3. Data, Science & AI

Provides API access to Twitter/X data including user profiles, tweets, trends, and communities for data analysis and research purposes.

Automates retrieval of clean markdown from blocked or error-prone web pages using Jina AI reader service with automatic retries.

Enables parallel execution patterns in LangGraph for fan-out/fan-in, map-reduce, and concurrent agent workflows in AI systems.

Generates text embeddings for semantic search and similarity, supporting model selection and chunking strategies.

Coordinates multiple specialized AI agents using fan-out/fan-in patterns and synthesizes their outputs for complex task execution.

Compresses long conversation history to prevent token overflow while preserving critical information using AI-powered summarization.

Fetches and structures Twitter/X tweet content from links, supporting single or batch extraction with clean data output.

Generates images via Google's Gemini AI model with standard resolution for quota efficiency, and auto-opens on macOS.

Converts Twitter link lists into structured JSON data generated by Grok AI for data organization.

Automates Twitter data collection, filtering, Grok-based transformation, and database storage in a single workflow.

Generates optimized AI prompts using semantic analysis, common sense reasoning, and consistency checks based on an element database.

Automates Twitter data collection, filtering, Grok-based transformation, and database storage in a single workflow.

Provides data visualization components for React using Recharts 3.x, featuring responsive charts, custom tooltips, animations, and accessibility.

Guides selection and implementation of multi-agent AI frameworks including CrewAI, Microsoft Agent Framework, and OpenAI Agents SDK for complex agent system development.

Automates secondary cleaning of Twitter data to extract complete tweet content and generate filterable datasets.

Graph-based memory orchestration using knowledge graphs with optional mem0 cloud for semantic search in AI systems.

AI analyzes voice recordings to uncover behavioral patterns, cognitive biases, and strategic blind spots, delivering actionable insights and building a personal work pattern knowledge base.

Curates high-quality datasets for AI model evaluation, defining quality criteria, curation workflows, and multi-agent analysis patterns for test data.

Enables dynamic routing, branching workflows, and retry loops in LangGraph-based AI agent applications.

Enables constructing context from retrieved documents, adding citations, and implementing hybrid search in RAG pipelines for grounded LLM responses.

Evaluates LLM outputs for quality, supporting LLM-as-judge patterns and automated evaluation pipelines.

Provides integration patterns for real-time voice processing, speech-to-text, and text-to-speech using leading AI models and APIs.

Implements a supervisor-worker pattern in LangGraph for central coordination of specialized agents, supporting round-robin and priority-based dispatch strategies.

Comprehensive suite for designing, optimizing, and managing AI prompts using Chain-of-Thought, few-shot learning, and versioning to improve model accuracy.