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🤝 Top Collaborators
🤓 Latest Submissions
GenomeAtlas AI™ – Mapping Evolution ⏳ Intelligence
🧬GenomeAtlas AI™ is a next-generation Evolutionary Time Machine and multimodal scientific intelligence platform that transforms biological observations into interactive evolutionary experiences. Users can provide image, video, voice (VO), text, or PDF inputs, allowing the platform to analyze specimens and supporting scientific information through a unified multimodal workflow. The Evolution Knowledge Engine interprets available evidence and generates three evolutionary pathways: ⚛️ Quantum Analysis Radiation-induced mutation & quantum tunneling pathways — explores alternative mutation scenarios and probabilistic evolutionary possibilities as an experimental AI analysis layer. 🌿 Natural Selection Classic evolutionary pressures & adaptive radiation — examines environmental pressures, competition, survival, reproduction, climate, and adaptation to explore plausible evolutionary changes. 🕰️ Deep Time Million-year projections across alternate timelines — explores how species and ecosystems could change across extended geological timescales and alternative evolutionary trajectories. Users compare the generated scenarios and select a pathway to create the 3D Genome Time Machine Cube™—an interactive representation of species identity, evolutionary traits, environmental influences, adaptations, timelines, and projected characteristics. AI-generated imagery can visualize the selected evolutionary scenario. A key innovation is species-specific deep/resonant vocalization of Species Name, combined with intelligent voice narration explaining the species identity, evolutionary journey, adaptations, environmental conditions, and future scenario. Users can therefore see, rotate, hear, and explore their evolutionary model. GenomeAtlas AI™ — Where multimodal artificial intelligence meets the 4-billion-year story of life, Mapping Evolution Through Intelligence ------
10 Aug 2026
Brain Connect AI
Diagnostic errors affect approximately 12 million Americans annually, largely due to physician cognitive overload from fragmented data across electronic health records (EHRs), imaging systems, and rapidly evolving medical literature. Brain Connect solves this by deploying a collaborative multi-agent system that mirrors a hospital's diagnostic team. Instead of relying on a single LLM prompt, Brain Connect utilizes a stateful Lang Graph architecture where specialized agents handle distinct domains: 1. Clinical Agent: Parses patient history, symptoms, and lab results to extract key clinical signals. 2. Imaging Agent: Analyzes text-based radiology reports to identify anatomical anomalies and pathologies. 3. Literature RAG Agent: Queries a vector database of PubMed abstracts and medical guidelines to find relevant research, retrieving context using high-throughput embeddings. 4. Guidelines Agent: Cross-references findings against standard care protocols. 5. Consensus Orchestrator: The "Attending Physician" agent that aggregates all insights, resolves conflicting data, and generates a ranked list of differential diagnoses with confidence scores and a recommended treatment plan.
13 Jul 2026
