Using cursor to develop production worthy codes in company is fantastic. Converting user prompt to ready to ship code is so easy and I just didn't code VS code for a long time. Ready to switch to new coding IDE. For example, implementing a trie tree structure take time and find production ready code from Google or Bing it's not easy. But cursor actually produces a clean version of trie tree implementation. With only a few naming changing, you got your production worthy code.

I just downloaded Cursor on Mac and tried some coding task. The first task is to write a fabonacci function in python. And once I typed the name "fabonacci(n)" and end the function name with the colon ":", there is a code hint tab poping-up suggesting the remaining code. The suggested codes are correct and are very succinct. Very Helpful AI coding tools. ``` def fabonacci(n): if n == 0: return 0 elif n == 1: return 1 else: return fabonacci(n-1) + fabonacci(n-2) ```

I asked ChatGPT (GPT4o) question about python coding "sandbox python programs with Docker environment". The responses are helpful and the generated code samples are clear. But the website front end is having a URL rendering issue of the "Docker installation guide". The font is highlighted so it means it's a link. But there is no href or URL associated with the text and I can't find the installation guide it refers to. Is it a known problem? Reproducing links: https://chatgpt.com/share/67298cb0-47c0-8005-96ea-0562df9f7158

I asked ChatGPT (GPT 4o) a coding related questions "export prompt and completion data in json format and convert to parquet format", and the python code generated by ChatGPT are quite good. It gives me examples of detailed json data format, python code to convert raw json data to Parquet format and even recommends all the python libs requirements. Very helpful response from ChatGPT(GPT4o) !!! Responses from ChatGPT: Step 1: Export Data in JSON Format Step 2: Convert JSON to Parquet using Python Explanation of the Code Prerequisites. And the ChatGPT dialogue sharing link is here: https://chatgpt.com/share/67205caa-a068-8005-be6b-76eeed480fbe

ChatGPT did a good job on this python code test, which is to "implement self attention layer in transformer using pytorch package". Overall, the python code is correct and concise. After comparing ChatGPT vs Gemini vs Claude for coding, I find that the overall response from ChatGPT lacks of detailed explanation of meanings of each section of the code, which is less helpful than the response from Claude Sonnet.