Update blog

Author: cmdr2

content/blog/2025-11-13-1763027191.md

@@ -38,6 +38,6 @@ For fusion, PolyBlocks uses a Polyhedral slicing-based approach in the affine pa
 - Fetch the data for the next iteration (of a loop) while processing the current iteration
 
 Some other random notes:
-- User-facing API: TensorRT-style compiled engine files, or Torch/Mojo/PolyBlocks-style JIT compilers inside Python.
+- User-facing API: TensorRT-style AOT compiled engine files, or Torch/Mojo/PolyBlocks-style JIT compilers inside Python, or in between (e.g. TensorRT-RTX).
 - For the host-side code (i.e. the code that talks to the driver), it might be a good idea to generate C++ code that people can compile themselves (for power users). But this would add more hoops for the user to jump through, so this might be an option?
 - Quantization hardware-awareness in the compiler is important, so that it can factor that in during tiling and memory layout.