During my walk Codex (gpt-5-high) rewrote a Python application to Go in one shot. I sat down, tested and it works. Except now I can just distribute a single binary instead of a virtualenv mess =)
Maybe the instruction was SUPER simple? Dunno.
During my walk Codex (gpt-5-high) rewrote a Python application to Go in one shot. I sat down, tested and it works. Except now I can just distribute a single binary instead of a virtualenv mess =)
Maybe the instruction was SUPER simple? Dunno.
It was a "script" (application?, the line is vague) that reads my Obsidian movie/anime/tv watchlist markdown files from my vault, grabs the title and searches for that title in Themoviedb
If there are multiple matches, it displays a dialog to pick the correct one.
Then it fills the relevant front matter, grabs a cover for Obsidian Bases and adds some extra info on the page for that item.
The Python version worked just fine, but I wanted to share the tool and I just can't be arsed to figure that out with Python. With Go it's a single binary.
Without LLM assistance I could've easily spent a few nights doing this, and most likely would've just not done it.
Now it was the effort of me giving the LLM a git worktree to safely go wild in (I specifically said that it can freely delete anything to clean up old crap) and that's what it did.
And this isn't the first Python -> Go transition I've done, I did the same for a bunch of small utility scripts when GPT3/3.5 was the new hotness. Wasn't as smooth then (many a library and API was hallucinated), but still markedly faster than doing it by hand.
Of course it's nice as a hobbyist end user to do exactly what you did for a simple script and that's to the credit of the LLM. The over-arching issue is that extremely inefficient process is only possible thanks to subsidization from Venture capital.
Also the compiler being a stickler for unused code etc. keeps the Agentic models in check, they can't YOLO stuff as hard like in, say, Python.
This way it's pretty close to zero effort.