feat(notes): add LLM programming languages overview

Author: lzwjava

notes/2025-06-28-pythons-ai-edge-en.md

@@ -0,0 +1,24 @@
+---
+title: "Top Programming Languages for LLMs"
+lang: en
+layout: post
+audio: false
+translated: false
+generated: true
+---
+
+The popularity and importance of programming languages in the context of Large Language Models (LLMs) can be influenced by several factors, including performance, robustness, and the ecosystem surrounding the language. While LLMs can assist with code generation and reduce the hassle of syntax, certain languages may stand out due to their efficiency and effectiveness in execution. Here are some languages that might gain more traction:
+
+Python: Already popular in the AI and machine learning community, Python's simplicity and readability make it a strong candidate for continued importance. Its extensive libraries and frameworks for AI (like TensorFlow and PyTorch) make it a go-to language.
+
+Julia: Known for its high performance and ease of use, Julia is designed for numerical and scientific computing, which makes it suitable for data-intensive AI tasks.
+
+Rust: With a focus on safety and performance, Rust is gaining attention for systems programming and could be important for developing efficient AI models and tools.
+
+Go: Developed by Google, Go is known for its simplicity and efficiency. It is particularly well-suited for building scalable and concurrent applications, which can be beneficial in AI deployments.
+
+Swift: Apple's Swift is optimized for performance and is increasingly being used for machine learning tasks, especially with the advent of Swift for TensorFlow.
+
+JavaScript/TypeScript: Given the ubiquity of web applications and the increasing use of AI in web services, JavaScript and TypeScript are likely to remain important for integrating AI functionalities into web platforms.
+
+These languages are likely to be more popular and important due to their performance characteristics and the support they receive from their respective communities and ecosystems. However, the landscape can evolve, and new languages or improvements to existing ones could also emerge as significant players.

original/2025-01-29-ai-thoughts-en.md

@@ -63,6 +63,7 @@ translated: false
 
 - AI can be evaluated by how well it can update a pom.xml or requirements.txt file to the latest version, update libraries, and perform checks. This process can involve a significant amount of work and can sometimes be complex.
 
+- In the AI era, programming languages that have better performance and robustness are more important and will be more popular, while syntax is less important. This is because LLM will help generate code, making it less of a hassle as long as the program executes well.
 
 - [New Platforms Powered by AI Workflows](./ai-workflow-en)