Julia has gained attention as a powerful programming language for parallel and distributed computing, especially in scientific research contexts. Users appreciate its speed and efficiency, particularly its ability to handle multi-dimensional arrays seamlessly. However, the language faces significant challenges, particularly in the form of a limited package ecosystem, long compilation times, and compatibility issues with Python, which ultimately affect user adoption. Additionally, concerns have been raised about JuliaHub's business model, which resembles traditional software commercial practices, potentially hindering wider usage. There's recognition within the community that for Julia to displace Python in data science and scientific computing, it must address these ergonomic and infrastructural issues, while also capitalizing on its unique strengths.