flow-nexus-neural

Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus

7.38x

Quality

37%

Does it follow best practices?

Impact

96%

7.38x

Average score across 3 eval scenarios

Securityby

Advisory

Suggest reviewing before use

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Evaluation results

96%

84%

IoT Demand Forecasting Model

Single-node architecture configuration and training workflow

Criteria

Without context

With context

LSTM architecture type

28%

100%

LSTM layer types

100%

Correct training config structure

100%

Training params present

62%

100%

Appropriate tier for PoC

100%

Divergent config block

100%

Divergent factor value

100%

Correct train tool name

80%

Training status monitoring

83%

Dropout regularization layers

10%

100%

72%

Large-Scale Vision Model Training Orchestration

Distributed training cluster lifecycle and node deployment

Criteria

Without context

With context

Cluster init tool

100%

wasmOptimization enabled

100%

Topology specified

100%

Consensus mechanism

100%

Parameter server node

100%

Worker node deployed

100%

Aggregator node deployed

100%

Autonomy values set

100%

Cluster connect called

100%

Distributed train tool

100%

Cluster status monitoring

100%

Cluster terminate called

100%

92%

Cross-Hospital Diagnostic Model with Privacy Constraints

Federated learning, production validation, and model publishing

Criteria

Without context

With context

Federated flag enabled

100%

Aggregation rounds set

100%

Min nodes per round

100%

Performance benchmark

100%

Validation workflow

100%

Model published

100%

Distributed inference aggregation

Cluster init for federated

100%

Cluster terminated

100%

Healthcare category used

100%

Repository: ruvnet/agentic-flow
Commit: 8db2712

Evaluated: 26 days ago
Agent: Claude Code
Model: Claude Sonnet 4.6

Table of Contents

IoT Demand Forecasting Model Large-Scale Vision Model Training Orchestration Cross-Hospital Diagnostic Model with Privacy Constraints

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