I’m NOT excited about coding by AI [venting]
I’m NOT excited about coding by AI [venting]
I’m a dev. I’ve been for a while. My boss does a lot technology watch. He brings in a lot of cool ideas and information. He’s down to earth. Cool guy. I like him, but he’s now convinced that AI LLMs are about to swallow the world and the pressure to inject this stuff everywhere in our org is driving me nuts.
I enjoy every part of making software, from discussing with the clients and the future users to coding to deployment. I am NOT excited at the prospect of transitioning from designing an architecture and coding it to ChatGPT prompting. This sort of black box magic irks me to no end. Nobody understands it! I don’t want to read yet another article about how an AI enthusiast is baffled at how good an LLM is at coding. Why are they baffled? They have "AI" twelves times in their bio! If they don’t understand it who does?!
I’ve based twenty years of career on being attentive, inquisitive, creative and thorough. By now, in-depth understanding of my tools and more importantly of my work is basically an urge.
Maybe I’m just feeling threatened, or turning into "old man yells at cloud". If you ask me I’m mostly worried about my field becoming uninteresting. Anyways, that was the rant. TGIF, tomorrow I touch grass.
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AI LLMs are the best rubber duck. Like a rubber duck, it's probably not going to "solve" anything for you directly, but LLMs can be a great tool to unlock your potential.
Non-AI rubber ducks don't steal your code and sell it to your competitors, though.
If a human can access your public repo and read comments posted on public forums, are they stealing your code? LLMs are just aggregators of a great many resources and they aren't doing anything more than a biological human can already do. The LLM can do so more efficiently than a biological human, while perhaps being more prone to error as it doesn't completely understand why something is written the way it is. As such any current AI model is prone to signpost errors, but in my experience it has been very good at organizing the broader solution.
I can give you two examples. I started trying to find out how a .Net API call was made. I was trying to implement a retry logic for a call, and I got the answer I asked. I then realized that the AI could do more for me. I asked it to write the routine for me and it suggested using a library which is well suited for that purpose. I asked that it rewrite it without using an external library and it spit it out. I could have written this completely from scratch, in fact I had already come up with something similar but I was missing the API call I was initially looking for. That said, the result actually had some parts I would have had to go back and add, so it saved me a lot of time doing something I already knew how to do.
In a second case, I asked if to solve a problem which at its heart was a binary search. To validate that the answer was correct it would need to go one extra step, but to answer the question it wasn't necessary to actually perform that last validation step. I was looking for the answer 10, but I got the AI to give me answers in the range of 9-11. It understand the basic concepts, but it still needs a biological human to validate what it generates.
I'm talking about asking the AI “where's the bug in this code” and pasting a snippet of code from my non-public repository.