Project ideas from Hacker News discussions.

Green’s Dictionary of Slang - Five hundred years of the vulgar tongue

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

Three prevailing themes

Theme	Key points	Representative quotes
1. Cataloguing profanity	Users discuss creating and maintaining lists/dictionaries of swear words in various languages.	“Nice! Brings back memories how we made a list of expressions for “fucking” in Czech. Got to 344 before moving on.” – yread
		“I can also recommend Roger’s Profanisaurus for a British view of swearwords and vulgar euphemisms.” – gadders
2. Language evolution & innovation	The dynamic nature of profanity is highlighted, especially how new forms emerge and are normalized in text processing.	“I did a lot of text cleaning a while ago and we tried to normalize curse word spelling… It is really clear how much innovation in the English language is happening there.” – jmward01
3. Historical & cultural context	Participants reference historical works and cultural anecdotes that illustrate how profanity reflects societal attitudes.	“Orwell’s Down and Out in Paris and London documented some of the swear words of his time.” – mmsc
		“Tough guys with Mullets that blasted Metallica said “Mint”… I just learned it also meant “a trace of homosexual tendencies” a few decades prior.” – runamuck

These threads collectively show a community fascinated by the systematic study of profanity, its rapid evolution, and its deep roots in cultural history.

🚀 Project Ideas

Profanity Normalizer API

Summary

A cloud‑hosted API that detects, normalizes, and optionally filters profanity in any text, supporting multiple languages and custom slang dictionaries.
Solves the frustration of manual text cleaning and inconsistent curse‑word handling that developers face when preparing datasets or moderating user content.

Details

Key	Value
Target Audience	NLP engineers, content moderation teams, data scientists, SaaS platforms
Core Feature	Real‑time profanity detection, spelling normalization, language‑agnostic slang mapping, customizable whitelist/blacklist
Tech Stack	Python (FastAPI), Redis, ElasticSearch, Docker, Kubernetes, OpenAI embeddings for slang expansion
Difficulty	Medium
Monetization	Revenue‑ready: per‑request tiered pricing (e.g., $0.001/request, $0.0005/request for volume)

Notes

HN commenters highlighted the need for “normalizing curse word spelling” and “text cleaning” as a major challenge (jmward01).
The API would let teams automate the “most interesting text cleaning” tasks without reinventing the wheel.
Practical utility: plug‑in for chat apps, comment sections, or data pipelines; discussion potential around privacy and bias in profanity detection.

SwearWord Explorer

Summary

A web platform that aggregates, translates, and visualizes profanity across languages, with community‑driven updates and scholarly annotations.
Addresses the lack of a single, up‑to‑date, multilingual resource for researchers and enthusiasts.

Details

Key	Value
Target Audience	Linguists, researchers, hobbyists, educators, developers
Core Feature	Interactive dictionary, translation pairs, etymology, usage examples, community voting, API access
Tech Stack	Next.js, PostgreSQL, GraphQL, WebSockets for real‑time updates, Docker
Difficulty	High
Monetization	Revenue‑ready: freemium with premium research reports and API access

Notes

Users referenced “The F Word” and “Roger’s Profanisaurus” as isolated resources; SwearWord Explorer unifies them.
The platform would support the “translation to English” effort mentioned by blauditore and the research migration from book to web noted by NelsonMinar.
Potential for lively discussion on cultural context, censorship, and the evolution of slang.

Slang Tracker

Summary

A real‑time monitoring service that scrapes social media, forums, and news to detect emerging slang and profanity, delivering trend analytics and alerts.
Meets the frustration of keeping up with the rapid evolution of language highlighted by ilamont and mmsc.

Details

Key	Value
Target Audience	Market researchers, brand managers, sociolinguists, content moderators
Core Feature	NLP‑driven slang extraction, trend heatmaps, sentiment overlay, API for custom dashboards
Tech Stack	Node.js, Kafka, spaCy, TensorFlow, Grafana, AWS Lambda
Difficulty	High
Monetization	Revenue‑ready: subscription tiers ($99/month for basic, $299/month for enterprise)

Notes

The discussion about “fast language evolution” and “hyperlocal terms” (ilamont) underscores the need for continuous tracking.
The tool would provide the “research for updates to the dictionary” that NelsonMinar’s project now offers online.
Discussion potential around data ethics, privacy, and the balance between monitoring and censorship.