{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_tensorplex","slug":"tensorplex","name":"Tensorplex","type":"product","url":"https://www.tensorplex.ai","page_url":"https://unfragile.ai/tensorplex","categories":["automation"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"tool_tensorplex__cap_0","uri":"capability://automation.workflow.decentralized.gpu.compute.resource.allocation","name":"decentralized gpu compute resource allocation","description":"Tensorplex operates a peer-to-peer GPU network where distributed node operators contribute compute resources (GPUs, TPUs) that are pooled and allocated to users via a smart contract-based resource registry. The platform uses a reputation and stake-weighted selection mechanism to route workloads to reliable nodes, with cryptographic proof-of-work validation ensuring task completion. This differs from centralized cloud providers by eliminating single points of failure and allowing direct node-to-user resource matching without intermediary infrastructure.","intents":["I want to run AI training jobs on distributed GPUs without relying on AWS, GCP, or Azure infrastructure","I need to reduce compute costs by accessing a peer-to-peer GPU market with transparent pricing","I want to ensure data sovereignty by keeping training data on decentralized nodes rather than centralized data centers"],"best_for":["AI researchers and ML engineers prioritizing decentralization and cost reduction over guaranteed uptime","Blockchain projects and crypto organizations with existing Web3 infrastructure expertise","Organizations with strict data residency requirements or privacy mandates"],"limitations":["Network size and available GPU capacity are significantly smaller than hyperscalers, causing potential resource contention during peak demand","No guaranteed SLA or uptime commitments — node operators can go offline unpredictably, interrupting long-running jobs","Latency overhead from peer-to-peer coordination and consensus mechanisms adds 5-15% computational overhead vs direct cloud access","Limited geographic distribution of nodes compared to AWS/GCP, potentially increasing data transfer latency"],"requires":["Cryptocurrency wallet with sufficient tokens to stake or pay for compute","Understanding of smart contracts and blockchain transaction mechanics","Network connectivity to Tensorplex node infrastructure (may require VPN or specific firewall rules)","Containerized workload format (Docker) for compatibility with heterogeneous node environments"],"input_types":["containerized ML models (Docker images)","training datasets (uploaded or referenced via IPFS/decentralized storage)","job specifications (JSON/YAML configuration with compute requirements)"],"output_types":["trained model weights and checkpoints","inference results and predictions","job execution logs and performance metrics","transaction receipts and cost breakdowns on-chain"],"categories":["automation-workflow","decentralized-infrastructure"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tensorplex__cap_1","uri":"capability://automation.workflow.liquid.staking.for.network.security.and.passive.income","name":"liquid staking for network security and passive income","description":"Tensorplex implements a liquid staking protocol where token holders deposit native tokens into a smart contract to secure the network and earn staking rewards, while receiving liquid staking tokens (LSTs) that represent their stake and can be traded or used in DeFi protocols. The staking mechanism uses a delegated proof-of-stake (DPoS) model where stakers choose validator nodes to secure network consensus, with slashing penalties for malicious behavior. This architecture decouples capital lockup from earning potential, allowing stakers to maintain liquidity while participating in network security.","intents":["I want to earn passive income on my cryptocurrency holdings by staking tokens to secure the Tensorplex network","I need liquidity while staking — I want to trade or use my staked tokens in other DeFi applications without unstaking","I want to participate in network governance and validator selection without running infrastructure myself"],"best_for":["Cryptocurrency investors and token holders seeking yield on idle assets","DeFi-native developers building applications that integrate liquid staking tokens","Organizations with large token holdings looking to generate revenue while supporting network security"],"limitations":["Staking rewards are variable and depend on network participation rate and inflation schedule — no guaranteed APY","Slashing risk exists if delegated validators behave maliciously, resulting in partial loss of staked capital","Liquid staking tokens may trade at a discount to underlying token value due to redemption risk and liquidity constraints","Unstaking period typically requires 7-28 day unbonding window before tokens become liquid again"],"requires":["Cryptocurrency wallet with native Tensorplex tokens (minimum stake amount, typically 1-100 tokens)","Understanding of DeFi mechanics and smart contract interactions","Gas fees for staking transactions (typically $5-50 depending on network congestion)","Access to a Web3 wallet interface (MetaMask, Ledger, Trezor, or similar)"],"input_types":["native Tensorplex tokens","validator node selection (address or identifier)","staking duration and amount parameters"],"output_types":["liquid staking token (LST) balance","staking reward accrual (tracked on-chain)","validator delegation confirmation","unstaking request with unbonding timeline"],"categories":["automation-workflow","blockchain-finance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tensorplex__cap_2","uri":"capability://safety.moderation.web3.native.identity.and.access.control.for.compute.resources","name":"web3-native identity and access control for compute resources","description":"Tensorplex uses blockchain-based identity (wallet addresses, ENS names, or decentralized identifiers) and smart contract-based access control lists (ACLs) to manage permissions for compute resource access, job submission, and result retrieval. Users authenticate via cryptographic wallet signatures rather than API keys, and permissions are encoded as on-chain smart contracts that can be programmatically updated or delegated. This approach enables fine-grained, transparent, and composable access control without relying on centralized identity providers.","intents":["I want to grant compute access to other developers or organizations using blockchain addresses without managing API keys","I need to revoke access to my compute resources instantly by updating on-chain permissions","I want to create