![[D] The fundamental problem with LLM hallucinations and why current mitigation strategies are failing](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-9476-300x171.png)
Python remains the dominant language for machine learning due to its extensive libraries, ease of use, and versatility, making it the go-to choice for most developers. For tasks that require high performance, languages like C++ and Rust are preferred, with Rust offering additional safety features. Julia is recognized for its performance but has not seen widespread adoption, while Kotlin, Java, and C# are used for platform-specific applications, such as Android. Other languages like Go, Swift, and Dart are chosen for their ability to compile to native code, enhancing performance, and R and SQL are utilized for statistical analysis and data management, respectively. CUDA is commonly used for GPU programming to accelerate machine learning tasks, and JavaScript is often employed for full-stack projects involving web interfaces. Understanding the strengths and applications of these languages helps developers choose the right tools for their specific machine learning needs.
