3. Data, Science & AI

Comprehensive reference for designing PostgreSQL database schemas, including data types, indexing, constraints, and performance patterns.

Generates evidence-based clinical decision support documents with statistical analysis, biomarker stratification, and regulatory compliance for pharmaceutical research.

Provides programmatic access to COSMIC database for querying cancer mutations, gene fusions, and mutational signatures in precision oncology research.

Enables exact symbolic computation in Python for algebra, calculus, equations, and mathematical expressions without numerical approximation.

Data framework for building LLM applications with RAG, featuring document ingestion, indexing, and querying for knowledge retrieval systems.

AI-powered speech-to-text model for multilingual transcription, translation, and language identification.

Creates scientific visualizations including line plots, bar charts, heatmaps, and 3D graphs for publication-ready figures in multiple formats.

Enables efficient out-of-core processing and analysis of massive tabular datasets (billions of rows) with lazy evaluation and visualization.

Programmatic PDF toolkit for extracting text and tables, creating, merging, splitting, and filling forms to enable document data analysis.

Python library for geospatial vector data analysis, enabling spatial operations, coordinate transformations, and map visualization with common formats.

Generates music and sound effects from text descriptions using AI models (MusicGen and AudioGen) built on PyTorch.

Provides molecular featurization tools for machine learning, including ECFP, MACCS, and ChemBERTa, to convert SMILES into features for QSAR and molecular ML.

Processes FCS flow cytometry files, extracting events and metadata into NumPy arrays and DataFrames for scientific data preprocessing.

Enables production deployment, monitoring, and scaling of ML models with MLOps, RAG systems, and LLM integration for enterprise AI solutions.

Optimizes AI model inference on CPU/GPU by quantizing models to 2-8 bits using GGUF format and llama.cpp, enabling deployment on consumer hardware.

Enables real-time AI-powered web searches using Perplexity models for current information, scientific literature, and source-cited answers via OpenRouter API.

Provides API access to openFDA data for drugs, devices, adverse events, recalls, and regulatory submissions to support safety research and analysis.

Queries NCBI Gene database via E-utilities/Datasets API to retrieve gene info (RefSeqs, GO, locations, phenotypes) for annotation and functional analysis.

Provides a multi-objective optimization framework with algorithms like NSGA-II and MOEA/D for engineering design and optimization problems.

Runtime safety framework for LLM applications with jailbreak detection, hallucination prevention, and PII filtering using Colang 2.0 DSL.

Deploys and scales machine learning models and compute-intensive Python workloads in the cloud with GPU acceleration and auto-scaling.

Provides fast nonlinear dimensionality reduction using UMAP for 2D/3D visualization and clustering preprocessing of high-dimensional data.

Enables clean, single-file LLM training and fine-tuning with LitGPT, supporting LoRA/QLoRA for educational and production use.

Reduces LLM size and accelerates inference using pruning techniques like Wanda and SparseGPT, achieving 50% sparsity with minimal accuracy loss.