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 # Tile Language
 [![PyPI version](https://badge.fury.io/py/tilelang.svg)](https://badge.fury.io/py/tilelang)
-[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/tile-ai/tilelang) [![Discord](https://img.shields.io/badge/Discord-%235865F2.svg?logo=discord&logoColor=white)](https://discord.gg/TUrHyJnKPG)
-
+[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/tile-ai/tilelang)
+[![Discord](https://img.shields.io/badge/Discord-%235865F2.svg?logo=discord&logoColor=white)](https://discord.gg/TUrHyJnKPG)
+[![Puzzles](https://img.shields.io/badge/🧩_Learn-TileLang_Puzzles-blueviolet)](LINK_HERE)
 </div>
 
 Tile Language (**tile-lang**) is a concise domain-specific language designed to streamline the development of high-performance GPU/CPU kernels (e.g., GEMM, Dequant GEMM, FlashAttention, LinearAttention). By employing a Pythonic syntax with an underlying compiler infrastructure on top of [TVM](https://tvm.apache.org/), tile-lang allows developers to focus on productivity without sacrificing the low-level optimizations necessary for state-of-the-art performance.
@@ -13,6 +14,7 @@ Tile Language (**tile-lang**) is a concise domain-specific language designed to
 <img src=./images/MatmulExample.png />
 
 ## Latest News
+- 02/02/2026 🧩: Check out [TileLang Puzzles](https://github.com/tile-ai/tilelang-puzzles), a fun and interactive way to learn TileLang programming with 10 progressively harder puzzles!
 - 12/18/2025 🚀: Added [CuTeDSL backend](https://github.com/tile-ai/tilelang/pull/1421) support, enabling compilation to NVIDIA CUTLASS CuTe DSL! Join us in building and optimizing this exciting new backend: [Issue #1454](https://github.com/tile-ai/tilelang/issues/1454).
 - 12/17/2025 🔬: Integrated [Z3 theorem prover](https://github.com/tile-ai/tilelang/pull/1367) into TVM Arith Analyzer, bringing SMT-based symbolic reasoning for enhanced optimizations and automatic correctness verification!
 - 10/31/2025 🔧: Migrated to [apache-tvm-ffi](https://github.com/tile-ai/tilelang/pull/1108), significantly reducing CPU overhead!