Cocktail Technology and Robot Bartenders

Project Gutenberg

It doesn’t exactly mix up your drinks, but it is taking baby steps in providing an automated way to check inventory and automatically restock inventory. The recyclable alcohol dispensers, shipped to your doorstep, provide a compact and elegant tabletop display. An iPad app displays the recipe, and all you have to do is dispense and mix.

The Barman Smart Cocktail Shaker

This device connects via BlueTooth to your phone to help guide pours to perfect amount… just in case you are too lazy to keep track of your jiggers.



This Kickstarter raised almost $200K and successfully shipped their Raspberry Pi-powered robot bartender over the summer of 2013.


Another DIY [Arduino-powered] robot bartender project raised $50K on Kickstarter and plans to ship summer of 2015.



Order your own robot bartender either unassembled or RTP (ready to pour) for about $1,450.

Open Source Machine Learning: Try out Prediction IO

Machine learning is all the rage, but very few shops understand enough about it to implement an effective solution. Machine learning involves having an expert on the team that understands all the mathematical algorithms used to analyze data and map it back to users. Explaining machine learning to the masses, has often reduced complex programs to mere ‘Related Articles’ or ‘Books you might like’ examples. Developers new to machine learning, then gain a false sense of confidence after home-brewing a recommender engine – but they’ve only scratched the surface.

Prediction IO takes all the complex math out of the equation and bundles a machine learning server into an open source platform to store user data. Here are some screenshots that Shekhar Gulati posted to illustrate the basics of Prediction IO.


1. Add an App

2. Setup App Key

3. Create an Engine

4. Collect the Data and Train the Model


The adding the engine will requires setting up the Prediction IO server, then writing client code to store user data and attributes to the server, then retrieving recommended data. This solution puts machine learning into the hands of Web developers without the need for a math programmer guru. It seems like a good start, but will still require many hands on deck to get something like this rolled out with any longterm meaning.


Top Picks from TC Disrupt 2012 in San Francisco

So many great startups showcased at the 2012 TechCrunch Disrupt in San Francisco. Here are my favorites:

GYFT – Digital Gift Card Wallet. They address the problem that plastic gift cards are often difficult to spend. All of us recall the gift card left on the dresser for years until it gets lost or tossed. I used to think this ‘breakage’ was the strategy behind gift cards, it has come to my attention that revenue a business collects that go unspent, actually gets donated to the state in which they were issued. The company offers a mobile app that lets you store your card so you have it whenever you need it. A neat upcoming feature would be located-based reminders if you are near a store where you might want to spend your credit.

While cars are going electric, this small startup claims the lightest electric transportation device weighing in at 14 lbs. The drawback is the driver needs to be skilled at longboarding.

Checkout the video:

BoostedBoards too hipster? The Segway too geeky? Lit Motors has showcased some amazing technology they refer to as ‘balance dynamics’, the use of gyroscopic technology to help stabilize yet another some-what scary electric vehicle.

Need a ‘wrench’ at your doorstep to get your junker on the road? No fear – there is – a mobile app that gets you a mechanic on demand.

And for the political Web-savvy crowd, there is a site for launching campaigns.