riri-au

It's useful for handling sites with really long login flows, and multiple accounts, so I use them as the exception, rather than the rule.

Eg: AWS I have a work and personal account, and their login flow sucks, but the work and personal containers handle this situation easily.

One thing I've found really really useful is handling multiple google users over multiple google sites.

I want youtube & google music to only use my personal account, so I never need to switch accounts, so google music and youtube open in their own container where only my personal account is logged in.

I want gmail and calendar to use my work account, so they open in the default container and automatically use my work google user.

I know that you can set your default google account by logging into it first, but I specifically don't want one default, I want different defaults for different google services, and that use case is handled beautifully by containers.

Agreed, shared programming is an immensely useful feature for numerical programming, including data science and machine learning. Lots of people will say that those should be written in C++, but I think the rise of machine learning & data science in high level languages argues against their point.

Hi everyone, I wrote this post primarily because

- Optaplanner's docs assume quite a bit of sophistication, and it was quite difficult to get up and running with the tool

- It's a really nice example of where even simple, day to day problems can be solved with computers. This was a real, genuine problem that I personally had. Most of the time I deal with work problems that are a little more abstract, but this is really direct and personal.

Also I know some people have strong opinions about the burn :). But I'm pretty sure no one has strong opinions about water planning and desert safety, and hopefully they balance out. ¯\_(ツ)_/¯

I guess I was a bit harsh, fair call.

I'll respond this way: basic BI infrastructure is the bedrock of an organization's data capabilities. The best way for an organization to build their initial BI/DS capability is to address some business problem, by building a warehouse. That's in contrast to building a warehouse and then looking for problems to solve with it.

So you're right, thanks for taking time to comment :)

That's a really good point, and I've seen that myopia cause problems as well. Like you say, it's about understanding the limits and applicability of data science, and how it interacts with experience and qualitative strategy.

You preempted my followup article. I'm not sure where the balance between these two is, but I'm sure that many places get it wrong.

Firefox already does this - that's why it's the awesome bar. You can see it in action here https://youtu.be/pKE0Oc3yIBs?t=53

Yep! No need for extensions, you can see it working here: https://youtu.be/pKE0Oc3yIBs?t=53

That video is a classic, I'm pretty sure most Australians watched it in school, and remember this scene: https://youtu.be/rm3hd1pxHME?t=44