I’ve been using Office 365 for office correspondence for about 7 months, since I moved to Qobo. Today I say enough to Office365 corporate lookalike email client. Everything is back to Google.
One of the things I couldn’t get used to is the right-click bindings. I guess the assumption was to enrich the functionality of the interface by letting you move/delete assets on your sidebar (aka folder management). As the result – half of the browser daily routine is cut off. Most of the time my main working tool (apart of the vim) is the browser (webdev happy days!).
When someone screws up the shortcuts that I use gazillion times per day, it kind of annoys me. Who would have thought to replace Ctrl-R to “reply” shortcut for “refresh“. Ah, bollocks, moving on!
Focused/Other/Pinned emails. Pinned email go to the top of the list. Focused follow right after. Others – somewhere at the end. 6 emails fit into my laptop screen height, so you can get an idea, that the number of pinned or focused emails is quiet limited. I guess, I’m too old school to get these things right!
Search & Filter. Google Email search and filtering is unbeatable. Period.
While working on Google Authenticator, stumbled upon these little facts, why base32 was chosen over base64 for shared secret key:
- The resulting character set is all one case, which can often be beneficial when using a case-insensitive filesystem, spoken language, or human memory.
- The Base32 result can be used as a file name because it can not possibly contain the ‘/’ symbol, which is the Unix path separator.
- The alphabet can be selected to avoid similar-looking pairs of different symbols, so the strings can be accurately transcribed by hand. (For example, the RFC 4648 symbol set omits the digits for one, eight and zero, since they could be confused with the letters ‘I’, ‘B’, and ‘O’.)
- Base32 result excluding padding can be included in a URL without encoding any characters.
Today, we are excited to share the fruits of our research with the broader community by releasing SyntaxNet, an open-source neural network framework implemented in TensorFlow that provides a foundation for Natural Language Understanding (NLU) systems. Our release includes all the code needed to train new SyntaxNet models on your own data, as well as Parsey McParseface, an English parser that we have trained for you and that you can use to analyze English text.
Google announced the release of their English parser earlier today. Great news, let’s see how it will influence the market in the bearest future.
Project Soli page:
The Soli sensor can track sub-millimeter motions at high speed and accuracy. It fits onto a chip, can be produced at scale, and can be used inside even small wearable devices.
An update from TechCrunch on 2016 research results:
Soli has a new trick up its sleeve thanks to researchers at Scotland’s University of St. Andrews (via The Verge) – it can now identify objects, using radar to determine both the exterior shape and internal structure of whatever it’s sensing to tell you what the thing is. It’s not fool-proof, since it has difficulty determining the difference between objects made up of similar material with similar density, and it has to train the system on what an object is before it can be identified.