It’s also critical to maintain speed as a habit. Statsig's experimentation platform enables both this speed and learning for us.
Notion
Mengying Li
Data Science Manager
We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration. We wanted a complete solution rather than a partial one, including everything from the stats engine to data ingestion.
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SVP, Data & Platform Engineering
We only had so many analysts. Statsig provided the necessary tools to remove the bottleneck. I know that we are able to impact our key business metrics in a positive way with Statsig. We are definitely heading in the right direction with Statsig.
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Partha Sarathi
Director of Engineering
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