Atari

Great stories about the creation of the Atari ST: part 1 and part 2. I still remember fondly the ST, TT030 and Falcons... (The TT030 my dad had still works, afaik). I also remember the huge Megafile 30 on top of the MegaST -- an incredible thirty megabytes of disk space ! Why is it that I am now always short on space :)

...and snow in london

After the south of France, it's now London's turn...

It apparently did not snow as much in 18 years -- London is very quiet today!

update:

update2, going to work:

Snow in Provence

A friend just sent me this picture:

...taken from her window, near Marseilles, south of France. Impressive isn't it ? highway closed, etc.

Usually happens every ten years or so :)

edit: another friend took the following pics:

edit 2: check that video...

A Modern Objective-C Runtime

David just got his paper describing his new Objective-C runtime for etoile published in the Journal of Object Technology. Congrats :)

Mona Lisa video

Here is a small video demonstrating the polygons evolution:

(better results should be doable with more time and/or more parameters tweakings)
For this example, I used 50 polygons of at most 16 points, starting with 10 polygons.

Genetic Algorithms and Mona Lisa

Genetic Algorithms try to apply evolution mechanisms to find solutions to hard problems (typically, where no "proper" solution is known and where the search area is large).

Roger Alsing posted a couple of days ago an extremely cool article showing the convergence of 50 polygons to represent the Mona Lisa, using a random approach.

That was too cool to not try to implement it :)

The screenshot shows a rendering of the Mona Lisa using 50 polygons (16 points each), after 40818 total iterations, with 4577 elected states; the middle image is the original (i.e. the target) and the right image the difference between the current polygon-based image and the target (i.e. a representation of the fitness function).
Underneath was an earlier attempt using ovals instead of polygons.

Now, to be more exact, Roger Alsing's algo is more a hill climber algorithm or possibly a simulated annealing algorithm than a good example of a genetic algorithm; it should be interesting to actually implement a proper genetic algorithm approach (i.e. a population > 1) and see how the convergence rate compares... combining polygons and ovals might also result into interesting things.