PAIN TRIGGERS
Regex and keyword blockers fail to filter changing slop content.
Feed overwhelmed by ragebait and parrot-posting.
Other· LinkedIn usersPain 7.00/10WTP 5.0/10Market 8.0/10Validation 6.0Confidence 88%Apr 19, 2026
SlopShield: AI-Powered Feed Filter for LinkedIn and X
Feeds overwhelmed by ragebait and repetitive 'engagement slop' posts; regex/keyword blockers fail due to constantly changing text.
ai-poweredbrowser-extensioncontent-filteringlinkedinlocal-llmproductivitysocial-mediatech-savvy-userstwitter
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STAGE 01 · PROBLEM
Is the problem real?
CANONICAL PROBLEM
Overwhelmed by 'engagement slop' like ragebait and repetitive posts on LinkedIn and X feeds, making scrolling intolerable.
FREQUENCY
Limited repetition signal.
INTENSITY
Users explicitly describe existing tools as bloated/overkill and mention workaround behavior.
EVIDENCE
I got sick of the "engagement slop" on LinkedIn and X, so I built an extension that uses local LLMs to hide it.
r/SideProject21
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STAGE 02 · CUSTOMER
Who feels this pain?
TARGET USERS
LinkedIn usersTech Professionals Scrolling Social Feeds
Tech-savvy LinkedIn and X users frustrated with engagement slop
Context
Filter and hide low-quality, intent-based slop posts in LinkedIn and X feeds using intelligent classification.
Using regex and keyword blockers to filter posts
Current Workarounds
Manually crafting regex and keyword blockers
Reactively muting accounts after seeing slop
Avoiding feeds by using search or lists only
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STAGE 03 · MARKET
Where's the gap?
EXISTING SOLUTION GAPS
Regex and keyword blockers ineffective due to constantly changing post text
OPPORTUNITY & VALUE
Why Now
Strong personal frustrations with slop overload and blocker failures, though not highly repeated across posts.
Value Proposition
Local AI processing handles evolving slop text without cloud dependency or rate limits.
Product Direction
Browser extension using local LLMs for intelligent classification and hiding of low-quality slop posts.
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STAGE 04 · BUSINESS
How does it make money?
MONETIZATION
$4.99/moUnlimited hides · pro AI accuracy
Model
Freemium browser extension
WILLINGNESS TO PAY
Users invest time hacking regex workarounds for relief from intolerable feeds; a $5/mo automated solution saves hours weekly and beats manual tweaks, as evidenced by repeated frustration with failing filters.
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STAGE 05 · EXECUTION
How do you ship it?
MVP PLAN
“Hide engagement slop from your feeds automatically.”
Browser extension using local LLMs for intelligent classification and hiding of low-quality slop posts.
Core Features
Real-time AI classification of posts as slop/ragebait
One-click hide with smooth layout handling
Customizable slop detection thresholds
Local LLM integration for privacy
Weekly Roadmap
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W1-W2
Core AI slop detector processes posts locally.
- •Fine-tune lightweight LLM on slop examples
- •Build Chrome extension skeleton with content script
- •DOM parser for LinkedIn/X post text extraction
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W3-W4
Hide functionality works with toggle for both platforms.
- •Implement real-time post scanning and hide CSS
- •Add sensitivity slider and basic keyword fallback
- •Test on live feeds with 10 dogfooders
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W5
Polish UI and freemium gating ready for beta.
- •Stripe integration for pro subscriptions
- •Export/import blocklist feature
- •Internal tests fix layout shift issues
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W6
Chrome Web Store launch with first 100 installs.
- •Submit to Chrome store review
- •Create landing page with demo video
- •Seed posts on HN/Product Hunt
Launch Strategy
Launch on Chrome Web Store; promote in r/LinkedIn, r/Twitter, r/LocalLLaMA, and X tech threads.
RISKS & ASSUMPTIONS
Top Risks
AI classification accuracy
Model may misclassify nuanced posts as slop, leading to user churn from hidden valuable content.
Platform policy changes
LinkedIn/X updates could restrict DOM access or ban extensions scraping post content.
User tolerance for layout shifts
Retroactive hiding causes annoying feed reflows, as noted in quotes, potentially reducing adoption.
Competition from free blockers
Users accustomed to free tools like uBlock may resist paying for AI upgrade.
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STAGE 06 · DECISION
Should you build it?
NEED A CLEARER CALL?
Run an Investment Memo to get a structured Go / No-Go verdict, competitor landscape, unit economics, and a 90-day validation roadmap for this opportunity.
Generate an investment memoWhat this score means
This idea scores in the upper-middle range of opportunities surfaced by MonetScope, with a validation sub-score of 6/10 against 1 independently sourced evidence signals. A "promising" rating usually indicates a real pain has been detected and discussed in the open, but the pipeline did not find enough signal to flag it as urgent or high-frequency. These opportunities can still produce excellent businesses — they often correspond to "boring" problems that established players have ignored — but the founder should expect a longer customer-development cycle to confirm willingness to pay.
Why this matters for Other founders
It sits at the intersection of "ai-powered", "browser-extension", "content-filtering", which makes it relevant to a specific subset of founders rather than a generic horizontal opportunity. Opportunities in this category typically reward founders who can describe the pain in the user's own language — both because that's the basis of effective marketing, and because it's the strongest signal that the founder has done the upfront listening. The MonetScope pipeline surfaces this category alongside other other signals, which is why it appears here rather than in a generic "trending ideas" feed.
Scores are derived from real forum discussions across Reddit, Hacker News and X, weighted by evidence volume and signal quality. How scoring works
Frequently asked questions
Is "SlopShield: AI-Powered Feed Filter for LinkedIn and X" a real validated startup idea or just an AI-generated suggestion?
MonetScope does not generate ideas from a language model's imagination. Every opportunity on this site is anchored to specific source posts and comments from real public discussions — typically on Reddit, Hacker News, or X — where actual users describe the pain in their own words. The AI's role is structuring, scoring, and grouping those signals into a navigable opportunity, not inventing the problem.
How recent is the underlying data for ai-powered?
MonetScope's spider pipeline runs continuously and surfaces opportunities as new evidence accumulates. The "Updated" date in the header reflects the most recent re-scoring of this specific opportunity. Most other opportunities visible in the public catalog draw from discussions in the last 30-60 days; older signals are de-prioritized because user pain shifts faster than most founders assume.
What's the difference between "overall score" and "validation score"?
Overall score is a composite across six dimensions — pain, urgency, willingness to pay, market size, defensibility, and execution ease — designed to give a single number for triage. Validation score is narrower: it asks "how cleanly does the same signal repeat across independent sources?" An opportunity can score high on overall but lower on validation when one or two large discussions dominate the evidence; conversely, validation can be high on a smaller-overall idea where the signal is consistent but the addressable market is modest.