1-bit compressed sensing combines the dimension reduction of compressed sensing with extreme quantization -- only the sign of each linear measurement is retained. We discuss recent convex-programming approaches with strong theoretical guarantees. We also show that the mapping which takes signals in $R^n$ and returns binary measurements acts as a near-isometry, thus giving a discrete method of dimension reduction.