diff --git a/README.md b/README.md
index 5da2b6b..fbc972b 100644
--- a/README.md
+++ b/README.md
@@ -5,28 +5,32 @@ electromagnetic simulations on parallel compute hardware (mainly GPUs).
 
 **Performance** highly depends on what hardware you have available:
 * A 395x345x73 cell simulation (~10 million points, 8-cell absorbing boundaries)
- runs at around 42 iterations/sec. on my Nvidia GTX 580.
-* On my laptop (Nvidia 940M) the same simulation achieves ~8 iterations/sec.
+ runs at around 91 iterations/sec. on my AMD RX480.
+* On an Nvidia GTX 580, it runs at 66 iterations/sec
+* On my laptop (Nvidia 940M) the same simulation achieves ~12 iterations/sec.
 * An L3 photonic crystal cavity ringdown simulation (1550nm source, 40nm
- discretization, 8000 steps) takes about 5 minutes on my laptop.
+ discretization, 8000 steps) takes about 3 minutes on my laptop.
 
 **Capabilities** are currently pretty minimal:
 * Absorbing boundaries (CPML)
-* Conducting boundaries (PMC)
+* Perfect electrical conductors (PECs; to use set epsilon to inf)
 * Anisotropic media (eps_xx, eps_yy, eps_zz, mu_xx, ...)
 * Direct access to fields (eg., you can trivially add a soft or hard
- current source with just sim.E[1] += sin(f0 * t), or save any portion
+ current source with just sim.E[ind] += sin(f0 * t), or save any portion
  of a field to a file)
 
+
 ## Installation
 
 **Requirements:**
 * python 3 (written and tested with 3.5)
 * numpy
 * pyopencl
-* h5py (for file output)
+* jinja2
+* dill (for file output)
 * [gridlock](https://mpxd.net/gogs/jan/gridlock)
 * [masque](https://mpxd.net/gogs/jan/masque)
+* [fdfd_tools](https://mpxd.net/gogs/jan/fdfd_tools)
 
 To get the code, just clone this repository:
 ```bash