February was the month of feature flags! Last month we rolled out our new free feature management offering so naturally we wanted to dive deeper into why Statsig loves feature flags and why you should too.
This virtual meetup offered insight into some really interesting customer use cases, some history on Statsig’s SDK architecture, and answered all of our audience's hard-hitting questions.
Feature flags are table stakes when it comes to building and optimizing products.
Statsig product manager MA Seger and Engineering Lead Tore Hanssen discuss how feature flags work, best practices for using them, and what makes Statsig’s Feature Management offering unique.
They also cover some of the early architecture decisions Statsig’s SDK team made when building our feature flagging infrastructure, as well as how customers continue to evolve our platform in creative ways. Enjoy this on-demand viewing and we hope you can join us live in the future!
AI technology has been here for years, but the new wave of AI products and features is game-changing. We covered this, and other topics, at the Seattle AI Meetup.
Building a culture of experimentation benefits greatly from things like reviewing experiments regularly and discussing the results.
Thanks to our support team, our customers can feel like Statsig is a part of their org and not just a software vendor. We want our customers to know that we're here for them.
Migrating experimentation platforms is a chance to cleanse tech debt, streamline workflows, define ownership, promote democratization of testing, educate teams, and more.
Calculating the right sample size means balancing the level of precision desired, the anticipated effect size, the statistical power of the experiment, and more.
The term 'recency bias' has been all over the statistics and data analysis world, stealthily skewing our interpretation of patterns and trends.