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. (Virtual Directions) (Subscribe to monthly meeting announcements)

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

What next?

KWLUG Meeting: Monday, Oct 2, 2023, 7pm

PrivacySafe, Game Wave Emulation

Meeting Date

We will hold this meeting online, and at the University of Waterloois to be determined. Stay tuned. location.

Mikalai Birukou will offer an interactive demo of the latest version of PrivacySafe, his privacy-protecting web platform. Audience members a will be invited to follow along by downloading the PrivacySafe clients on their own computers and interacting with the features. Low- or zero-touch clients are available for MacOS, Linux, and Windows.

Nathan G will tell us about his efforts to emulate the Game Wave Family Entertainment System, a Canadian-designed video game console. 

In-Person Social: Wednesday, October 18, 2023

Dinner at McCabe's Kitchener

Meeting Date

We are continuing the dinner meetup tradition at McCabe's Irish Pub and Grill in Kitchener, starting at 7pm. Note that this dinner is on a Wednesday. Future restaurant meetups will alternate between Mondays and Wednesdays.

This is a supplementary informal meeting; we will still hold virtual meetings for technical topics.

The address is 352 King Street West, Kitchener, ON, near King and Francis streets.

You can probably just show up, but the organizers would find it helpful if you RSVPed to .

Parking at City of Kitchener parking lots is free after 5pm. The nearest City parking lot is at 28 Water Street South.

KWLUG Meeting: Monday, Nov 6, 2023, 7pm

Machine Learning for Observability + 1 more

Meeting Date

Note: The location for this meeting is TBA, but will likely be hybrid. Stay tuned.

Andrew Maguire from NetData will discuss "10 practical machine learning use cases in observability!" He writes: 

I will walk through 10 practical, clearly motivated and formulated use cases for ML in observability. What is the problem, what are the inputs and what are the outputs. The main overarching theme is that of “ML as UX”  being an approach that actually can be very useful for us once we cut through all the buzz and hype from our marketing friends. These use cases give us “humans in the loop” more tools that sometimes might help us solve our problem quicker and less painfully. Let’s go beyond dashboards and begin to look at useful, mature, practical and realistic ways we can make ML less sexy and more boring - just another tool we can leverage to get on with our day quicker.