Can I use pre-trained models instead of training from scratch?

Yes, the Template Marketplace allows you to browse, download, and deploy pre-trained models for tasks such as sentiment analysis, image classification, and regression.

What neural network architectures are supported?

Flow Nexus Neural supports feedforward, LSTM, GAN, Autoencoder, and Transformer architectures with fully customizable layer configurations.

How does distributed training work with this skill?

You can initialize clusters with multiple E2B sandbox nodes, assigning specific roles like parameter servers, worker nodes, and aggregators to handle parallel processing.

Does this skill support data privacy during training?

Yes, it features a federated learning mode where data remains on local nodes and only model gradients are synchronized, ensuring sensitive information is never centralized.

Flow Nexus Neural

Name: Flow Nexus Neural
Author: whrit

bywhrit

Ciencia de Datos y ML

Train, deploy, and manage distributed neural networks across sandboxed E2B environments using multiple architectures and federated learning.

Acerca de

Flow Nexus Neural is a comprehensive suite for machine learning orchestration within Claude Code, enabling developers to architect and scale complex neural networks directly within distributed E2B sandboxes. The skill supports a wide range of architectures—including Transformers, LSTMs, and GANs—and offers advanced features like federated learning, decentralized autonomous agents, and cluster topology management. Whether you are utilizing the pre-built template marketplace for rapid deployment or configuring large-scale clusters for heavy training loads, this skill provides the infrastructure needed for professional AI model development, performance benchmarking, and real-time inference.

Características Principales

Support for Transformer, LSTM, GAN, Autoencoder, and Feedforward architectures
0 GitHub stars
Integrated marketplace for deploying and publishing pre-trained model templates
Distributed training across E2B sandbox clusters with mesh and ring topologies
Comprehensive performance benchmarking for latency, throughput, and accuracy
Federated learning capabilities for privacy-preserving, decentralized model training

Casos de Uso

Scaling large Transformer models using distributed worker nodes and parameter servers
Rapidly prototyping and benchmarking custom neural architectures for time-series or vision tasks
Implementing privacy-focused federated learning workflows for sensitive datasets

Acerca de

Características Principales

Support for Transformer, LSTM, GAN, Autoencoder, and Feedforward architectures
0 GitHub stars
Integrated marketplace for deploying and publishing pre-trained model templates
Distributed training across E2B sandbox clusters with mesh and ring topologies
Comprehensive performance benchmarking for latency, throughput, and accuracy
Federated learning capabilities for privacy-preserving, decentralized model training

Casos de Uso

Scaling large Transformer models using distributed worker nodes and parameter servers
Rapidly prototyping and benchmarking custom neural architectures for time-series or vision tasks
Implementing privacy-focused federated learning workflows for sensitive datasets