sdague

I did some experimenting with Watson and Google using podcasts, and Watson came out pretty well in the mix - https://dague.net/2017/06/12/comparing-speech-recognition-fo... and https://dague.net/2017/06/13/visualizing-watson-speech-trans.... I haven't done systematic analysis with a lot of data points yet.

You can get per word confidence levels back from the Watson API if you ask for it, which is useful to know what parts it is sure about and what parts it is not.

Disclaimer: I work at IBM. But feel free to reach out at [email protected] if you want some help debugging on the Watson STT side.

Also, there is a lot of inertia in the home automation space. Hue is all Zigbee, and is one of the biggest off the shelf IoT platforms right now. The door lock space is mostly z-wave. Friends that I have that have a mix of these things are more and more putting their energy into z-wave as it's far more reliable than wifi, having the advantages of the mesh network, and living in a non crowded spectrum.

That's what the AppDaemon project is about - https://home-assistant.io/docs/ecosystem/appdaemon/ - it let's you do the automation rules inside code instead of inside yaml. You'll still need/want yaml for basic device connections and grouping, but doing complex automations can be done easier in AppDaemon.

Thank you for that link. I knew the old stuff didn't work with new ipython, but I didn't realize development kept going somewhere.

Because the calendar used to start in March. Feb was truncated because it was the last month of the year, thus where to put all the fudge factor to make the year work right.

Yes, there are plenty of local maxima, but this is a pretty nice look at the global picture - http://redmonk.com/sogrady/2013/12/19/dvcs-and-git-2013/ - and git definitely won the mindshare war by those numbers.

My experience has been about a month.