AlaShiban

Haven't used AWS/Google yet for the same set up, we've customized the Azure set up to include tens of TBs of disk space, over 100 compute nodes with quite a bit of RAM. I'd say for any reasonably sized start up or company, it's a good choice - plus the Mesosphere stack also helps avoiding lock-in to one vendor. (would love to see them making multi-cloud tooling)

We're running significant loads on Azure with Mesos+Docker (Azure Container Service). Mesosphere's stack removed the Docker runtime and only uses Docker for image packaging. Stable and in production for over 8 months now.

The mobo chair mount has been the most transformative product that reduced/eliminated RSI for me. For years, I tried to find better tables, better chairs, better mice/keyboards, postures -

The problem turned out to be in the seams between products, not the individual pieces.

The mobo mount solves this by throwing away the table component from the keyboard/mouse equation - solving most posture problems. Try it out.

https://www.amazon.com/Mobo-Chair-Mount-Keyboard-System/dp/B...

We'll always need fantastic developers and engineers :) The goal is to help you find good samples, whether it makes sense and how its used is still very much open to how good (or not) a developer is

We're worried about that too. What we're hoping to do as we progress is to use best practices as metrics. Because we can do semantic matching, and not just textual matching, there's a world of possibilities to make results more helpful

Static analysis is part of how we grade the samples (using Roslyn). Obviously this tool isn't a magic wand, the goal is to get you as close as possible to relevant results that you can dig deeper into

A core difference here is the contextual and semantic understanding of the code. The way we search and rank ties into alot of coding metrics and semantic parsing of the code around the web using the new 'Roslyn' C#/VB compiler-as-a-service technology.

Most other experiences base their search algorithms on search engines / textual matching / rely on the host web site's search accuracy, where as we combine search engine smarts with semantic and contextual analysis.

We try and avoid those cases, but it's possible yes. The semantic and contextual approach help you find relevant samples and discussions that are highly relevant to your code, you still have to vet whether the sample makes sense. (Visiting the original sample page is likely to shed more light on those cases where we mis-label)

The url to the original sample is visible on the bottom right. That said, continuing the optimizations and smarts around the code metrics and semantic understanding will be an ongoing effort around the service. But yeah, we still have stuff to improve

There's alot more that can be done with the technology. We wanted to start with a few core partners and drive the quality and relevancy up as we go - so, can we? Sure. It's a matter continuously improving the service

And, agreed, MSR does some cool stuff :)

If you want more info about the extension let us know. There's alot of cool contextual and semantic pieces in there that makes it a smarter search