[n49o7]

Probabilistic feature flags! Love it.

[Thorrez]

Always base the probability on something stable, such as hash of the username.

[gurchik]

You just need to make sure that this doesn't mean people are consistently "lucky" or "unlucky."

I was on a team where app updates were deployed using a canary system. A small percentage of users (say, 1%) received the update first, then the team watched for incoming crash reports from that cohort. If it looked good, the feature was rolled out to a few more people, and this was repeated. This allows you to identify a problem by only negatively impacting a relatively small percentage of customers.

The problem occurs when the calculation to determine which cohort the user belongs to is deterministic. In this case, the calculation was based on the internal ID of the user. This means some users always get the updates first, and deal with bugs more frequently than other users. Conversely, some users are so high in the list that they virtually never get an update until it's been tested by a wide user base, so their experience is consistently stable.

Or you might have a problem where some players in a video game consistently take more damage than their friends: https://news.ycombinator.com/item?id=34742505

[patmorgan23]

You just need to be able to look up what feature flags where enabled on a given request (maybe by correlation I'd)

[IshKebab]

Bug report: changing my username breaks $product.

Yeah no thanks. It's probably better than completely random but software should be predictable and unsurprising.

[thehappypm]

Multi-armed bandits utilize this