Why existing profanity detection libraries suck and how I built a better one
In this technical retrospective (2019), @vzhou842 shares practical architectural lessons learned from running, scaling, or migrating production software under lean constraints.
Core Principles for Independent Builders:
Defaulting to simpler architectures (such as monolithic frameworks, SQLite, or managed VPS) prevents early engineering bankruptcy.
Evaluating true compute and egress costs reveals that high cloud bills can often be slashed by 80%+ through judicious re-platforming.
Code you write at 2 AM as a solo engineer must be easy to debug six months later without complex microservice dependencies.
Community Debate Themes:
HN engineers debated the tradeoffs between cloud convenience and raw server economics, database indexing strategies, and resisting premature architectural complexity.
CATEGORY Frugal Engineering →
HISTORICAL ERA Cloud & Mobile Era (2014–2019)
COMMUNITY SCORE 12 points
TOTAL DISCUSSIONS 17 comments
Most of the words on the profanity list are in common use in polite company these days. If the goal is to identify hate speech and sexual speech, doesn't there need to be a semantic component. I wonder why a profanity filter is needed--what is the use case?
Only as anecdata, I remember a Forum/Discussion Board (technical) that a few years ago had issues (its word filter) with the word Matsushita (Panasonic was fine ;)).
a propos the subjectivity of profanity, the word Slut in Danish means finished, it might often be found at the end of long bodies of text.
There is no sociologically valid use case for a profanity detection library. This is a misuse of technology. Get over your shit.
Do none of the winners of the Kaggle competition he linked release their code?