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.

Using cursor and Claude Sonnet models helps the front end development of html, css and javascript generation quickly and trustworthy. It can actually produce a working demo with just three files: styles.css, index.html and scripts.js. It's great for small team without front-end engineers to produce great looking demos.

Another more complicated task I tested on Cursor Code Editor is to write a python code to implement the Transformer function as in paper "Attention is all you need". The code I am writing include function name "def transformer(x):" and some of the beginning lines like "q=torch.linear(". And with the suggested code, I wrap up the transformer function in less than one minutes. So it can really help me increase the productivity of writing code. The only drawback is when I want to modify the code and use backspace to delete some lines, it seems like Cursor doesn't know what to do and give no hints for a while. Unless I start writing something else. And the newly suggested code will usually be quite different from the previously suggested ones. ``` ## Machine Learning ### Input import torch def transformer(x): ndim = 16 q=torch.linear(ndim, ndim)(x) k=torch.linear(ndim, ndim)(x) v=torch.linear(ndim, ndim)(x) attn = torch.matmul(q, k.transpose(-2, -1)) attn = attn / math.sqrt(ndim) attn = torch.softmax(attn, dim=-1) ### Cursor Suggested Code: out = torch.matmul(attn, v) out = torch.linear(ndim, ndim)(out) return out ```

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) ```

This picture of "Donald trump as ancient roman empire warrior" generated by Stable Diffusion is quite interesting. The six packs is consistent with the text prompts. The facial expressions are good. But the left hand is a little bit weird. The left hand is holding up and showing an awkward gesture.

I used Stable Diffusion and Control Net to generate the picture of "Donald trump as judo master, blue belt, tough facial expressions". The results are pretty amazing. The only limitation of AI generated images is your imagination.

The blue color of the BMW car looks a little bit fake. Other details of the car generate by SD3 look good.

I think the underground tunnels and laboratories generated by Stable Diffusion looks great, expect that the color of the floor is also showing reflecting lights, which is a little bit weird.

The stable diffusion generated images look amazing.

I saw a post in Reddit about the midsummer Dragon Cartoon. The pictures have bright colors with baby dragons on a big donuts floating on the lake. The blur in the background is a little bit strange, maybe change the prompts and add something about "perspective" parameters? Other details all look good!