Much of cloud computing infrastructure remains hard to use, in spite of decades of both academic research and commercialization.  Fortunately, recent technologies developed for web services and internet startups can be repurposed to enable a much lower-friction scalable cloud experience. Our goal is making the power, elasticity, and dynamism of commercial cloud services like Amazon’s EC2 accessible to busy applied physicists, electrical engineers, and data scientists, as well as a compelling new capability over Matlab, hopefully encouraging migration. We built PyWren, a transparent distributed execution engine on top of AWS Lambda, which simplifies many scale-out use cases for data science and computational imaging. We’ll talk about recent work pushing this infrastructure in unexpected directions, including large-scale linear algebra, sorting, and the viability of other cloud providers.