3. Data, Science & AI

Handles annotated data matrices for single-cell genomics, supporting AnnData objects, h5ad files, and integration with scanpy/scverse tools.

Queries ClinicalTrials.gov API v2 to search and retrieve clinical trial data by condition, drug, location, status, or phase for research purposes.

Accesses RCSB PDB database to search, download, and retrieve 3D protein/nucleic acid structures and metadata for structural biology and drug discovery.

Framework for organizing PyTorch code into LightningModules, enabling scalable neural network training with distributed computing and integrated logging.

Enables deployment of pre-trained transformer models for NLP, computer vision, and multimodal tasks including text generation and image classification.

Enables efficient fine-tuning of large language models with limited resources using parameter-efficient methods like LoRA and QLoRA, integrated with HuggingFace Transformers.

Provides an overview of the experimental MVCC feature, covering snapshot isolation, versioning, and limitations in database systems.

Explains transaction management mechanics in TursoDB, including WAL, checkpointing, concurrency rules, and recovery processes.

Describes SQLite storage internals including B-trees, pages, cells, overflow, and freelist as used in tursodb.

Retrieves and analyzes data from the AgentScope library to support AI-driven decision-making processes.

Designs and builds AI agents for business, research, and creative domains, covering architecture, autonomous behavior, and multi-step orchestration.

Trains and deploys neural networks in distributed E2B sandboxes using Flow Nexus.

Enables semantic vector search using AgentDB for document retrieval, similarity matching, and context-aware queries in RAG systems.

Enables AI-driven task dispatch and performance monitoring through Worker-Agent integration in distributed environments.

Provides quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination capabilities.

Enables adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement in self-learning agents.

Optimizes AI model performance with significant speedups in Flash Attention, search, and memory usage through comprehensive benchmarking.

Enables advanced vector search, distributed coordination, and multi-database management for AI applications and multi-agent systems.

Advanced collective intelligence system enabling queen-led multi-agent coordination through consensus mechanisms and persistent memory.

Enables adaptive learning for self-learning agents using AgentDB's vector database, featuring trajectory tracking and memory distillation for optimized decision-making.

Optimizes AgentDB performance through quantization, HNSW indexing, caching, and batch operations for reduced memory usage and faster search at scale.

Orchestrates multi-agent swarms for parallel task execution, dynamic topology, and intelligent coordination in distributed AI systems.

Creates and trains AI learning plugins using AgentDB's reinforcement learning algorithms for self-learning agents and behavior optimization.

Enables streaming JSON data transformation and sequential workflow orchestration for multi-agent data pipelines.