Number one is the fact that the IPython notebook was used by pretty much everyone. I use it a lot myself, but I didn't realize how ubiquitous it has become. It is quickly becoming the standard now. The IPython notebook is using Markdown and in fact it is better than Rest. The way to remember the "()" syntax for links is that in regular text you put links into () parentheses, so you do the same in Markdown, and append  for the text of the link. The other way to remember is that  feel more serious and thus are used for the text of the link. I stressed several times to +Fernando Perez and +Brian Granger how awesome it would be to have interactive widgets in the notebook. Fortunately that was pretty much preaching to the choir, as that's one of the first things they plan to implement good foundations for and I just can't wait to use that.
It is now clear, that the IPython notebook is the way to store computations that I want to share with other people, or to use it as a "lab notebook" for myself, so that I can remember what exactly I did to obtain the results (for example how exactly I obtained some figures from raw data). In other words --- instead of having sets of scripts and manual bash commands that have to be executed in particular order to do what I want, just use IPython notebook and put everything in there.
+Aaron Meurer and I have done the SymPy tutorial (see the link for videos and other tutorial materials). It's been nice to finally meet +Matthew Rocklin (very active SymPy contributor) in person. He also had an interesting presentation
about symbolic matrices + Lapack code generation. +Jason Moore presented PyDy.
It's been a great pleasure for us to invite +David Li (still a high school student) to attend the conference and give a presentation about his work on sympygamma.com and live.sympy.org.
It was nice to meet the Julia guys, +Jeff Bezanson and +Stefan Karpinski. I contributed the Fortran benchmarks on the Julia's website some time ago, but I had the feeling that a lot of them are quite artificial and not very meaningful. I think Jeff and Stefan confirmed my feeling. Julia seems to have quite interesting type system and multiple dispatch, that SymPy should learn from.
I met the VTK guys +Matthew McCormick and +Pat Marion. One of the keynotes was given by +Will Schroeder from Kitware about publishing. I remember him stressing to manage dependencies well as well as to use BSD like license (as opposed to viral licenses like GPL or LGPL). That opensource has pretty much won (i.e. it is now clear that that is the way to go).
I had great discussions with +Francesc Alted, +Andy Terrel, +Brett Murphy, +Jonathan Rocher, +Eric Jones, +Travis Oliphant, +Mark Wiebe, +Ilan Schnell, +Stéfan van der Walt, +David Cournapeau, +Anthony Scopatz, +Paul Ivanov, +Michael Droettboom, +Wes McKinney, +Jake Vanderplas, +Kurt Smith, +Aron Ahmadia, +Kyle Mandli, +Benjamin Root and others.
It's also been nice to have a chat with +Jason Vertrees and other guys from Schrödinger.
One other thing that I realized last week at the conference is that pretty much everyone agreed on the fact that NumPy should act as the default way to represent memory (no matter if the array was created in Fortran or other code) and allow manipulations on it. Faster libraries like Blaze or ODIN should then hook themselves up into NumPy using multiple dispatch. Also SymPy would then hook itself up so that it can be used with array operations natively. Currently SymPy does work with NumPy (see our tests for some examples what works), but the solution is a bit fragile (it is not possible to override NumPy behavior, but because NumPy supports general objects, we simply give it SymPy objects and things mostly work).
Similar to this, I would like to create multiple dispatch in SymPy core itself, so that other (faster) libraries for symbolic manipulation can hook themselves up, so that their own (faster) multiplication, expansion or series expansion would get called instead of the SymPy default one implemented in pure Python.
Other blog posts from the conference: