
AI AdBlocker sɣur eye3
AI-powered blocker (your path) Hybrid rules + on-page heuristic for Firefox MV2. Uses a machine learning model (running in the browser with ONNX Runtime) to see or analyze the DOM.
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Ilaq-ak·am Firefox i useqdec n usiɣzef-a
Asiɣzef aɣefisefka
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Ɣef usiɣzef agi
This add-on is a starter AI Ad Block extension.
• The uploaded .zip is the Firefox-compatible build (no rules/ directory, uses background.scripts instead of service_worker).
• The uploaded source .zip contains the full, human-readable source code (TypeScript, scripts, manifests, build files) without dist/ or node_modules/.
• No remote code execution, dynamic code generation, or obfuscated code is used.
• Can detect hidden ads, sponsored labels, promoted posts, native ads that rule-based systems miss.
• Can adapt better if you re-train the model with new data.
Example AI Features You Can Add DOM / Heuristic Classifier:
Train a lightweight ML model on HTML snippets (features: tag type, attributes, text like “Sponsored”).
Content script grabs candidate nodes → runs model → hide if classified as ad.
Vision-based Ad Detection:
Use a small CNN (e.g., MobileNet/ONNX quantized) to check if an <img> looks like a banner ad.
Useful for “image-only” ads where markup doesn’t give them away.
Hybrid (most practical):
Use heuristics to filter likely candidates (divs with fixed size, suspicious classes, “sponsored” text).
Use ML to confirm → avoid false positives.
Future-proofing: when advertisers obfuscate HTML/CSS, rules break → but your ML model still generalizes.
Privacy-preserving: everything runs locally in the browser; no need to send page data to servers.
Research value: positions your extension as “next-gen” ad blocker, different from commodity ones.
• The uploaded .zip is the Firefox-compatible build (no rules/ directory, uses background.scripts instead of service_worker).
• The uploaded source .zip contains the full, human-readable source code (TypeScript, scripts, manifests, build files) without dist/ or node_modules/.
• No remote code execution, dynamic code generation, or obfuscated code is used.
• Can detect hidden ads, sponsored labels, promoted posts, native ads that rule-based systems miss.
• Can adapt better if you re-train the model with new data.
Example AI Features You Can Add DOM / Heuristic Classifier:
Train a lightweight ML model on HTML snippets (features: tag type, attributes, text like “Sponsored”).
Content script grabs candidate nodes → runs model → hide if classified as ad.
Vision-based Ad Detection:
Use a small CNN (e.g., MobileNet/ONNX quantized) to check if an <img> looks like a banner ad.
Useful for “image-only” ads where markup doesn’t give them away.
Hybrid (most practical):
Use heuristics to filter likely candidates (divs with fixed size, suspicious classes, “sponsored” text).
Use ML to confirm → avoid false positives.
Future-proofing: when advertisers obfuscate HTML/CSS, rules break → but your ML model still generalizes.
Privacy-preserving: everything runs locally in the browser; no need to send page data to servers.
Research value: positions your extension as “next-gen” ad blocker, different from commodity ones.
Rated 0 by 0 reviewers
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Yesra tisirag:
- Kcem ɣer isefka-inek deg ismal web meṛṛa
Ugar n telɣut
- TigIseɣwan n uzegrir
- Lqem
- 0.1.0
- Teɣzi
- 2,89 MB
- Aleqqem aneggaru
- 6 μέρες πριν (18 Αυγ 2025)
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- Rnu ar tegrumma
Mudd afus i uneflay-agi
Aneflay n usiɣzef-agi isutur-ak-d tallelt akken ad iseddu taneflit ines ticki tmuddeḍ-as cwiṭ n tewsa.
Ugar n isiɣzaf sɣur eye3
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It does not collect or transmit user data.
Reviewers can build the extension from source using the included scripts (see README).