3. Data, Science & AI

Deletes specified PDF documents from Qdrant vector database collections, enabling document version control and cleanup in AI/ML applications.

Provides tools to instrument and track evaluation metrics, quality scores, and feedback loops for data and AI model assessment.

Performs square root, cube root, and nth root calculations for user queries containing terms like 'sqrt' or 'root of'.

Analyzes SQL query execution plans and provides optimization recommendations including index usage, N+1 detection, and join tuning for improved database performance.

Provides core setup and optimization techniques for fast fine-tuning of large language models using Unsloth's FastLanguageModel and gradient checkpointing.

Provides INT8 quantization techniques for model size reduction and inference acceleration in PyTorch, including PTQ and QAT.

Designs and operates multi-agent orchestration patterns for LLM systems, including ReAct loops and tool routing, to build and debug agent workflows.

PyTorch library for audio feature extraction, waveform manipulation, and GPU-accelerated data augmentation techniques.

Provides NumPy-based sorting and searching utilities for numerical data, including partitioning, binary search, and multi-key sorting.

Provides fundamental NumPy operations for array creation, shape manipulation, and memory-efficient data views with emphasis on dtypes and alignment.

Optimizes PyTorch models using torch.compile (TorchDynamo/Inductor) to reduce execution time and debug compilation issues.

Guides selection and configuration of AI model architectures including Llama 4, DeepSeek-R1, and Qwen3 for optimal deployment.

Enables extreme VRAM efficiency for large language models via 4-bit quantization using Unsloth and BitsAndBytes, optimized for QLoRA.

Deploys fine-tuned AI models for production inference using optimized frameworks like vLLM and SGLang.

Provides advanced NumPy indexing techniques including slicing, fancy indexing, and boolean masks with memory implications of views vs. copies.

Enables one-step preference alignment for AI models using Odds Ratio Preference Optimization (ORPO).

Modern API for polynomial fitting, root finding, and orthogonal series (Chebyshev, Legendre) operations in scientific computing.

Enforces strict JSON schema compliance for LLM responses using Pydantic models and JSON mode.

Provides evaluation patterns for RAG and LLMs, including faithfulness metrics, synthetic dataset generation, and LLM-as-a-judge techniques.

Generates database diagrams including ERDs and schema visualizations using Mermaid.js for clear representation of database relationships.

Performs set-theoretic operations on NumPy arrays including unique elements, membership testing, and array intersections.

High-level PyTorch framework simplifying model training with abstractions for boilerplate, multi-GPU support, and reproducibility.

Enables AI grounding through web search API integration, supporting query operators and citation metadata mapping.

Enables distributed training for PyTorch models using DDP and FSDP, including multi-node setup and checkpointing via torchrun.