Add authors and typo fix

Author: czgdp1807

content/blog/lpython_mvp.md

@@ -2,7 +2,7 @@
 title: "LPython: Making Python faster with LLVM"
 date: 2023-07-28
 tags: ["Python", "Announcement"]
-author: "[Ondřej Čertík](https://ondrejcertik.com/), [Brian Beckman](https://www.linkedin.com/in/brianbeckman), [Gagandeep Singh](https://github.com/czgdp1807), [Thirumalai Shaktivel](https://www.linkedin.com/in/thirumalai-shaktivel/), [Rohit Goswami](https://rgoswami.me), [Smit Lunagariya](https://www.linkedin.com/in/smit-lunagariya-356b93179/), [Ubaid Shaikh](https://Shaikh-Ubaid.github.io/), [Pranav Goswami](https://www.linkedin.com/in/pranavgoswami1/)"
+author: "[Ondřej Čertík](https://ondrejcertik.com/), [Brian Beckman](https://www.linkedin.com/in/brianbeckman), [Gagandeep Singh](https://github.com/czgdp1807), [Thirumalai Shaktivel](https://www.linkedin.com/in/thirumalai-shaktivel/), [Rohit Goswami](https://rgoswami.me), [Smit Lunagariya](https://www.linkedin.com/in/smit-lunagariya-356b93179/), [Ubaid Shaikh](https://Shaikh-Ubaid.github.io/), [Pranav Goswami](https://www.linkedin.com/in/pranavgoswami1/), [Virendra Kabra] (https://www.linkedin.com/in/virendrakabra/)"
 type: post
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@@ -1088,6 +1088,6 @@ Done.
 
 ## Conclusion
 
-The benchmarks support the claim that LPython is competitive with its competitors in all features it offers. In JIT, the execution times of LPython-compiled functions are at least as short as equivalent Numba functions.The speed of JIT compilation, itself, is slow in some cases because it depends on a C compiler to generate optimal binary code. For algorithms with rich data structures like `dict` (hash maps) and `list`, LPython shows much faster speed than Numba. In AoT compilation for tasks like the Dijkstra algorithm, LPython beats equivalent C++ code very comfortably. For an array-based implementation of the Floyd-Warshall algorithm, LPython generates code almost as fast as doess C++.
+The benchmarks support the claim that LPython is competitive with its competitors in all features it offers. In JIT, the execution times of LPython-compiled functions are at least as short as equivalent Numba functions.The speed of JIT compilation, itself, is slow in some cases because it depends on a C compiler to generate optimal binary code. For algorithms with rich data structures like `dict` (hash maps) and `list`, LPython shows much faster speed than Numba. In AoT compilation for tasks like the Dijkstra algorithm, LPython beats equivalent C++ code very comfortably. For an array-based implementation of the Floyd-Warshall algorithm, LPython generates code almost as fast as C++ does.
 
 The main takeaway is that LPython/LFortran generate fast code by default. Our benchmarks show that it's straightforward to write high-speed LPython code. We hope to raise expectations that LPython output will be in general at least as fast as the equivalent C++ code. Users love Python because of its many productivity advantages: great tooling, easy syntax, and rich data structures like lists, dicts, sets, and arrays. Because any LPython program is also an ordinary Python program, all the tools -- debuggers and profilers, for instance -- just work. Then, LPython delivers run-time speeds, even with rich data structures at least as short as alternatives in most cases. In the future, LPython will allow user-defined implementations of data structures for those rare cases where the versions shipped with LPython are not good enough.