3. Data, Science & AI

Dispatches concurrent AI agent workflows to solve independent problems, enabling parallel task execution.

Enables advanced text analysis, fuzzy matching, and mathematical semantic operations using Julia's scientific computing capabilities for data science workflows.

Accelerates billion-scale vector similarity search using FAISS with CUDA GPU acceleration for AI applications.

Enhances AI memory systems with hybrid search fusion, combining keyword and semantic retrieval for improved accuracy in RAG workflows.

Builds custom AI agents with SpoonReactMCP, enabling tool chain configuration, system prompt design, and concurrent agent pattern implementation.

Builds structured AI agents with Anthropic SDK, defining tools and orchestration for AI-powered automation tasks.

Generates Supabase database queries, migrations, and RLS policies for efficient database management and operations.

Performs mathematical optimization and chaos analysis using Julia's Optim.jl package for scientific computing applications.

Analyzes blockchain transaction data from Etherscan and Dune Analytics to extract insights and metrics.

Integrates Context7 for document ingestion, semantic search, and role/tenant-scoped context retrieval to ground AI responses in knowledge bases.

Aggregates orderflow, scanner, and AI-generated signals to produce unified trade recommendations for algorithmic trading strategies.

Manages the Canonical Cycle data flow (Raw -> Filtered -> Canonical -> Artifacts) for data processing pipelines.

Uses semantic search to accurately locate business accounts by matching company names like 'Sunny Days Childcare' or 'ABC Corp'.

Automates updating account descriptions in Qdrant to ensure search results remain accurate after state changes.

Optimizes prompts and configurations for Claude Code skills to improve consistency, tool utilization, and modernization.

Expert guidance for integrating Power Query with Frappe ERP, including M code development, authentication, and handling long-running reports for Power BI and Excel data pipelines.

Analyzes market data from major NFT platforms including OpenSea, Blur, and Magic Eden to provide trend insights and performance metrics.

Generates optimized SIMD kernels for Mojo, accelerating data-parallel computations in AI and scientific computing applications.

Analyzes and refines AI prompts and agent configurations using established best practices to improve model performance and reliability.

Trains and fine-tunes language models using Hugging Face Jobs with TRL, covering SFT, DPO, GRPO, and reward modeling, plus GGUF conversion for deployment.

Handles Alembic database migrations: initializing, generating, applying, and rolling back schema changes in SQLAlchemy projects.

Manages Hugging Face datasets with repo initialization, config definition, streaming updates, and SQL-based transformations for AI workflows.

Adjusts physics parameters for realistic balloon flight simulation, including buoyancy, wind, and thermal dynamics.

Enables efficient machine learning model inference using the JAX framework for high-performance numerical computations.