3. Data, Science & AI

Extracts and processes Langfuse Cloud data for debugging, telemetry analysis, and generating ALCOA+ compliant reports with time-range filtering.

Delivers accurate, peer-reviewed content and expertise in Physical AI and Humanoid Robotics for technical audiences.

Analyzes Polymarket trader data to identify and track profitable traders for copy trading and discovery features.

Connects to Beam AI workspace for agent management, validating configurations and routing user requests to appropriate AI operations based on keywords.

Provides analysis, conversion, and querying capabilities for DBF files, with specialized support for Thai legacy accounting data and Parquet format conversion.

Framework for building, testing, and deploying algorithmic trading strategies with data-driven backtesting for prediction markets.

Generates images and videos using Google's GenAI models (Gemini, Veo, Imagen) for specific user requests.

Enables seamless connection to Airtable bases for data querying, adding records, and schema management through natural language commands.

Provides data handling utilities for SikhAid project, including Firebase/Firestore integration, Svelte stores, and CRUD operations.

Systematically inventories datasets, assesses data quality, and identifies instrumentation gaps for data-driven initiatives.

Analyzes game configuration database schemas and relationships via HTTP API and CFG syntax for efficient data querying and management.

Imports and validates auto insurance business data using CSV/JSON formats with priority management for streamlined data processing.

Enables intelligent data validation through the Minion framework to ensure data integrity and correctness in applications.

Comprehensive guide to phylogenetic tree construction methods including distance-based, maximum likelihood, and Bayesian inference, with coverage of alignment, tree formats, and evaluation metrics.

Builds frameworks to evaluate AI agent performance, validate context engineering, and measure iterative improvements.

Personalizes the Nexus system by configuring user roles, goals, and preferences for tailored interactions.

Provides semantic search for conceptual code understanding, enabling exploration of concepts, cross-language patterns, and business logic in codebases.

Analyzes performance metrics to identify trends, detect anomalies, and provide actionable optimization recommendations.

Comprehensive guide for generating publication-quality phylogenetic tree visualizations using Python (matplotlib) and R (ggtree), covering layouts, annotations, and scientific best practices.

Configures ECharts for bar, line, and combination charts to visualize data effectively.

Provides ML integration patterns for bioinformatics workflows including sequence classification, ensemble methods, and generative tree synthesis using sklearn, PyTorch, and GNNs.

Compiles recurring quality reports by analyzing defect trends, automation health metrics, and providing actionable recommendations for software quality improvement.

Generates synthetic datasets via statistical sampling and LLM for testing, training, and simulation. Supports CSV, JSON, JSONL, Parquet outputs.

Provides guidelines for detecting and predicting ribosomal RNA sequences using sequence patterns, HMM models, and BLAST-based methods in prokaryotic and eukaryotic organisms.