Filter AI-generated LinkedIn posts using an open-source browser extension

Executive Summary

Professionals who rely on LinkedIn for discovery and hiring face a growing signal-to-noise problem: an increasing share of low-effort, AI-generated posts dilutes useful content and wastes attention for a global population of roughly 300 million knowledge workers. Recruiters, marketers, and subject-matter experts specifically complain about reduced feed quality and desire tools to curate and restore trust in what they see. You could build an open-source browser extension that detects, flags, and optionally filters AI-generated LinkedIn posts using a privacy-first, client-side classifier with explainable signals and per-user configurable rules; features would include shareable filter presets, enterprise policy enforcement, and community-sourced labeling to improve models. Making it open source and local-first addresses the privacy backlash while leaving room for a $50/year ACV subscription offering for managed enterprise features and support. The timing is favorable: the market maps to an addressable $15.0B opportunity (300M users × $50/year) as AI content creation surges and professionals push for more control over feeds, and distribution is simplified by the mature Chromium/WebExtension ecosystem. Competition is medium—there are content curation tools and proprietary detectors, but few open-source, privacy-respecting options focused specifically on LinkedIn signal quality. To stand out you should emphasize transparency (open code and explainable flags), a local-first architecture for privacy, and UX that minimizes false positives while enabling shared community filters and enterprise policies. Realistic challenges include an arms race with improving generative models, potential platform policy resistance, and the need to continuously fund label quality and classifier maintenance, so early focus should be on demonstrable accuracy, legal review, and clear value for power users and procurement teams.