3. Data, Science & AI

Comprehensive reference for designing PostgreSQL tables, covering data types, indexing, constraints, and performance optimization patterns.

Processes and generates multimedia content (audio, images, video, documents) via Google Gemini API for multimodal AI analysis and creation.

Enables effective context management in AI agent systems through architecture design, token optimization, memory systems, and multi-agent coordination.

Packages code repositories into AI-optimized formats, preserving structure and context for efficient language model analysis.

Manages database operations for MongoDB and PostgreSQL, including schema design, query optimization, migrations, and performance analysis.

Manages Model Context Protocol (MCP) servers to discover, analyze, and execute AI model tools, prompts, and resources programmatically.

Enables programmatic creation of AI-generated notebooks, source management, and artifact generation via Google NotebookLM API.

Selects optimal external AI models for code analysis, bug investigation, and architectural decisions using empirically validated rankings and consultation strategies.

Guides creation of structured, reproducible Jupyter notebooks for MLOps prototyping with emphasis on configuration management and pipeline integrity.

Guides setup of a new MLOps project with standard tools (uv, git, VS Code) and best practices for machine learning deployment.

Guides structuring MLOps projects for team collaboration, model sharing, and community engagement in machine learning workflows.

Guide for implementing end-to-end observability in MLOps, covering reproducibility, lineage, monitoring, alerting, and model explainability.

Batch-generates images via OpenAI Images API with random prompts and creates an index.html gallery.

Generates and edits images using Gemini 3 Pro Image AI, supporting text-to-image, image-to-image, and 1K/2K/4K resolutions.

Converts PDF, Word, Excel, images, audio, and other formats to Markdown for text analysis and LLM processing.

Architects autonomous AI agents with tool use, memory systems, and multi-agent orchestration for complex task execution.

Provides tools for building reliable autonomous AI agents through goal decomposition, planning loops, and error mitigation strategies.

Builds and optimizes RAG systems using vector databases, embeddings, and retrieval strategies for enhanced LLM applications.

Expertly designs and optimizes prompts for LLM applications to ensure accurate, context-aware, and formatted outputs.

Provides tools for building natural voice interaction systems with low-latency speech-to-speech and pipeline architectures, handling real-time conversation challenges.

Expert in CrewAI, a role-based multi-agent framework for building collaborative AI agent teams with task orchestration and memory systems.

Designs AI agent tools using JSON Schema, LLM-optimized descriptions, and validation to prevent hallucinations and reduce token costs.

Expert guide for crafting effective prompts, optimizing AI model interactions, and debugging agent behavior through proven strategies.

Provides implementation patterns for Retrieval-Augmented Generation including chunking, embeddings, vector stores, and retrieval optimization.