removed footnotes

Author: QuillPusher

_pages/research.md

@@ -37,13 +37,13 @@ process, while being able to use lightweight APIs for simpler workflows and
 diagnostics, helping create sophisticated applications more efficiently.
 
 Practical applications of CaaS include deeper and interactive program analysis
-and conversion from one programming language to another (e.g., C++ <->
+and conversion from one programming language to another (e.g., C++ and
 Python).
 
 
 ### Incremental C++
 
-Despite its high performance capabilities, C++ not the first programming
+Despite its high performance capabilities, C++ is not the first programming
 language that comes to mind for rapidly developing robust applications, mainly
 due to the long edit-compile-run cycles.
 
@@ -56,16 +56,17 @@ scientific applications.
 Following are some practical applications of a "[C++ Interpreter]," so to speak:
 
 - In Data Science: Interactive probing of data and interfaces, making complex
-  libraries and data more accessible to users. [^1]
+  libraries and data more accessible to users. 
 
-- In CUDA: The Cling CUDA extension brings the workflows of interactive C++ to
-  GPUs without losing performance and compatibility to existing software. [^1]
+- In CUDA: The Cling CUDA extension brings the workflows of [Interactive C++] to
+  GPUs without losing performance and compatibility to existing software. 
 
 - In Exploratory Programming: rapid reproduction of results, which is crucial
-  during the exploratory phase of a project. [^2]
+  during the exploratory phase of a project.
 
-- In Jupyter Notebooks: Interactive C++ can be integrated with Jupyter
-  Notebooks, providing a swift prototyping and learning experience for C++ users. [^2]
+- In Jupyter Notebooks: Interactive C++ can be [integrated with Jupyter
+  Notebooks], providing a swift prototyping and learning experience for C++
+  users.
 
 ### Language Interoperability
 
@@ -76,7 +77,7 @@ helping them identify each other's entities (like variables and classes) for
 seamless integration.
 
 This interoperability can be achieved by libraries like [CppInterOp], which
-expose APIs from compilers like Clang/LLVM in a backward-compatible manner. By
+expose APIs from compilers like Clang in a backward-compatible manner. By
 enabling interactive C++ usage through the Compiler-As-A-Service, CppInterOp
 simplifies complex tasks such as "language interoperability on the fly".
 
@@ -88,13 +89,11 @@ instantiate templates, and execute them efficiently. This dynamic approach not
 only improves performance but also simplifies code development and debugging
 processes, offering a more efficient alternative to static binding methods.
 
----
-Footnotes:
 
-[^1]: [Interactive C++ for Data Science](https://blog.llvm.org/posts/2020-12-21-interactive-cpp-for-data-science/)
 
-[^2]: [Interactive Workflows for C++ with Jupyter](https://blog.jupyter.org/interactive-workflows-for-c-with-jupyter-fe9b54227d92)
+[Interactive C++]: https://blog.llvm.org/posts/2020-12-21-interactive-cpp-for-data-science/
 
+[integrated with Jupyter Notebooks]: https://blog.jupyter.org/interactive-workflows-for-c-with-jupyter-fe9b54227d92
 
 [CppInterOp]: https://github.com/compiler-research/CppInterOp/blob/main/README.md