wkonkel

The Crystal-Lang developers have a fundraiser going and Matz (creator of Ruby) has already contributed $500:

https://salt.bountysource.com/teams/crystal-lang

It's also available on the web for any project, for example:

https://www.bountysource.com/teams/bountysource/issues

The Bountysource browser extension is similar. It works with existing votes and +1's from GitHub, Bugzilla, Trac, etc.

https://www.bountysource.com/extension

I heard a similar story in the past but with the culprit being a janitor turning on their vacuum every morning at 6am causing power spikes.

Bountysource has a browser extension which does this:

https://www.bountysource.com/extension

Have you seen Bountysource?

https://www.bountysource.com/

More active discussion on reddit -- http://www.reddit.com/r/javascript/comments/2r6gzk/javascrip...

pagekite

I just posted a $15 bounty to "Make it a real app" -- https://github.com/captbaritone/winamp2-js/issues/2

To explain this in a different way, let's use a simplified example:

Suppose I have a website with a "Click Me" button that's green in color. I want to increase clicks and think to myself, "perhaps if it was a red button instead of a green button, more people would click!" To test this, I would run an A-B test along the lines of:

if random(2) == 0 then color='red' else color='green';

In theory, I just push this code and track the number of clicks on the red button versus the green button and then pick the best. But in practice, when I push the code, there might be 5 clicks on green and none on red in the first hour. Maybe green is better? Maybe I didn't wait long enough? Okay, let's wait longer. A few hours later, there's now 10 clicks on red and only 6 clicks on green. Okay, so red is better? Let's wait even longer. A week later, there's 5000 clicks on red and 4500 clicks on green. That seems like enough data that I can make a conclusion about red vs. green. But is there a better way?

This is where A-A-B-B testing can help. Let's start by looking at just the A-A part of the test. If I split my audience into two groups (green1 and green2) and show them both green buttons, the results should be identical because both buttons are green. If I check back in an hour and the "green1" and the "green2" groups are off by 20%, then I have a large margin of error and need to wait longer. If I check back in 6 hours and they're off by 10%, then I need to wait longer. If I check back in a day and green1 and green2 are only off by 1% then that means we've probably waited long enough and my margin of error is around 1%. I can now add green1+green2 and compare it to red1+red2 groups and see if there's a clear winner (e.g. red is 5% better). And this only took a day instead of a week!

A simple hack is to run an A-A-B-B test instead of an A-B test. Rather than splitting 50-50, use 25-25-25-25 splits. When A1==A2 and B1==B2, then you know that you have statistically relevant data and you can compare A to B. Depending on the dataset, this could happen in minutes or weeks.

They should have used www.bountysource.com!

I'm one of the co-founders of BountySource and would be happy to answer any questions about our service. We're also on irc.freenode.net in #bountysource if you want to come chat!

We mocked out the API layer to achieve the demo so it'll be slightly slower when it has to make real requests to our API. That said, the live version is quite snappy!

Give Badger.com a try!

Give Badger a try... we salt and hash passwords. http://badger.com/