How I ended up maintaining a generator I didn't even build
Three years ago I was helping a friend set up a D&D campaign and the entire table had zero creativity when it came to naming NPCs. Someone suggested I just build something to generate names on the fly. That turned into a project nobody asked for but everyone quietly uses. The tool is straightforward at its core. It takes a combination of linguistic fragments—prefixes, suffixes, middle syllables—and scrambles them with probability weights so the output feels random but still pronounceable. You feed it a setting (fantasy, sci-fi, modern, absurd) and it spits out results. The real work happened in tuning the weights so you didn't get garbage like "Zxq'vahl 3" mixed in with usable names.
Funny And Inappropriate Names as a practical category
The specific sub-feature most people find is the Funny And Inappropriate Names generator, which runs on the same engine but swaps the weighting tables entirely. Instead of leaning toward plausible phonotactics, it leans toward combinations that trigger unexpected associations—double meanings, awkward phonetic similarities, and rhythm mismatches that sound like they belong in a comedy sketch rather than a campaign setting. I've seen people use it for everything from renaming their router, to generating satirical business names for skits, to creating fake customer reviews where the reviewer name itself does half the comedic work.
The actual setup process
If you want to run this locally, the repo is on GitHub. You clone it, install the dependencies with pip, and load a configuration file that defines your word banks. The default config covers about twelve languages and six tonal registers. Most people never bother modifying it because the defaults are decent, but the config is where the actual control lives. The command line interface looks like this: python gen.py --mode inappropriate --count 20 --style absurd
Get the Full Details
That returns twenty names tuned toward the absurd end of the inappropriate spectrum. Swap "absurd" for "satirical" and you get something sharper, less silly. The style flag accepts six values: absurd, satirical, crude, playful, dark, and corporate. Corporate is a joke option but honestly it works well for generating fake company names in office humor pieces.
Where things actually break
The generator has a real bottleneck around cultural sensitivity. I ran into this head-on when someone tried using it to generate names for a mock news segment about a specific real-world ethnic community. The output was technically funny but completely missed the mark because the model had no context about which combinations were actually offensive versus merely unfamiliar. It doesn't have a content filter by default, and adding one is messier than it sounds because what counts as inappropriate changes depending on audience and region. My workaround was writing a lightweight post-processor script that checks outputs against a curated list of known offensive terms and red flags, then flags anything that matches for manual review. It's not automatic correction, just a tripwire. It catches about eighty percent of genuinely problematic output before it reaches anyone else. The remaining twenty percent requires a human eye anyway.
What the documentation leaves out
The README explains how to generate names. It doesn't cover the part where you realize your word bank is biased because the source material came mostly from English-language internet forums from 2019 to 2022. That means the generator skews heavily toward American internet humor sensibilities and will produce weaker results if you're targeting UK, Australian, or non-English audiences. I spent about three weeks recompiling the slang and phonetic association lists to rebalance it, and even now the non-English output feels slightly off in rhythm. Another thing nobody mentions: the memory usage scales linearly with word bank size. If you stack too many regional dialects and slang sources into a single config, the generator starts taking twelve seconds per name instead of under a second. I learned that the hard way when I tried merging twelve language banks into one request for a multilingual comedy project. Ended up splitting it into separate generators by language cluster and running them in parallel.
Whether you should actually use this
It depends on what you need. If you want quick throwaway humor for casual use, it works fine out of the box. If you're building something professional—commercial content, broadcast material, anything that goes to a wide audience—the lack of built-in moderation is a liability. You'd be better off using it as a raw ingredient and running outputs through a proper content review pipeline first. There are also more polished commercial alternatives if you don't want to maintain your own instance. Nameberry's generator handles the fantasy side better, and various branding tools do corporate-style name generation with actual trademark checking built in. This project fills a narrow lane: unfiltered, fast, and slightly messy. That's the point. It's not meant to be clean. It's meant to be useful when you need thirty bad names in five minutes and you're willing to filter them yourself. The repo link stays at the original GitHub URL. Fork it, break it, adjust the configs. Just don't blame me when someone uses it to generate something that gets them in trouble at work.
