KWLUG - The Kitchener-Waterloo Linux User Group is a monthly meeting of GNU/Linux, Free Software, Open Source and technology enthusiasts.

Where? When? We meet in Kitchener, Ontario, usually on the first (non-holiday) Monday of the month, beginning at 7pm. (Directions)

How much? Our meetings are free of charge and open to anybody with an interest in Linux and/or free software.

What next?

Special Event: Cory Doctorow in KW: Monday, Dec 4 2017

Booksigning and evening lecture

Meeting Date

Note: This visit is being organized by the University of Waterloo Cheriton School of Computer Science;, as part of their 50th anniversary celebrations, but the organizers have kindly opened the evening talk to the general public as well as the UW community.

Blogger, activist and author Cory Doctorow will be giving two events in Kitchener-Waterloo on Monday, December 4.

  • At 3pm Doctorow will be doing a reading and book signing at the central branch of the Kitchener Public Library. The event is free but tickets are required. See http://www.kpl.org/85-queen-afternoon-cory-doctorow-ticketed-event to get tickets.
  • At 7pm Doctorow will be giving a talk at the Modern Languages building of the University of Waterloo. It is titled "Dead canary in the coalmine: we just lost the web in the war on general purpose computing" This event is also free, but tickets are required (and the event is expected to sell out). See https://cs.uwaterloo.ca/events/cory-doctorow for the talk description and a link to get tickets.

 

KWLUG Meeting: Monday, December 11 2017, 7pm

Curv, Mattermost

Meeting Date

Note: We have shifted this meeting a week forward. Cory Doctorow will be giving a talk on Dec 4 at the University of Waterloo, and there is some chance that tickets will be made available to the public. In this case many KWLUG members would prefer to attend that talk rather than this meeting.

Doug Moen will tell us about Curv, a 3D modelling language he is developing for making art using mathematics. Jonathan Fritz will tell us about his work on Mattermost, an open-source alternative to Slack.

Doug writes:

Curv is an open source 3D solid modelling language, oriented towards 3D printing, procedurally generated art, and mathematical visualization. It's a pure functional language where geometric shapes are first class values, and are constructed by transforming and combining simpler shapes using an unusually rich collection of operators.

Instead of polyhedral meshes or other boundary representations, Curv represents shapes as pure functions (Function Representation or F-Rep). This is a volumetric representation, where a function maps every point (x,y,z) in 3D space onto the properties of a shape. This representation is powerful, supporting a wide range of shape operators, and is a good match to the volumetric nature of 3D printing.

F-Rep is well suited to being directly rendered by a GPU. To achieve this, Curv code is compiled into GPU shader programs or compute kernels.

Jon writes:

Mattermost is an open source alternative to popular enterprise chat applications like Slack, HipChat, and Microsoft Teams. It can be used on the web, your desktop, or your mobile device. The project is MIT-licensed, with an enterprise version available for paid customers that need additional functionality.

KWLUG Meeting: Monday February 5 2018, 7pm

Data Visualization with ElasticSearch

Meeting Date

Mary Loubele will lead an interactive demonstration of how to use Elasticsearch to visualize the Twitter API. Bring your laptop loaded with VirtualBox. You will also need a Twitter login to access the API. A VM for you to import, as well as instructions for getting started, will be released closer to the presentation date. (If it is fewer than two weeks until the presentation and there are no links here, please remind us via the contact page).

The demonstration will cover the following topics:

  • How to install Elasticsearch and Kibana on a virtual machine.
  • How to get Twitter data from the Twitter API into Elasticsearch.
  • How to build insightful visualizations with Kibana.
  • How to perform text classification with Elasticsearch.