Combating Internet Brain Rot Through AI
Advanced AI-powered content moderation research to foster healthier digital environments. Open-source models, comprehensive datasets, and rigorous academic methodology.
Advancing Digital Wellness Through AI Research
Founded by computer science students at FEU Institute of Technology, we're developing cutting-edge solutions to combat the degradation of online discourse and critical thinking.
Research-Driven Approach
Our interdisciplinary research combines deep learning and behavioral psychology to understand and address the root causes of neurodegenerative online content.
Through rigorous academic methodology and open-source development, we're building transparent, accountable AI systems that can be audited and improved by the global research community.
Transfer Learning
Augmented tools built on top of industry-leading models
Content Protection
Moderation of harmful, explicit, and uneducational content
High-Performance Computing
Trained and hosted on powerful Google Cloud TPUs & NVIDIA GPUs
Precision Filtering
High accuracy, customizable, and critical content analysis
Upload Your Video
Demo with our fine-tuned multimodal model specializing in recognizing sludge videos - a subtype of internet brain rot that grabs attention by presenting mutliple streams of visual clips (e.g. Subway Surfers, Family Guy, Soap Cutting,)
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Advancing AI Content Moderation
Our research spans multiple domains with a focus on practical applications, open-source development, and academic rigor.
Visual-Qwen & Sludge-Qwen: Augmenting Multimodal Deep Learning with Attention Mechanisms to Recognize ”Sludge Content” from Short Form Videos
FEU Institute of Technology - Computer Science Thesis
- • Open-source 6k human-model-annotated expert-validated multimodal dataset of short-form videos of sludge & non-sludge content
- • Multimodal architecture that leverages attention mechanisms and integrates video and textual features
- • Pre-trained Vision-Language Model (Visual-Qwen) that can be fine tuned on various datasets
- • Fine tuned model (Sludge-Qwen) for detecting sludge and non-sludge content from short-form videos
- • Study in tackling a modern global digital problem with High Acceptance from target audience and experts
- • TPU training methodology at FEU Institute of Technology
Development Timeline
From research inception to commercial launch - tracking our milestones and future roadmap.
Startup Founded
MicroMarc Research officially established
Research Inception
Initial research and development of the MicroMarc AI system
Initial Dataset & Grant
Ethically scraped 2K dataset + TPU Research Cloud grant secured
Novel Architecture
Developed breakthrough multimodal AI architecture
Model Training
Trained initial multimodal content moderation model
Expert Validation
Verified with 12 AI/Data experts & 40+ content creators/moderators
Meet the Researchers
Four dedicated CS students and their extraordinary advisor from FEU Institute of Technology, combining academic excellence with entrepreneurial vision.
Justine Jude Pura
Advisor
Marc Olata
Founder & CEO
Alpha Romer Coma
Founding Engineer
Job Isaac Ong
Founding Data Scientist
Kristoffer Ian Sioson
Founding Data Scientist
Target Audience & Business Model
Addressing multiple market segments with AI-powered content moderation solutions.
Social Media Platforms
Enterprise-grade content moderation APIs
Parents & Families
Mobile app for child digital wellness
Content Creators
Specialized content analysis & recommendations
Content Moderators
AI-powered content moderation tools
Digitally Conscious Users
Background agents for healthier browsing
Revenue Streams
Business Model
Sponsors & Partners
Grateful for the support of organizations sharing our vision of healthier digital environments.
YouTube Researcher Program
Ethical Data Collection
TPU Research Cloud
AI Infrastructure
🤝 Become a Partner
Support cutting-edge AI research and help combat internet brain rot through strategic partnerships.