First of all, thanks for the tremendous work everyone has done! In the Sage project (http://www.sagemath.org), we have a slightly patched version of scipy 0.6 currently. We recently upgraded to numpy 1.2 and would like to match that with an upgrade of scipy. We are using scipy more and more; for example, in our next version out later this week, we switched our floating point and complex matrices to use a numpy/scipy backend for most calculations. Is there a recommended SVN version that we should update to while waiting for 0.7 to be released? We're looking for an SVN revision that is relatively stable. Incidentally, count me into the crowd that would find scipy much more valuable if there were more frequent releases; Sage users in general would be testing the code and giving feedback as well. Also, we noticed the following behavior in our current version of scipy, but only on an OSX 10.5 box. If it's easy, can someone see if the following commands give the spurious result we see below for the inverse matrix with scipy.linalg.inv on an OSX 10.5 box? I'd test it, but the code works on our old scipy on my 32 bit Ubuntu box. sage: import numpy sage: a=numpy.array([[1,2,3],[4,5,6],[7,8,9]],dtype="float64") sage: import scipy sage: import scipy.linalg sage: import numpy.linalg sage: scipy.linalg.det(a) 0.0 sage: scipy.linalg.inv(a) array([[ -4.50359963e+15, 9.00719925e+15, -4.50359963e+15], [ 9.00719925e+15, -1.80143985e+16, 9.00719925e+15], [ -4.50359963e+15, 9.00719925e+15, -4.50359963e+15]]) sage: numpy.linalg.det(a) 6.6613381477509392e-16 sage: numpy.linalg.inv(a) array([[ -4.50359963e+15, 9.00719925e+15, -4.50359963e+15], [ 9.00719925e+15, -1.80143985e+16, 9.00719925e+15], [ -4.50359963e+15, 9.00719925e+15, -4.50359963e+15]]) Thanks, Jason