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The following commit(s) were added to refs/heads/main by this push:
     new 8482608eb70 Update TIRx post acknowledgements and contributing wording 
(#65)
8482608eb70 is described below

commit 8482608eb705a75cdd941ef2d920314622d7a8e7
Author: Bohan Hou <[email protected]>
AuthorDate: Mon Jun 22 07:39:43 2026 -0700

    Update TIRx post acknowledgements and contributing wording (#65)
    
    - Acknowledgements: add the TileLang team alongside FlashInfer and
      FlashInfer-Bench for helpful technical discussions.
    - Contributing: invite contributions to the compiler and the community
      projects (generic) instead of naming the kernel library specifically.
---
 _posts/2026-06-22-tirx.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/_posts/2026-06-22-tirx.md b/_posts/2026-06-22-tirx.md
index c8764e88094..aca383a1c4b 100644
--- a/_posts/2026-06-22-tirx.md
+++ b/_posts/2026-06-22-tirx.md
@@ -294,8 +294,8 @@ In this view, TIRx is not just a target language for 
generated kernels. It is so
 
 ## **Contributing**
 
-TIRx is an open compiler foundation. The core abstraction boundary is 
intentionally small, but the ecosystem around it can grow in several 
directions. Feel free to try out TIRx and contribute to the compiler and kernel 
library!
+TIRx is an open compiler foundation. The core abstraction boundary is 
intentionally small, but the ecosystem around it can grow in several 
directions. Feel free to try out TIRx and contribute to the compiler and the 
community projects.
 
 ## **Acknowledgement**
 
-TIRx would not exist without Apache TVM, on whose compiler infrastructure it 
is built. Beyond that foundation, its design has been shaped by a long line of 
systems work, including NumPy, CuTe, Triton, ThunderKittens, and TileLang. We 
thank the FlashInfer and FlashInfer-Bench teams and the Apache TVM community 
for helpful technical discussions.
+TIRx would not exist without Apache TVM, on whose compiler infrastructure it 
is built. Beyond that foundation, its design has been shaped by a long line of 
systems work, including NumPy, CuTe, Triton, ThunderKittens, and TileLang. We 
thank the FlashInfer, FlashInfer-Bench, and TileLang teams and the Apache TVM 
community for helpful technical discussions.

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