3. Data, Science & AI

Provides tools and techniques for efficient LLM inference, model serving, optimization, and local deployment including vLLM and GGUF formats.

Evaluates AI agent performance using test frameworks, quality metrics, and benchmarks for LLM-based systems.

Analyzes Excel formulas to map dependency relationships and trace calculation flows for validation and reuse.

Offers tokenization capabilities for NLP using Hugging Face's library, supporting BPE, WordPiece, and custom vocabulary training.

Provides fast repository indexing for RAG workflows to reduce context drift during AI analysis.

Enables geospatial data manipulation, analysis, and visualization using Python, supporting formats like shapefiles and GeoJSON.

Implements Manifold-Constrained Hyper-Connections (mHC) to solve residual connection issues in neural networks via Doubly Stochastic Matrices.

Provides tools for training, fine-tuning, and optimizing large language models using distributed frameworks and efficient techniques.

High-performance data frame operations with lazy evaluation, parallel processing, and Arrow backend for efficient ETL and large dataset transformations.

Optimizes and compiles prompts for LLMs using DSPy, enabling declarative and automatic prompt engineering for AI applications.

Enables Bayesian statistical modeling, MCMC sampling, and uncertainty quantification using PyMC for probabilistic programming tasks.

Manages collaborative AI agent workflows, role-based coordination, and autonomous team execution through CrewAI framework.

Enables graph analysis, network algorithms, and community detection for social networks and knowledge graphs.

Provides SHAP-based model explainability for feature importance, prediction interpretation, and debugging in machine learning workflows.

Guides the design, description, and implementation of AI agent tools, including MCP specifications and function calling protocols.

Diagnoses and debugs AI agent failures related to context issues such as lost-in-middle, context poisoning, and attention pattern anomalies.

Enables comprehensive spreadsheet creation, editing, and analysis with formulas, formatting, and data visualization for data processing tasks.

Guides the design and implementation of multi-agent systems using patterns like supervisor, orchestration, and swarm coordination.

Provides geocoding utilities using OpenStreetMap Nominatim API for accurate GPS coordinate lookup from addresses.

Analyzes Shanghai dialect transcriptions for consistency, typos, and alignment shifts using project-specific xtask tools.

Enhances image quality through upscaling, sharpening, and resolution improvement techniques for technical applications.

Enables multimodal AI tasks including text-to-image generation, speech-to-text transcription, and image-text similarity analysis.

Enables document-focused AI applications using RAG frameworks like LangChain and LlamaIndex for semantic search and QA.

Provides parallel and distributed computing for large-scale data processing, enabling out-of-core operations and parallel pandas/numpy integration.