41–45 of 45 posts
Now I use AI to go asap to 70% of my code. The last 30% is a manual, low AI, approach where I fix the hallucinations, file structure and do the stuff AI is failing me at.
I use Claude Chat, ChatGPT, Claude Code and Windsurf (switching between those all the time)
My take on the subject has shifted the last months. I had issues with AI's output, the mistakes, hallucinations. Now I use AI to go asap to 70% of my code. The last 30% is a manual, low AI, approach where I fix the hallucinations, file structure and do the stuff AI is failing me at. I use Claude Chat, ChatGPT, Claude Code and Windsurf (switching between those all the time)
I've just written about that exact scenario. A shoddy piece of code that's just about okay: https://richardcocks.github.io/2025-03-24-PasswordGen
If you're a junior, you might not realise there's anything wrong with the generated code at all.
My take on the subject has shifted the last months. I had issues with AI's output, the mistakes, hallucinations. Now I use AI to go asap to 70% of my code. The last 30% is a manual, low AI, approach where I fix the hallucinations, file structure and do the stuff AI is failing me at. I use Claude Chat, ChatGPT, Claude Code and Windsurf (switching between those all the time)
Whenever new layers of abstraction enter the industry, it has allowed coders to distance themselves from various pieces of the puzzle. Back in ye olde days, every coder also knew how to set up the infrastructure to run it, often to the point of also maintaining the hardware. Then along came the cloud. Nowadays, some people still know the whole infrastructure, but just as many only know how to run their local dev and…
Previous layers of abstraction were largely reliable and deterministic. Compilers don't just randomly generate assembly. Yes, all abstractions leak, but most of them just drip, while whatever "abstraction" AI provides leaks like a sieve. It would be like if CDK just created or deleted random resources 50% of the time.
I've found LLMs making inexplicable mistakes when writing statistical code, but the code will still run and return a confident result. If you can't look at the code, you won't see, e.g., your hourly time series being aggregated on a daily basis.
Earlier quoted context omitted.
Perhaps there's something wrong with traditional docs, how we write them, when junior devs are so insistent on using AI?
Most docs are for experienced people to get valuable, exhaustive, and correct information when they need to. Learning about the same concepts is a different paradigm and you'd be better off with an actual book/tutorial. My most use information repositories are MDN and the likes, Devdocs|Dash, and sometimes library code when the documentation is lacking. But when I'm unfamiliar with a language/platform, I seek a book…
read.csv("large.csv", nrows = 100)
read_csv("large.csv", n_max = 100)
pd.read_csv("large.csv", nrows=100)
pl.read_csv("large.csv", n_rows=100)
CSV.read("large.csv", DataFrame; limit=100)