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 # Dervish MCP
 
-<p align="left">
+<p align="center">
   <img src="dervish-logo.png" alt="Dervish" width="180">
 </p>
 <p align="center">
   <img src="https://img.shields.io/badge/license-MIT-blue" alt="License">
   <img src="https://img.shields.io/badge/python-3.10%2B-blue" alt="Python 3.10+">
   <img src="https://ci.corentic.eu/api/badges/7/status.svg" alt="CI Pipeline Status">
-  <a href="https://www.buymeacoffee.com/IjonTichy85"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy me a coffee" width="120"></a>
   <br>
   <a href="SHOWCASE.md">Showcase</a> ·
   <a href="#quick-start">Usage</a> ·
   <a href="#papers">Papers</a>
+  <br>
+  <a href="https://www.buymeacoffee.com/IjonTichy85"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy me a coffee" width="140"></a>
 </p>
 
 **Dervish** infers **regular expression grammars** from example sequences using the BEX family of algorithms. Given a set of example sequences (strings over some alphabet), it learns a compact regular expression that captures the general pattern.