3. Data, Science & AI

Provides fundamental tools for astronomical data processing with Astropy, including FITS I/O, coordinate transformations, unit handling, and time operations for research applications.

Coordinates collaboration between multiple AI agents in Agent Hive, managing dependencies, preventing conflicts, and enabling workflow synchronization via a coordination server.

Coordinates multiple AI agents in Agent Hive, managing dependencies, preventing conflicts, and enabling collaborative workflows through a coordination server.

Validates CDC and incremental sync implementations by verifying correct offset tracking and record filtering in data connectors.

Refines ambiguous user intent into a structured, stable AI prompt through iterative clarification until user confirmation.

Architects AI agent workflows using Meta methodologies, translating business problems into structured solutions with tool integration and multi-layer planning.

Provides reference documentation for the 4D Context Canvas framework used in AI context engineering and system design.

Manages post-launch AI feature calibration through error documentation, performance review, and promotion decisions based on continuous evaluation.

Optimizes AI prompts for production use, providing critique, templates, and strategies to enhance model performance and instruction clarity.

Generates production-ready KQL queries for Microsoft Sentinel, Defender XDR, and Azure Data Explorer using schema validation and documentation.

Manages DNA Discrete Diffusion (D3) model experiments on SLURM clusters, including job submission, WandB tracking, and result handling for genomic datasets.

Compares multiple AI model evaluation attempts (brazil-bench) across dimensions, generating a ranked leaderboard and detailed summary with automatic pruning of lower entries.

Extracts timestamped transcripts from YouTube videos to enable translation, summarization, and content creation workflows.

Enables Bayesian statistical modeling with PyMC v5+, supporting MCMC inference, model diagnostics, and comparison for probabilistic tasks.

Provides strategies for managing LLM context windows to maintain coherence in AI agents during long conversations and multi-step tasks.

Provides tools for loading, using, and fine-tuning pretrained models from Hugging Face Transformers for text, vision, audio, and multimodal tasks.

Enables building, training, and optimizing deep learning models using PyTorch, including data pipelines and distributed training.

Enables local deployment, fine-tuning, and serving of large language models on Apple Silicon using MLX, including Hugging Face model conversion.

Enables memory-efficient fine-tuning of large language models (7B+) on consumer GPUs via 4-bit quantization and LoRA adapters.

Enables parameter-efficient fine-tuning of large language models using Low-Rank Adaptation (LoRA) with minimal GPU memory requirements.

Explains Reinforcement Learning from Human Feedback (RLHF) techniques for aligning language models with human preferences, covering preference data and reward modeling.

Optimizes LLM interactions through strategic prompt design, enhancing output quality and reliability for complex tasks.

Provides architectural patterns for building LLM-driven AI agents with tool integration, multi-step reasoning, and autonomous task orchestration.

Provides read-only access to the Gertrude PostgreSQL database for querying, schema analysis, data analysis, and debugging database issues.