Over the last couple of years, cat videos have become the undisputed champions of the web. Whether it’s kittens playing with their shadows to failed jump attempts to giving each another massages, we’re all guilty of watching a few of these clips from time to time (yes, even at work). Built with this in mind, oCat is a real-time tracker for feline-related activity on the Internet.

oCat consists of two parts: the oCat News Distractor and the Kitty o’Cat Twitter bot. Using Google’s YouTube API, the system works by continuously monitoring for new uploads, the number of new views each day, or a specific video that has received a remarkable amount of attention. It then tweets these stats and prints them out on thermal paper, stamping a paw print on the timeline for every 1,000 views.

Created by Annika Engelhardt, a digital media design master’s student at the University of the Arts in Bremen, oCat uses an Arduino along with an ESP Wi-Fi module, a servo, and an LCD screen. The aim of the project is to increase and reveal the amount of hours people spend watching cat videos online.

The cat is an altered Maneki-neko, holding a stamp using welding wire and hot glue. Even though I filled the stamp with extra ink, it did not work properly and I had to cut out the paw-shape from a sponge and glue it onto the original stamp.

The thermal printer used in the device needs a USB connection, so I used a Raspberry Pi to control it. I wrote a Python script that checks four different RSS news feeds for new posts every 15 minutes and prints one headline with a timestamp every minute.

The Twitter bot was programmed using Python and a library called tweepy. Most of the script is reading JSON files, juggling and comparing data and text files and in the end mixing up parts of a sentence to form a tweet. The bot will be enhanced in the future

Engelhardt exhibited the project at Galerie Flut in Bremen back in October. You can find more pictures and information on the project here.

Woodworking and electronics, automatons and camera sliders. Ben Brandt's YouTube channel offers lots of cool projects.

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At Arduino Day, I talked about a project I and my collaborators have been working on to bring machine learning to the maker community. Machine learning is a technique for teaching software to recognize patterns using data, e.g. for recognizing spam emails or recommending related products. Our ESP (Example-based Sensor Predictions) software recognizes patterns in real-time sensor data, like gestures made with an accelerometer or sounds recorded by a microphone. The machine learning algorithms that power this pattern recognition are specified in Arduino-like code, while the recording and tuning of example sensor data is done in an interactive graphical interface. We’re working on building up a library of code examples for different applications so that Arduino users can easily apply machine learning to a broad range of problems.

The project is a part of my research at the University of California, Berkeley and is being done in collaboration with Ben Zhang, Audrey Leung, and my advisor Björn Hartmann. We’re building on the Gesture Recognition Toolkit (GRT) and openFrameworks. The software is still rough (and Mac only for now) but we’d welcome your feedback. Installations instructions are on our GitHub project page. Please report issues on GitHub.

Our project is part of a broader wave of projects aimed at helping electronics hobbyists make more sophisticated use of sensors in their interactive projects. Also building on the GRT is ml-lib, a machine learning toolkit for Max and Pure Data. Another project in a similar vein is the Wekinator, which is featured in a free online course on machine learning for musicians and artists. Rebecca Fiebrink, the creator of Wekinator, recently participated in a panel on machine learning in the arts and taught a workshop (with Phoenix Perry) at Resonate ’16. For non-real time applications, many people use scikit-learn, a set of Python tools. There’s also a wide range of related research from the academic community, which we survey on our project wiki.

For a high-level overview, check out this visual introduction to machine learning. For a thorough introduction, there are courses on machine learning from coursera and from udacity, among others. If you’re interested in a more arts- and design-focused approach, check out alt-AI, happening in NYC next month.

If you’d like to start experimenting with machine learning and sensors, an excellent place to get started is the built-in accelerometer and gyroscope on the Arduino or Genuino 101. With our ESP system, you can use these sensors to detect gestures and incorporate them into your interactive projects!

In this video we are going to build an Arduino Uno clone in a breadboard using only 5 parts.

Arduino Uno (ATMEGA328P) on a breadboard - [Link]

Lonnie Honeycutt writes:

This is the Nokia 5110 84X48 display that was used on millions of phones in the late 90′s. In this video, I show how to connect the Nokia 5110 LCD to an Arduino Uno, import the correct libraries to the Arduino IDE, and write code to generate text and graphics on the display.

How to use the Nokia 5110 84X48 LCD display with Arduino - [Link]

Last year to celebrate the launch of the new Arduino Starter Kit,  RS Components in collaboration with Arduino,  produced  10 video tutorials featuring Massimo Banzi showing how to create cool projects with the redesigned release of the Kit and all its components.
 

Today RS Components announced on their Twitter and Google+ that the Arduino video tutorials are now marked with a Creative Commons license, that means that you can remix and reuse them as you like.

We created a Playlist on Arduino official Channel and soon we’ll add also German and French subtitles.

XBee Pro 60mW Wire Antenna - Series 1 (802.15.4) 

US$37.95 AND YOU NEED 2

FPV 433Mhz Radio Telemetry Kit 100mW
US$29.99 AND YOU GET A PAIR

Same product from Sparkfun 9 Degrees of Freedom - Razor IMU 

US$124.95

So HK sell this identical device for almost 80% less

But it get worse if are a Kiwi you buy from our local Sparkfun reseller

9 Degrees of Freedom - Razor IMU - AHRS compatible NZ$180.49

To be fair on Mind Kits they do buy from Sparkfun but that said the bloke at Mind Kits has a way worse supply deal then Hobby King