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82Score
HN · front_page
Freemium
Build

AI Slop Filter Browser Extension

Build a browser extension that scores articles and posts for likely low-value AI writing, then hides, grays out, or summarizes them before the user wastes time. The winning angle is not just detection, but user control and nuance: separate AI-polished human writing from generic filler and allow community feedback loops.

5 Kanäle30-Tage-Erwähnungstrend: latest 1, peak 6, 30-day series
Auf Reddit ansehen
Entdeckt 4. Aug. 2026

Warum das wichtig ist

You open a technical article hoping to learn something useful, then quickly realize it is padded with generic, over-explained prose that burns minutes before delivering the actual insight. After repeating this enough times, you start scanning comments for clues or writing your own hacks to avoid reading junk. Existing tools either rely on crude AI detection, only work on a few sites, or fail to distinguish between a human using light editing help and content that is mostly synthetic filler. What you want is a reading layer that protects your time across the web and lets you tune how aggressive the filter should be.

  • · Entwickelt für Heavy readers of technical and professional content, including developers, analysts, researchers, and knowledge workers who consume high volumes of online articles and discussion threads..
  • · Wahrscheinlichste Monetarisierung: Freemium.

Der Schmerz · Narrativ

You open a technical article hoping to learn something useful, then quickly realize it is padded with generic, over-explained prose that burns minutes before delivering the actual insight. After repeating this enough times, you start scanning comments for clues or writing your own hacks to avoid reading junk. Existing tools either rely on crude AI detection, only work on a few sites, or fail to distinguish between a human using light editing help and content that is mostly synthetic filler. What you want is a reading layer that protects your time across the web and lets you tune how aggressive the filter should be.

Score-Details

Schmerzintensität9/10
Zahlungsbereitschaft7/10
Umsetzbarkeit6/10
Nachhaltigkeit7/10

Marktsignal

30-Tage-ErwähnungstrendSpitze: 6
Sparkline: latest 1, peak 6, 30-day series
Abgedeckte Kanäle
front_pageChatGPTwebdevselfhostedEntrepreneur

Markteinführung

Genauer Zielnutzer

Developers and technical readers who browse long-form web content daily and feel their reading time is being diluted by low-value AI-generated prose.

Geschätzte Nutzeranzahl

~100K highly engaged early adopters globally

Primärer Akquisekanal

Twitter dev community

Preisanker

$8/month

Erster Meilenstein

1000 extension installs and 50 paying users within 30 days of launch

MVP-Umfang · 1–2 Wochen

Woche 1
  • Build a Chrome extension that injects a score badge into article pages
  • Create a simple classifier pipeline using readability, repetition, and LLM-assisted style heuristics
  • Add manual controls to hide, gray out, or ignore a page
  • Store per-domain preferences and allowlists locally
  • Set up a landing page with waitlist and install instructions
Woche 2
  • Add optional server-side scoring API for heavier pages and faster iteration
  • Implement thumbs-up and thumbs-down feedback collection on scores
  • Launch support for 3 high-traffic content sites with custom DOM parsers
  • Add quick bullet summary generation for flagged pages
  • Instrument retention, score override rate, and conversion analytics
MVP-Funktionen: On-page AI-likelihood and verbosity score · Auto-gray or hide setting for low-value content · One-click community feedback to improve detection · Fast extractive summary for borderline content · Site-specific rules and allowlists

Differenzierung

Bestehende Lösungen
PangramLinkedIn AI-content flaggingMeta self-disclosure checkboxOpenRouter provider listings
Unser Ansatz
There is a clear unmet need for independent trust infrastructure around AI-era content and inference: one side for readers filtering low-value generated prose, and another for developers evaluating hidden tradeoffs in model-serving vendors.

Warum dies scheitern könnte

Selbstwiderlegung — das wichtigste Vertrauenssignal

  1. 1Users may enjoy complaining about AI prose more than paying to solve it, leading to weak monetization despite strong engagement.
  2. 2The model may misclassify authentic but awkward writing, creating trust problems that are hard to recover from.
  3. 3Platform changes and anti-scraping restrictions could make reliable browser support expensive to maintain.

Evidenzzusammenfassung

Wie KI diese Erkenntnis synthetisiert hat — keine wörtlichen Zitate

The strongest signal in the discussion is repeated frustration with machine-like prose and wasted reading time. Roughly a dozen commenters described wanting filtering, reporting, or de-emphasis of likely AI-written content, and one person had already built a personal workaround. Existing options were mentioned, but users still debated quality, fairness, and portability across sites, suggesting room for an independent cross-site product.

1 1 Beitrag analysiert5 5 KanäleAI · KI-synthetisiert · keine wörtliche Wiedergabe

Aktionsplan

Validiere diese Gelegenheit, bevor du Code schreibst

Empfohlener nächster Schritt

Bauen

Starke Nachfragesignale erkannt. Echter Schmerz und Zahlungsbereitschaft vorhanden — fang an, ein MVP zu bauen.

Landing Page Textpaket

Druckfertige Texte basierend auf echten Reddit-Kommentaren — direkt einfügen

Überschrift

AI Slop Filter Browser Extension

Unterüberschrift

Build a browser extension that scores articles and posts for likely low-value AI writing, then hides, grays out, or summarizes them before the user wastes time. The winning angle is not just detection, but user control and nuance: separate AI-polished human writing from generic filler and allow community feedback loops.

Für Wen

Für Heavy readers of technical and professional content, including developers, analysts, researchers, and knowledge workers who consume high volumes of online articles and discussion threads.

Funktionsliste

✓ On-page AI-likelihood and verbosity score ✓ Auto-gray or hide setting for low-value content ✓ One-click community feedback to improve detection ✓ Fast extractive summary for borderline content ✓ Site-specific rules and allowlists

Wo Validieren

Teile deine Landing Page in r/HN · front_page — genau dort wurden diese Schmerzpunkte entdeckt.

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Häufig gestellte Fragen

Wer spürt diesen Schmerz?
Heavy readers of technical and professional content, including developers, analysts, researchers, and knowledge workers who consume high volumes of online articles and discussion threads.
Ist das eine echte Chance?
Diese Chance erreicht 82/100 bei der zusammengesetzten Metrik von Pain Spotter (Schmerzintensität, Zahlungsbereitschaft, technische Machbarkeit und Nachhaltigkeit). Validieren Sie weiter, bevor Sie Entwicklungszeit investieren.
Wie sollte ich das validieren?
Führen Sie 5 Customer-Discovery-Gespräche mit der Zielgruppe, veröffentlichen Sie eine Landingpage mit Warteliste und prüfen Sie den verlinkten Quellbeitrag auf aktuelle Aktivitäten, bevor Sie mit der Entwicklung beginnen.