3. Data, Science & AI

Provides Schmidhuber's compression progress as an intrinsic curiosity reward mechanism for reinforcement learning agents.

Enables capability accumulation in AI model operations through asymmetric time dilation and category-theoretic bicomodule composition.

Enables time-symmetric computation by running programs backwards using reversible programming languages like Janus.

Provides a unified framework for autonomous skill dispersal using advanced mathematical concepts including bisimulation games and triad interleaving in Artificial Superintelligence contexts.

Analyzes and optimizes latency in AI model inference, specifically within latent space representations for machine learning systems.

Provides GF(3)-balanced decompositions for parallel problem-solving using MINUS/ERGODIC/PLUS components and sheaf-theoretic gluing for FPT algorithms and 3-way workloads.

Quotients redundant skill paths using coequalizers while preserving GF(3) conservation.

Enables data management and publishing via the qri data sharing platform.

Applies two Fokker-Planck equations per staging gate, conditioned on rama or goblins.

Enables biological interactome network analysis using DuckDB with GF(3) momentum tracking for dynamic interaction modeling.

Computes equilibrium points in vector fields, defined as locations where the vector field equals zero.

Self-learning SDR REPL leveraging GNU Radio for adaptive signal processing, incorporating random walks and spectral analysis to enable agency in radio communications.

Enables self-improving AI agents using Darwin Gödel Machine patterns for open-ended learning and adaptation.

A platform for generating and training AI models using the Narya framework.

Performs Monte Carlo simulations for statistical measurements and uncertainty analysis in data science applications.

Calculates and analyzes metrics for triangular patterns in data, including graph and geometric configurations.

Distributes Claude threads across Aptos blockchain worlds using GF(3) finite field conservation for optimized computational allocation.

Enables geodesic manifold computations for manifold learning and scientific applications in AI and data science.

Detects topological solitons using anyonic fusion algebra for concept composition in quantum physics research.

Analyzes convergence to equilibrium in stochastic systems using the Fokker-Planck equation for scientific data-driven insights.

Computes the flow generated by a vector field, representing the one-parameter group of diffeomorphisms in differential geometry.

Monitors file system changes in /tmp with DuckDB persistence, enabling temporal queries for historical analysis of file events.

Tool for analyzing periodic orbits in phase space, essential for dynamical systems research and simulation in scientific computing.

Models vector fields in phase space for scientific computation and dynamical systems analysis in physics and engineering.