Gen Z slang intelligence. Track, translate, and understand emerging language in real time.
I spent years building infrastructure for language models. Somewhere along the way I realized there's a language barrier that no model covers: the one between generations. Your boss doesn't know what "cooked" means. Your dad thinks "cap" is a hat. Your investor just used "fire" wrong in a pitch deck. The entire professional world — and every LLM trained before last month — is quietly falling behind on how actual humans under 25 communicate.
Slang is an AI-native toolkit that tracks Gen Z and Gen Alpha slang as it emerges, understands the meaning and cultural context behind it, and gives you tools to actually work with it — translate it, score your own fluency, or plug it into any LLM so your AI doesn't sound like it was born in 2019.
Lexicon — a living dictionary of ~4,000 slang terms, auto-updated from Reddit, TikTok, and Twitter/X. Each entry has a definition, usage examples, origin story, virality score (how fast it's spreading), and a shelf-life estimate (how long before it's dead).
Translate — paste a sentence in Gen Z and get a clean, professional English translation. Or go the other way: paste corporate speak and get the Gen Z equivalent. Bidirectional, context-aware, handles nested slang.
Vibe Check — paste any text and get a cultural fluency score from 0 to 100. How natural does your writing sound to a 20-year-old? Where did you slip? What gave you away as a boomer? Detailed breakdown with suggestions.
Trend Radar — real-time tracking of which terms are rising, which are peaking, and which are dying. See the lifecycle of a slang word from first appearance to mainstream adoption to cringe territory. Tracks velocity, not just volume.
LLM Plugin — a system prompt module and embedding set you can inject into any OpenAI/Anthropic/local model to make it fluent in current slang. Stop your chatbot from saying "that's lit fam" in 2026.
git clone https://github.com/OWNER/slang.git
cd slang
pip install -e .
# add your OpenAI key (required for translate and vibe-check)
cp .env.example .env
# edit .env
# explore the lexicon
slang lookup "cooked"
slang lookup "unc"
slang lookup "twin"
# translate
slang translate "nah that's cooked fr, the unc was not locked in at all"
# vibe check your writing
slang vibe "We are pleased to inform you that your application has been received."
# see what's trending right now
slang trending
# start the API server
slang serveWhen running as a server (slang serve), all features are available via REST:
# look up a term
curl localhost:8899/api/lookup/cooked
# translate text
curl -X POST localhost:8899/api/translate \
-H "Content-Type: application/json" \
-d '{"text": "bro got ratio\'d into the shadow realm", "direction": "to_formal"}'
# vibe check
curl -X POST localhost:8899/api/vibe \
-H "Content-Type: application/json" \
-d '{"text": "hey chat, is this bussin or nah"}'
# trending terms
curl localhost:8899/api/trendingSlang doesn't rely on a static word list. The collector runs daily, scanning Reddit (r/GenZ, r/slang, r/teenagers, r/brainrot), TikTok comment sections, and Twitter/X for terms that match the pattern of emerging slang — high novelty, rapid spread, clustered usage among young demographics.
Each candidate term is:
- Verified by the LLM as actual slang (not a typo, not a brand name)
- Defined with context, usage examples, and origin
- Assigned a virality score based on velocity of adoption
- Assigned a shelf-life prediction based on historical patterns of similar terms
- Embedded for semantic search and nearest-neighbor lookups
The lexicon ships with a snapshot of ~4,000 terms. Run slang collect to update it with fresh data.
slang/
├── cli.py # command-line interface (click)
├── config.py # pydantic settings
├── server.py # FastAPI REST API
├── lexicon/
│ ├── store.py # SQLite-backed term storage
│ ├── models.py # Term, Definition, ViralityScore data models
│ └── seed.py # built-in lexicon snapshot
├── collector/
│ ├── base.py # abstract collector interface
│ ├── reddit.py # Reddit scanner
│ ├── tiktok.py # TikTok comment scraper
│ └── twitter.py # Twitter/X scanner
├── engine/
│ ├── translate.py # bidirectional translation engine
│ ├── vibe.py # cultural fluency scorer
│ ├── radar.py # trend tracking and lifecycle analysis
│ └── embeddings.py # sentence-transformer embeddings for semantic search
└── plugin/
├── system_prompt.py # LLM system prompt injection
└── export.py # export lexicon for fine-tuning
The vibe check isn't just "does this have slang in it." It evaluates:
- Vocabulary — are you using current terms, or are you three cycles behind?
- Syntax — Gen Z has distinct sentence structures. Short fragments. No capitalization. Strategic punctuation.
- Tone — irony, understatement, and hyperbole have specific patterns.
- Cringe detection — using slang incorrectly is worse than not using it at all. "Hello fellow kids" energy gets flagged.
The score is calibrated against a dataset of 50,000 real posts from users aged 16-24.
Every generation invents its own language. That's not new. What's new is the speed. A word can go from a small Discord server to mainstream news coverage in 48 hours. TikTok accelerated language evolution by 10x compared to previous generations. By the time a term hits Urban Dictionary, it might already be dead.
This creates a real problem for anyone building consumer products, writing marketing copy, training AI models, or just trying to communicate with people younger than them. You can't keep up manually. You need infrastructure.
Slang is that infrastructure.
If this is useful and you want to see it keep running:
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