May 26, 2026
Code drama goes delightfully dull
Use Boring Languages with LLMs
Why coders are suddenly obsessed with ‘boring’ tools for AI help
TLDR: The article argues that AI coding tools work best with simple, consistent programming ecosystems because fewer choices mean fewer weird mistakes. Commenters instantly turned that into a brawl over whether this is true, whether Go is now officially “boring,” and whether AI is actually as confused as advertised.
A software consultant tossed a spicy idea into the internet: if you want better results from AI coding tools, stop chasing the shiny new thing and stick with boring, predictable languages. His argument is simple enough for non-coders too: when a tool has one obvious way to do things, artificial intelligence is less likely to wander off into chaos. In his view, messy ecosystems like JavaScript and Python can leave AI guessing, while stricter worlds like Go or Ruby on Rails give it fewer chances to go rogue.
But the comments? Oh, they did not just nod politely. One of the biggest clap-backs came from an Elm user, who basically said, “Nice theory, except the AI still shows up acting like it’s writing Haskell,” turning the thread into a mini roast of robot confusion. Another commenter flat-out challenged the premise, saying models seem pretty good at sorting this stuff out already, thank you very much. And then there was the identity crisis bombshell: has Go itself become a boring language now? That one has real “look how they massacred my boy” energy.
The funniest twist is that some readers didn’t hate the idea, they just hated the word boring. One person tried a rebrand, arguing this is really about a “pit of success” — tools that gently push you toward sane choices. Translation: the community may not agree on the slogan, but they are absolutely united in turning it into a drama-filled philosophy fight.
Key Points
- •The article argues that LLM coding performance is more reliable in languages and ecosystems with stronger conventions and less fragmentation.
- •It describes AI-assisted coding as probabilistic, with models sometimes producing unexpected package choices or outdated coding patterns during inference.
- •JavaScript and Python are presented as examples of fragmented ecosystems with many frameworks, package managers, and implementation paths.
- •The article links lower-variance training corpora to more consistent model behavior, describing this in terms of embeddings, cosine similarity, attention, and token prediction.
- •Rails and Go are cited as examples of more constrained ecosystems that the article says tend to produce more consistent output from coding agents.