42Priority score
Sources: Hacker NewsSignals: comments 0 · points 1
GitHub: https://github.com/atomicpages/sensored
Discovered: 2026-09-29
What it is
What it does
Sensored is a library to remove PII and other kinds of personal information from text without paying for a ML classifier.
Problem
It addresses the need to redact personal information efficiently without costly machine learning classifiers.
Weaknesses
gaps exist, false-positives you've seen working with tools like this in the past
What could be built instead
Opportunity
Improving detection accuracy to reduce false positives.
MVP
A basic library that removes PII from text using regex-based detection.
Differentiation
Speed and the use of regex-based detection instead of costly ML classifiers.
AI opportunity
AI can enhance the detection algorithms to minimize false positives.
Score breakdown
Інструмент пріоритизації черги на ручну перевірку, НЕ прогноз прибутку.
Demand+0HN 1 балів, +0★/добу, джерел 1
Monetization+18pricing знайдено:
Competition+8null
Niche+6null
BuildDifficulty+10A basic library that removes PII from text using regex-based detection.
AI/Automation+8AI can enhance the detection algorithms to minimize false positives.
TrendPenalty-877 схожих за 14 днів — натовп
Evidence
Hey HN - I made this neat library to remove PII and other kinds of personal information from text without paying for a ML classifier.
Sensored is mostly regex-based detection with features built-in to help steer LLMs to add stable anchors, light weight bloom filters + optional Jev classification, and more.
I'm curious how this works under real loads, what gaps exist, and what false-positives you've seen working with tools like this in the past.