3. Data, Science & AI

Scrapes URLs to fetch article content and generates concise summaries for efficient information extraction.

Optimizes and designs effective prompts for LLM interactions, improving output quality and reliability in production environments.

Provides implementation patterns, templates, and examples for building RAG applications with LlamaIndex, including vector indices and query engines.

Configures vector databases (pgvector, Pinecone, etc.) with comparison guides and migration helpers for AI applications.

Guides users on implementing engram memory tools for semantic search, context persistence across sessions, and effective engram MCP usage in Claude.

Integrates ElevenLabs Speech-to-Text for multi-language audio transcription with speaker diarization and Vercel AI SDK support.

Provides caching patterns for AI embeddings and vectors to reduce computational costs in large-scale AI applications.

Provides reusable templates, scripts, and examples for building RAG pipelines with LangChain to enhance AI application development.

Validates database schemas via SQL syntax checks, constraint verification, and naming convention enforcement for integrity and best practices.

Manages embedding model configurations and calculates associated costs for efficient AI model selection and budgeting.

Intelligently extracts and searches content from large documents, enabling efficient information retrieval without processing entire files.

Provides semantic, hybrid, and reranking strategies to optimize retrieval performance in RAG systems.

Generates realistic synthetic test data including user information, transactions, logs, and API responses while ensuring GDPR compliance and supporting schema-based, time-series, and anomaly data generation.

Generates marketing content, images, and videos using AI models including Google Imagen and Veo.

Provides API templates and examples for Google File Search integration with Gemini RAG, including document chunking and grounding citations.

Provides implementation patterns for RAG systems including document chunking, embedding generation, vector database integration, and semantic search.

Provides production-ready machine learning examples including sentiment analysis, text generation, and financial trade classification with complete training and deployment code.

Configures training monitoring dashboards with TensorBoard and Weights & Biases for real-time metrics tracking, experiment comparison, and hyperparameter visualization in ML workflows.

Provides implementations and benchmarking tools for document chunking strategies (fixed-size, semantic, recursive, sentence-based) to optimize RAG pipelines.

Enables safe, read-only CLI interaction with Codex for non-interactive AI queries.

Provides cost estimation for ML workloads on cloud platforms, including GPU hours, training, and inference pricing for Modal, Lambda Labs, and RunPod.

Provides production-ready database schema patterns for AI applications, including RAG storage with pgvector, chat systems, and multi-tenant models.

Provides a guide for managing JSON data files and generating structured detailed data outputs.

AI-assisted sanitization of Bruker NMR datasets, removing compound identity metadata while preserving spectroscopic data for blind CASE studies.