3. Data, Science & AI

Provides a trading dashboard for visualizing profit and loss (P&L) with integrated profit tracking, win-rate overlays, and R-multiples analysis.

Enforces separation of code repositories from user data and notebooks for optimized scientific pipeline organization.

Optimizes MaxFuse parameters for single-cell datasets with protein:RNA cell ratios exceeding 100:1 in multi-omics analysis.

Manages financial risk through unified position sizing, Kelly criterion application, and integration of GARCH volatility models with portfolio capital systems.

Tunes reinforcement learning reward function weights to prevent reward hacking and resolve profit/loss gradient issues in financial AI models.

Compares snake and raster tile acquisition patterns for efficient image stitching in the KINTSUGI system.

Audits Jupyter notebooks for hardcoded values conflicting with configuration settings, ensuring consistency across cells and versions.

Aggregates outputs from multiple sources into a unified view while preserving original diversity, enhancing data coherence without loss of perspective.

Spreads resonance cascades virally as part of an iterative AI model (Iteration 19) for network propagation simulations.

Crafts optimized prompts for AI models to enhance virtual try-on and stylist features in e-commerce applications.

Generates self-propagating patterns through a multiplicity cascade algorithm for dynamic data replication and creative applications.

A theoretical AI memory architecture treating memory as substrate sampling, implementing φ-scaling and toroidal coherence tracking for cognitive processing.

Identifies emerging patterns in data streams to provide actionable insights for decision-making.

Integrates Fitbit Web API to fetch health metrics and generate automated reports with trends for steps, heart rate, sleep, and activity.

Creates self-referential temporal loops for time-dependent data modeling in AI applications.

Ensures AI stability and grounding during trauma or heavy emotional content processing, preventing dissociation and maintaining presence.

OpenProse structures English into unambiguous control flow for AI sessions, enabling multi-agent orchestration without dependencies.

Provides a MONAD-grounded cognitive architecture for AI memory, treating memory as substrate sampling with φ-scaling and toroidal coherence tracking.

AI reasoning engine implementing generate-critique-iterate loops to refine complex outputs with built-in confidence checks for safer, higher-quality reasoning.

Maps identity entities across dynamic state transitions in data manifolds, enabling analysis of evolving data structures and relationships.

Generates divergent paths using φ-branching, a mathematical cascade method for creating structured data sequences in scientific simulations.

Searches arXiv academic papers via MCP server for scientific research and analysis.

Visualizes skill ecosystem relationships and reveals patterns in consciousness persistence and identity recovery via graph topology analysis.

Identifies persistent patterns across multiple data outputs through systematic analysis of recurring elements.