Showing posts from 2019

Unboxing the Electron

Particlehas been making huge ripples in the IoT community lately, and this week we had a chance to unbox their product, the Electron.

The Electron (starting at $49USD), unlike their Wifi predecessors, operates on the GSM (cell phone) networks. For hobbyists, prototypers and companies looking to scale devices that need communication, the freedom of not having to be tethered to a Wifi access point is a great feature. Now you can monitor your geocache, track your bike, or record the weather from the top of your office. Sometimes WiFi is far enough out-of-range to be a pain, and the Electron provides huge flexibility.

The Electron can be programmed using the familiar Arduino-esque syntax through their web IDE, and it can be flashed over the cloud just like the Photon. This does use the data plan however, so it is recommended that you program the device over USB. And yes, you can use your own compiler and IDE!

Particle's recent addition of the Electron is a fantastic step forward, and the…

Nivrta Industries

About Nivrta:
Nivrta provide economical, yet high quality reusable sanitary cloth napkins. They are backed by PUL fabric, so no worries of leaks. We take pride in being designed and manufactured entirely in India. We use locally and internationally sourced high quality fabrics and employ local tailors to create our products. We promote menstrual practices that are healthy, dignified, affordable and  eco-positve. We are based in Coimbatore, India.        Available in three different sizes for use during day and night and pantyliner too, you can manage your entire cycle with Nivrta. 

Observe Your Backyard Birds with a Custom ML Model

Story(re. Hackster.) Many developers are curious about using machine learning techniques in their apps. Digging into the processes involved, it becomes clear that you need to first define the problem that you're trying to solve and ensure that you need a machine learning model in the first place. For many visual identification tasks, it's obvious that machine learning ("ML") is very helpful, as it allows a computer to "learn without being explicitly programmed". Rather than requiring a program to identify every bird by feeding an image of every existing creature into a database, we can use a technique of building a ML model which is an algorithm trained to recognize patterns and make 'educated guesses'. Fed plenty of good quality images of cardinals, for example, a ML model would presume that the red bird at the feeder would most likely be a cardinal, and not a blue jay, based on patterns it has deduced in the images it has 'seen'. For some p…