“koala bear eating eggs benedict in a bistro” is the prompt I entered into OpenAI’s DALL·E system to generate this image. I have been reading articles about AI image generation since DALL·E 2 launched earlier this year, and have been experimenting with it hands-on since I received access earlier this week. It is lots of fun, but doesn’t always generate the results you might expect. I’ve been trying to describe to it artist Henri Julien’s Chasse-gallerie, a drawing of 8 voyageurs flying in a canoe at night, and DALL·E struggles with the flying canoe. For each prompt, DALL·E initially creates 4 image variations - I’ve selected the most interesting one for each below.

I have been in IT “war room” type situations a number of times, working to get service to a production system restored. It was with professional interest that I followed the Rogers outage on July 8th, 2022.

For anyone looking for more information than what was covered in the media, Rogers’ response to the CRTC’s questions about the incident was published and can be downloaded from the CRTC (the .DOCX link on the July 22, 2022 post).

I recently participated in GeekWeek 7.5, the Canadian Centre for Cyber Security’s (CCCS) annual cybersecurity workshop. I was assigned to a team of peers in banking, telecom, government and academia. We were to work together on analyzing how Bluetooth item trackers (eg: Apple AirTags, Tile) can be covertly used for malicious purposes, and developing processes and tools to detect them.

It was my first time attending the event, and I wasn’t sure what to expect. Here’s how it worked. The CCCS builds teams from the pool of GeekWeek applicants, based on interests and skills identified by the applicant in the application process. Leading up to the event, CCCS appoints a team lead, who defines goals for the team.

Ever since I first read about Hackinthoshes, I’ve thought about building one. A friend of mine edits all of his video on a purpose built Hackintosh. I never did build one - for myself, I like to run Linux, I don’t really need a Mac for anything, and I find that off-lease corporate grade laptops are the best value in computing. But, every once in a while, I have something I want to build on my iPhone, and a Mac is like a dongle that makes it possible.

I wanted a hard copy of an eBook I had that is out of print. There are many resources out there for binding books. Many recommend using acid free PVA glue. I can’t speak to how it compares to other glues, but “Aleene’s Tacky Glue” is a PVA glue, available acid free, which was available at craft stores in my area.

This post will focus on prepping an eBook for print. As US Letter is the common paper size here, which is too big for a book, I decided to print 4 pages per US letter page, 2 pages per side, each 5.5" wide by 8.5" tall.

I wanted to build my own vision model for a few reasons:

1. I wanted to learn how
2. In my limited experience with OpenALPR, it looked like it was missing some license plates that seemed fairly readable to my eyes - could I possibly do better training my own model?
3. Just the way it is built, I know I wouldn’t be able to get OpenALPR to run faster on my Pi - I wouldn’t be able to get it to run faster by off loading image processing to a VPU like the Myriad X in my Oak D camera.
4. The gen2-license-plate-recognition example provided by Luxonis, built from Intel’s Model Zoo, does not work well with Ontario license plates

The first step was building a library of images to train a model with. I sorted through hundreds of images I’d taken on rides in September, and selected 65 where the photos were clear, and there were license plates in the frame. As this was my first attempt, I wasn’t going to worry about sub-optimal situations (out of focus, low light, over exposed, etc…). I then had to annotate the images - draw boxes around the license plates in the photos, and “tag” them as plates. I looked at a couple tools - I started with Microsoft’s VoTT, but ended up using labelimg. Labelimg was efficient, with great keyboard shortcuts, and used the mouse scroll wheel to control zoom, which was great for labeling small details in larger photos.