Comment by UncleOxidant

I do, but I also use LLMs in a manner that seems drastically different, from most folks here.

I use the standard $20/ChatGPT Pro sub, and run Thinking 5.5 as a chat interface.

I use it like a "trusted personal advisor," as opposed to a "black box employee."

I'm intimately involved in almost every step of the development process. Most of what I ask from the LLM, is function-length snippets.

It's made a huge difference in the velocity and scope of my work.

I have learned that I need to be very careful, though. The LLM sometimes really borks things, and I have to rip out the garbage, and rewrite the code, myself. I can't even imagine the quality of "vibe-coded" software.

I like building stuff. I don't care about the code. I convinced myself over 20 years that I liked coding to get myself through the drudgery of corporate work but the reality is the building was the important thing to me. I'm able to build things quickly with AI.

I guess some people only enjoy the destination and don't care how they got there. These people seem to enjoy AI more than the people who want to enjoy the journey along the way.

I'm curious of the places you've found joy while writing software traditionally. For me, it has been in reasoning about the system, debugging issues, and discovering what works. The iterative process of eventually coming to a more complete understanding, as you stand on and build off of your prior understanding.

All of those elements are present for me while using AI to augment my output. I have started using voice to interact with my coding harness though and I think that has maybe influenced my opinion. I also don't let things go fully autonomously and look at the diffs along the way.

LLMs have gotten so good at coding I often find it exhilarating. I'm mostly doing experiments in accelerating machine learning in hardware. I can do way more experiments now. As an example: Just this afternoon working with claude to experiment with using a GA to learn a non-uniform 1D CA rules to calculate the popcount function where the GA is running in an FPGA to speed it up. It's non-uniform in that each cell gets it's own 8-bit rule.

Depends on how you use them. I'm a detail-obsessed perfectionist. I believe these qualities are what have enabled me to produce better software than most people. I use LLMs the way I can without violating these principles.

I asked the LLM to explain, rephrase, or rewrite things until I was happy. Some examples :

I asked for examples of how the algorithm worked. I asked for examples of how to call the code. I asked for a happy-path unit test and a simple error-handling unit test. I asked it to rewrite something as a pure function. I pointed out an obvious race condition and told it to guard against that issue. I asked it to rewrite a function in the style of this other function. I told it to separate one function into two separate functions that handle the first step and the second step separately.

Etc etc.

If you don't understand it, ask for more or better comments, or better variable names, or cut down the scope into a smaller section, or more examples.

Edit: also I almost entirely leave the LLM in read only mode... I tell it to make the smallest change possible, and tell it I will only copy paste it in its proposed change when I understand the change and where it needs to be made. That way it's my hands on the keyboard, interacting with the code by making recommended changes... 80% of the code is touched by me (via copy-paste) most-of-the-way before I will 'git commit'.

Sure, there was one recursive folder descent function that found the most recent file modification time that I didn't fully understand, but it's self-contained in a function, I don't care to learn every corner of file modification times, and it appears to work, so I left it as is for my static site generator.