Tag: windows

Try Hack Me – Cyber Security Challenges

Try Hack Me – Cyber Security Challenges

Sometimes I just stumble across these random resources that I immediately want to share with fellow geeks. If you like computers and programming, you should definitely have a look at…


TryHackMe started in 2018 by two cyber security enthusiasts, Ashu Savani and Ben Spring, who met at a summer internship. When getting started with in the field, they found learning security to be a fragmented, inaccessable and difficult experience; often being given a vulnerable machine’s IP with no additional resources is not the most efficient way to learn, especially when you don’t have any prior knowledge. When Ben returned back to University he created a way to deploy machines and sent it to Ashu, who suggested uploading all the notes they’d made over the summer onto a centralised platform for others to learn, for free.

To allow users to share their knowledge, TryHackMe allows other users (at no charge) to create a virtual room, which contains a combination of theoretical and practical learning components.. In early 2019, Jon Peters started creating rooms and suggested the platform build up a community, a task he took on and succeeded in.

The platform has never raised any capital and is entirely bootstrapped.


I don’t have any affiliation or whatever with the platform, but I just think it’s a super cool resource if you want to learn more about hands-on computer stuff.

Here’s a nice demo on an advanced programmer taking on one of the first challenges. I definitely still have a long way to go, but it’s fun to watch someone sneak into a (dummy) server and look for clues! Like a proper detective, but then an extra nerdy one!

There are many “hacktivities” you can try on the platform.

And if you’re serious about learning this stuff, there are learning paths set out for you!

If you like their content, do consider taking a paid subscription and share this great initiative!

ArchiGAN: Designing buildings with reinforcement learning

ArchiGAN: Designing buildings with reinforcement learning

I’ve seen some uses of reinforcement learning and generative algorithms for architectural purposes already, like these evolving blueprints for school floorplans. However, this new application called ArchiGAN blew me away!

ArchiGAN (try here) was made by Stanislas Chaillou as a Harvard master’s thesis project. The program functions in three steps:

  1. building footprint massing
  2. program repartition
  3. furniture layout
Generation stack image
Stanislas’ three generation steps

Each of these three steps uses a TensorFlow Pix2Pix GAN-model (Christopher Hesse’s implementation) in the back-end, and their combination makes for a entire apartment building “generation stack” — according to Stanislas — which also allows for user input at each step.

The design of a building can be inferred from the piece of land it stands on. Hence, Stanislas fed his first model using GIS-data (Geographic Information System) from the city of Boston in order to generate typical footprints based on parcel shapes. 

Model 1 results image
The inputs and outputs of model I

Stanislas’ second model was responsible for repartition and fenestration (the placement of windows and doors). This GAN took the footprint of the building (the output of model I) as input, along with the position of the entrance door (green square), and the positions of the user-specified windows.

Stanislas used a database of 800+ plans of apartments for training. To visualize the output, rooms are color-coded and walls and fenestration are blackened.

Model II results image
The inputs and outputs of model II

Finally, in the third model, the rooms are filled with appropriate furniture. What training data Stanislas has used here, he did not specify in the original blog.

Model III results image
The inputs and outputs of model III

Now, to put all things together, Stanislas created a great interactive tool you can play with yourself. The original NVIDEA blog contains some great GIFs of the tool being used:


Stanislas’ GAN-models progressively learned to design rooms and realistically position doors and windows. It took about 250 iterations to get some realistic floorplans out of the algorithm. Here’s how an example learning sequence looked like:

Architectural sequence image
Visualization of the training process

Now, Stanislas was not done yet. He also scaled the utilization of GANs to design whole apartment buildings. Here, he chains the models and processes multiple units as single images at each step.

Apartment building generation pipeline image
Generating whole appartment blocks using ArchiGAN

Stanislas did other cool things to improve the flexibility of his ArchiGAN models, about which you can read more in the original blog. Let these visuals entice you to read more:

GAN-enabled building layouts image
ArchiGAN scaled to handle whole appartment blocks and neighborhoods.

I believe a statistical approach to design conception will shape AI’s potential for Architecture. This approach is less deterministic and more holistic in character. Rather than using machines to optimize a set of variables, relying on them to extract significant qualities and mimicking them all along the design process represents a paradigm shift.

Stanislas Chaillou (via)

I am so psyched about these innovative applications of machine learning, so please help me give Stanislas the attention and credit he deserved.

Currently, Stanislas is Data Scientist & Architect at Spacemaker.ai. Read more about him in his NVIDEA developer bio here. He recently published a sequence of articles, laying down the premise of AI’s intersection with Architecture. Read here about the historical background behind this significant evolution, to be followed by AI’s potential for floor plan design, and for architectural style analysis & generation.

Evolving Floorplans – by Joel Simon

Evolving Floorplans – by Joel Simon

Joel Simon is the genius behind an experimental project exploring optimized school blueprints. Joel used graph-contraction and ant-colony pathing algorithms as growth processes, which could generate elementary school designs optimized for all kinds of characteristics: walking time, hallway usage, outdoor views, and escape routes just to name a few.

Two generated designs, minimizing the traffic flow (left) as well as escape routes (right) [original]
Other designs tried to maximize the number of windows, resulting in seemingly random open courtyards [original]

The original floor plan [original]
Definitely check out the original write-up if you are interested in the details behind the generation process! Or have a look at some of Joel’s other projects.