3. Data, Science & AI

Enables real-time data synchronization by capturing database changes and triggering event-driven updates.

Enables defining data products and establishing governance frameworks for scalable, governed data ecosystems.

Validates and scores player guesses against drawing cards using fuzzy matching and scoring logic, handling edge cases in game applications.

Provides standardized conventions for adding new columns in database schemas to ensure consistency and maintainability.

Generates Pictionary drawing cards using AI (Groq) with fallback to hardcoded libraries for category-based selection and graceful degradation.

Builds structured database queries using an Options API for efficient data retrieval and manipulation.

Creates dimensional models and fact tables to optimize data warehouse structures for analytical reporting and business intelligence.

Interface for managing Temporal Database and AI system (ReDB + GGUF embeddings) for efficient data handling and model operations.

Routes user tasks to optimal AI agents and tools by analyzing minimal context for efficient processing.

Retrieves relevant context from previous user interactions to enhance conversational continuity and personalization.

Provides patterns for building AI applications with Vercel AI SDK, including streaming LLM responses, tool calling, and structured outputs.

Manages the agent's internal memory for saving, recalling, and organizing user-specific information and conversation context.

Generates AntV-based infographics in Obsidian using JSON or DSL syntax for process flows, timelines, and data charts within markdown documents.

Enables defining and training Multi-Layer Perceptrons (MLPs) using Flax NNX for neural network development.

Guide for building AI applications with Vercel AI SDK v6, covering LLM features including tool calling, agents, and structured output across frameworks.

Creates single-file interactive HTML visualizations from documents or conversations.

Enables natural language search to discover skills from multiple sources, including GitHub repositories, using AI-powered retrieval.

Generates optimized SQL queries from natural language requests and database schemas.

Enables semantic and text-based search of previous Claude Code conversations to retrieve facts, patterns, decisions, and context.

Provides CLI commands for querying Langfuse traces, managing prompts/datasets, and analyzing LLM observability metrics including token usage and model performance.

Designs evaluation frameworks for LLMs, including scoring rubrics, golden datasets, and LLM-as-judge methodologies to measure model quality and performance.

Optimizes PostgreSQL performance on Supabase by diagnosing slow queries, adding indexes, and fixing RLS policies.

Framework for constructing complex AI agent workflows including ReAct agents, multi-agent systems, and state machines with human-in-the-loop support.

Extracts and processes web content via concurrent scraping, deduplication, and content filtering for data collection in research, documentation, and archival.