{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_neuralhub","slug":"neuralhub","name":"Neuralhub","type":"product","url":"https://neuralhub.ai","page_url":"https://unfragile.ai/neuralhub","categories":["model-training"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_neuralhub__cap_0","uri":"capability://collaboration.collaborative.model.development.workspace","name":"collaborative-model-development-workspace","description":"Provides a centralized, shared environment where multiple team members can simultaneously work on AI model projects with real-time collaboration features. Enables distributed research teams to coordinate on model building without context switching between separate tools.","intents":["I want my research team to work on the same model project without using multiple disconnected tools","I need to see what my teammates are doing on our model in real-time","I want to reduce friction when collaborating across time zones on model development"],"best_for":["small to medium research teams","distributed AI development teams","academic research groups"],"limitations":["requires all team members to adopt the platform","collaboration features may lack maturity compared to specialized tools","unclear how it handles concurrent editing conflicts"],"requires":["team account setup","internet connectivity","basic understanding of model development workflows"],"input_types":["project metadata","team member invitations","model configurations"],"output_types":["shared project workspace","activity logs","collaboration notifications"],"categories":["collaboration","research"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_neuralhub__cap_1","uri":"capability://coding.model.building.interface","name":"model-building-interface","description":"Provides a user interface for constructing AI models without requiring extensive manual code writing. Abstracts away boilerplate and configuration complexity to accelerate the model creation process.","intents":["I want to quickly build a model without writing all the infrastructure code","I need a visual or simplified way to define model architecture","I want to reduce the time from idea to working model"],"best_for":["researchers new to model development","teams prioritizing speed over customization","practitioners wanting to avoid low-level implementation details"],"limitations":["likely limited to predefined model architectures","may not support highly custom or novel model designs","unclear what frameworks are supported"],"requires":["basic ML knowledge","understanding of model architecture concepts"],"input_types":["model architecture specifications","framework selection","hyperparameter definitions"],"output_types":["model code","model configuration files","initialized model objects"],"categories":["coding","research","machine-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_neuralhub__cap_2","uri":"capability://machine.learning.hyperparameter.tuning.automation","name":"hyperparameter-tuning-automation","description":"Automatically searches for optimal hyperparameter combinations for AI models using systematic tuning algorithms. Reduces manual experimentation and helps identify better model configurations without exhaustive manual testing.","intents":["I want to find the best hyperparameters for my model without manually trying hundreds of combinations","I need to improve my model's performance but don't know which parameters to adjust","I want to automate the tedious process of hyperparameter optimization"],"best_for":["researchers optimizing model performance","teams with limited computational resources wanting efficient tuning","practitioners unfamiliar with hyperparameter optimization strategies"],"limitations":["tuning quality depends on search space definition","computational cost may be high for large models","unclear what tuning algorithms are available"],"requires":["trained baseline model","defined hyperparameter search space","computational resources","validation dataset"],"input_types":["model","hyperparameter ranges","optimization metric","tuning budget"],"output_types":["optimal hyperparameter set","tuning history","performance comparison charts"],"categories":["machine-learning","research","optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_neuralhub__cap_3","uri":"capability://machine.learning.model.training.orchestration","name":"model-training-orchestration","description":"Manages the end-to-end training process for AI models, including data loading, training loop execution, and progress monitoring. Abstracts infrastructure complexity and provides a unified interface for training across different hardware configurations.","intents":["I want to train my model without managing infrastructure details like GPU allocation","I need to monitor training progress and metrics in real-time","I want to easily switch between local and cloud training without changing code"],"best_for":["researchers wanting simplified training workflows","teams without dedicated MLOps expertise","projects requiring flexible compute resource management"],"limitations":["may not support highly custom training loops","unclear what frameworks are natively supported","distributed training capabilities unclear"],"requires":["prepared dataset","model definition","training configuration","compute resources"],"input_types":["model","training data","training parameters","hardware specifications"],"output_types":["trained model weights","training logs","performance metrics","checkpoints"],"categories":["machine-learning","research","infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_neuralhub__cap_4","uri":"capability://research.experiment.tracking.and.versioning","name":"experiment-tracking-and-versioning","description":"Records and organizes all model training runs, hyperparameter configurations, and results in a centralized repository. Enables researchers to compare experiments, reproduce results, and track model evolution over