Update (2008.08.06): I have posted the slides I have available from the minisymposium described below.
The 2008 SIAM annual meeting was held July 7-11 in San Diego, and we had a healthy presence of Python-based tools for scientific computing. Randy LeVeque from U. Washington and I co-organized a 3-part minisymposium entitled Python and Sage: Open Source Scientific Computing. You can see the list of talks for parts I, II and III online. I should add that for the talks where we had cancellations, we ended up with excellent impromptu replacements by Bill Hart from Sandia National Labs and Travis Oliphant from Enthought.
Our sessions were well attended (especially considering the very large number of parallel sessions at the conference) and generated lively discussion. There's clearly a need in the scientific and mathematical community for open source, high level tools that integrate well with existing high performance codes written in Fortran, C or C++, while providing interactivity, visualization, access to the network, etc. This is the kind of idea that many of us in the SciPy and Sage worlds have been preaching for a while, but now the response from the wider community is much more positive than before (admittedly, years ago the tools were immature enough that they were very much of the "user assembly required" variety).
I am extremely pleased to report that we were chosen for the conference highlights page (many thanks to John Hunter, of matplotlib fame, for the great screenshot)! Given that this page mostly lists the invited plenaries and other special events, I am very happy about this (SIAM had literally hundreds of talks from parallel sessions to choose from and only two of those were highlighted). I think it is both a recognition of the work done by all our speakers, and of the relevance of the topic today. Thanks again to all who contributed. We should take this level of interest as both a recognition an a challenge: as more scientists become interested in these tools for their everyday research, the pressure on high quality documentation, easy installation and deployment, good tutorials, etc, will only increase.
Everyone is working hard on this, and in particular it's worth mentioning the excellent work that is being done this summer as part of the NumPy/SciPy Documentation Marathon 2008, where a lot of new documentation has been added to NumPy. This is an effort where anyone can contribute! The infrastructure is just a wiki where you can register to edit docs, and developers will later review and merge your contributions into the core project for the next release.
So let's all keep up the momentum going, and I hope to see many more interested faces, both new and old, at the upcoming SciPy 2008 conference at Caltech in August!