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CLUSTER SPAM-WEB-LEARNING · 4 ENTRIES · EARLIEST ENTRY 2026-09-04

ML SMS spam detectors

This cluster consists of web-based machine learning tools that classify SMS messages (and in one case, emails) as spam or legitimate. The dated history before the index start is too thin to read a shape from—only 0 dated entries exist, indicating the cluster predates the index or its members are undated. Traction leaders all show 0 stars, with SpamShield, OR-Spam-Detector, and sms-spam-detection tied for the highest activity signal.

Entry timeline

ENTRY TIMELINE

AUG 2026: 1SEP 2026: 3
AUG 2026SEP 2026

Each bar is the month a member carries, which is a creation date where the source published one and a crawl date otherwise. Nothing in the index was found before 11 August 2026, and coverage broadened sharply on 14–15 August, so a bar from then on reports how much we found that month, not how much was built; an earlier bar reports how far back a member's own source dated it — not how long we have been watching.

Members by activity

Activity signals, not revenue.

Build origin · 4 unlabelled

Operator · 3 hobby · 1 unlabelled

Two labels per entry, never collapsed into one: how it was built, and who stands behind it. Counted over the 4 listed below. Unlabelled means nothing has decided yet — not that the answer is small.

  1. 01sms-spam-detection0★25 days—hobbyWeb app that classifies SMS messages as spam or legitimate using a trained machine learning model.
  2. 02sms-spam-classifier0★1 mo—hobbyWeb-based tool that lets users submit an SMS message and automatically detects whether it is spam or legitimate using machine learning.
  3. 03OR-Spam-Detector0★1 molivehobbyWeb app that classifies emails and text messages as spam or not spam and keeps a history of predictions.
  4. 04BongoScam - SMS Checker | Swahili SMS Scam Detection0★unknownliveWeb app that detects scam messages in Swahili SMS using machine learning.

Ranked by traction, highest first, and paged 20 at a time — this is a window on the cluster, never the whole of it. Ties are broken by a stable id so a product cannot appear on two pages or fall between them. Browse every cluster.