time.","intents":["I want to keep track of all my model experiments and their results","I need to compare different model versions to see which performed best","I want to reproduce a previous experiment or model state"],"best_for":["research teams running many experiments","practitioners needing reproducibility","teams collaborating on model iteration"],"limitations":["unclear how much experiment history is retained","may have storage limitations","unclear integration with version control systems"],"requires":["active training runs","consistent experiment naming/tagging","metadata about experiments"],"input_types":["training runs","hyperparameters","metrics","model artifacts"],"output_types":["experiment database","comparison reports","version history","reproducibility information"],"categories":["research","productivity","machine-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_neuralhub__cap_5","uri":"capability://research.model.performance.visualization","name":"model-performance-visualization","description":"Generates interactive charts and dashboards displaying training metrics, validation performance, and comparative analysis across experiments. Makes model behavior and performance trends easily interpretable.","intents":["I want to visualize how my model is performing during training","I need to compare performance metrics across different model versions","I want to identify trends and anomalies in my training process"],"best_for":["researchers analyzing model behavior","teams making model selection decisions","practitioners debugging training issues"],"limitations":["visualization options may be limited to predefined charts","unclear what metrics can be visualized","real-time visualization performance unclear"],"requires":["training metrics data","experiment history"],"input_types":["training logs","metrics","experiment metadata"],"output_types":["interactive dashboards","comparison charts","performance reports"],"categories":["research","visualization","analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_neuralhub__cap_6","uri":"capability://machine.learning.model.deployment.preparation","name":"model-deployment-preparation","description":"Prepares trained models for production deployment by handling model serialization, optimization, and packaging. Bridges the gap between research and production environments.","intents":["I want to convert my trained model into a format ready for production use","I need to optimize my model for inference speed and size","I want to package my model with its dependencies for easy deployment"],"best_for":["research teams transitioning models to production","practitioners needing model optimization","teams without dedicated MLOps infrastructure"],"limitations":["unclear what deployment targets are supported","optimization capabilities may be limited","unclear versioning and rollback support"],"requires":["trained model","target deployment environment specifications"],"input_types":["trained model","optimization preferences","deployment target specification"],"output_types":["serialized model","optimized model artifacts","deployment package","inference code"],"categories":["machine-learning","infrastructure","deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_neuralhub__cap_7","uri":"capability://collaboration.integrated.project.management","name":"integrated-project-management","description":"Provides project organization and management features within the platform, allowing teams to structure work, assign tasks, and track progress on model development initiatives.","intents":["I want to organize our model development work in one place without switching to separate project management tools","I need to assign tasks and track who is working on what","I want to see the overall progress of our AI development projects"],"best_for":["small to medium teams","research groups wanting unified workflows","teams avoiding tool fragmentation"],"limitations":["likely simpler than dedicated project management tools","unclear what project management features are included","may lack advanced workflow customization"],"requires":["team setup","project definition"],"input_types":["project metadata","task definitions","team assignments"],"output_types":["project dashboards","task lists","progress reports","team activity logs"],"categories":["collaboration","productivity","research"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"high","permissions":["team account setup","internet connectivity","basic understanding of model development workflows","basic ML knowledge","understanding of model architecture concepts","trained baseline model","defined hyperparameter search space","computational resources","validation dataset","prepared dataset"],"failure_modes":["requires all team members to adopt the platform","collaboration features may lack maturity compared to specialized tools","unclear how it handles concurrent editing conflicts","likely limited to predefined model architectures","may not support highly custom or novel model designs","unclear what frameworks are supported","tuning quality depends on search space definition","computational cost may be high for large models","unclear what tuning algorithms are available","may not support highly custom training loops","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.15000000000000002,"quality":0.47,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:31.858Z","last_scraped_at":"2026-04-05T13:23:42.563Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=neuralhub","compare_url":"https://unfragile.ai/compare?artifact=neuralhub"}},"signature":"3cNu4htmUU/YoFR1ARCzOKylfp37vL788bVUFA6xl1VCOi8r2RV0HvKrOwFVmv1I++wC7NLAg3wh8ymQG/X5Cg==","signedAt":"2026-06-21T22:48:29.674Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/neuralhub","artifact":"https://unfragile.ai/neuralhub","verify":"https://unfragile.ai/api/v1/verify?slug=neuralhub","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}