3. Data, Science & AI

Adds curated documents to a golden dataset with multi-agent validation for testing and model training purposes.

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

Manages golden datasets for AI/ML systems, ensuring integrity through backup, restore, and validation to prevent data loss during evaluation.

Builds RAG pipelines for generating grounded LLM responses by constructing context from retrieved documents and adding citations.

Automates synchronization of session context, decisions, and patterns to Mem0 for persistent AI agent memory across sessions.

Enables dynamic workflow branching, retry loops, and state-based routing in LangGraph agent applications through conditional edge patterns.

Enables autonomous LLM reasoning through ReAct agent patterns and multi-step task execution workflows.

Customizes LLMs for specific domains using efficient fine-tuning techniques (LoRA, QLoRA, DPO) and synthetic data generation.

Optimizes LLM costs by caching prompts for Claude and OpenAI, reducing token usage for repeated prefixes and system prompts.

Provides advanced LLM guardrails for hallucination prevention, fact-checking, toxicity detection, and red-teaming using NeMo, Guardrails AI, and OpenAI.

Coordinates multiple specialized AI agents using fan-out/fan-in patterns to synthesize parallel outputs for complex problem-solving.

Specializes in database management for PostgreSQL, MongoDB, Redis, Oracle, and advanced data patterns in modern application development.

Specializes in Firebase Firestore for mobile-first applications, including real-time data sync, offline caching, and security rule configuration.

Specializes in optimizing data formats (JSON, YAML, TOON) for LLM transmission, high-performance serialization, and schema validation in modern applications.

Searches indexed Claude/Codex history with semantic, lexical, and hybrid methods, outputting RAG-optimized JSON for AI systems.

Provides semantic, lexical, and hybrid search over indexed Claude/Codex conversation history with RAG-ready JSON output.

Performs comprehensive diagnostic analysis on time series data, identifying stationarity, seasonality, trend, and forecastability to guide data transformation and modeling.

Enables AI agent development and integration with the Lithent library for building agent-based applications.

Manages an AI agent's internal memory for saving, recalling, and organizing user interactions and findings.

AI-powered analysis tool for images, screenshots, text, and conditions using models like Gemini. Handles description, comparison, generation, and evaluation tasks.

Specializes in R 4.4+ for data analysis pipelines, visualizations, and Shiny application development.

Manages enterprise context and sessions with token budget optimization for AI applications, ensuring state persistence.

Breaks down complex reasoning tasks and assigns explicit confidence scores to each step for enhanced AI model transparency.

Curates a list of preferred stars for celestial navigation and astronomical reference purposes.