time-limited or usage-limited access grants that expire automatically"],"best_for":["Decentralized autonomous organizations (DAOs) and multi-party collaborations requiring transparent access governance","Privacy-conscious developers who want to avoid centralized identity providers","Teams building Web3-native applications with existing blockchain infrastructure"],"limitations":["Wallet-based authentication requires users to manage private keys securely — no password recovery or account recovery mechanisms","On-chain ACL updates incur transaction costs and confirmation delays (typically 12-60 seconds), making real-time access revocation slower than API key invalidation","Limited support for traditional enterprise identity systems (LDAP, SAML, OAuth) — requires custom bridges for legacy integrations","Wallet address enumeration and transaction history are publicly visible on-chain, potentially leaking usage patterns"],"requires":["Cryptocurrency wallet with signing capability (MetaMask, Ledger, Trezor, or hardware wallet)","Understanding of blockchain transactions and gas fees","ENS domain or wallet address for identity","Familiarity with smart contract ABIs and function calls"],"input_types":["wallet address or ENS name","permission scope (read, write, execute, delete)","access duration or usage limits","cryptographic signature from wallet"],"output_types":["access token or session proof","on-chain transaction receipt confirming permission grant","access revocation confirmation","audit log of permission changes"],"categories":["safety-moderation","blockchain-identity"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tensorplex__cap_3","uri":"capability://automation.workflow.cross.chain.token.payment.and.settlement.for.compute.services","name":"cross-chain token payment and settlement for compute services","description":"Tensorplex integrates multi-chain payment processing where users can pay for compute resources using native tokens, stablecoins, or wrapped assets across multiple blockchains (Ethereum, Polygon, Arbitrum, etc.). The platform uses atomic swap mechanisms or bridge protocols to convert payments into the native Tensorplex token for node operator rewards, with settlement occurring on-chain within minutes. This architecture enables global payments without traditional banking infrastructure while maintaining transparent, auditable transaction records.","intents":["I want to pay for GPU compute using stablecoins or tokens I already hold, without converting to a specific blockchain","I need transparent, on-chain billing and settlement that I can audit and verify programmatically","I want to use multiple payment methods across different chains without managing separate accounts"],"best_for":["International teams and organizations avoiding traditional payment processors and currency conversion fees","Cryptocurrency-native projects with existing token holdings across multiple chains","Developers building automated payment systems that require on-chain settlement transparency"],"limitations":["Cross-chain bridge operations introduce 5-30 minute settlement delays and bridge slippage (typically 0.1-1% of transaction value)","Stablecoin volatility and depegging risk — if payment stablecoin loses peg, compute costs become unpredictable","Gas fees for multi-chain transactions can exceed $10-100 depending on network congestion, making small payments uneconomical","Limited payment method support — only cryptocurrency and wrapped assets, no credit cards or traditional payment rails"],"requires":["Cryptocurrency wallet with sufficient balance in supported tokens or stablecoins","Understanding of blockchain networks and bridge mechanics","Gas fees in native blockchain currency (ETH, MATIC, ARB, etc.)","Access to decentralized exchanges or bridges for token conversion if needed"],"input_types":["payment token address and amount","source blockchain network","destination wallet address for settlement","compute service specification and duration"],"output_types":["payment transaction hash and confirmation","on-chain invoice or receipt","compute resource allocation confirmation","settlement proof and token transfer confirmation"],"categories":["automation-workflow","blockchain-finance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tensorplex__cap_4","uri":"capability://automation.workflow.containerized.ml.workload.orchestration.across.heterogeneous.gpu.nodes","name":"containerized ml workload orchestration across heterogeneous gpu nodes","description":"Tensorplex provides a container orchestration layer that accepts Docker images containing ML models and training code, then distributes and executes these containers across heterogeneous GPU nodes (NVIDIA, AMD, TPU) with automatic resource matching and scheduling. The platform uses a constraint-based scheduler that matches workload requirements (GPU type, memory, compute capability) to available nodes, handles container image distribution via IPFS or decentralized storage, and manages job lifecycle (queuing, execution, monitoring, result collection). This enables developers to package ML workloads once and run them across a distributed network without manual node selection.","intents":["I want to submit a containerized training job and have it automatically scheduled on available GPUs without managing individual nodes","I need to run the same ML workload across multiple GPU types (NVIDIA A100, H100, AMD MI300) without rewriting code","I want to monitor job progress, collect results, and handle failures across distributed nodes transparently"],"best_for":["ML engineers and researchers familiar with containerization (Docker) who want to leverage distributed compute without infrastructure management","Teams running batch training jobs or hyperparameter sweeps that can tolerate some node failures","Organizations with heterogeneous GPU hardware seeking unified job submission interface"],"limitations":["Heterogeneous GPU support requires workload compatibility testing — not all CUDA code runs identically on AMD or TPU hardware","Container image distribution via IPFS adds 2-10 minute overhead for large images (>10GB), delaying job start time","No built-in distributed training orchestration (Horovod, DeepSpeed) — users must implement multi-node synchronization themselves","Limited support for stateful workloads or persistent storage — jobs are ephemeral and results must be explicitly saved to external storage","Scheduling latency and node heterogeneity can cause unpredictable job completion times (±20-40% variance)"],"requires":["Docker or container runtime knowledge","ML framework compatible with target GPU types (PyTorch, TensorFlow, JAX)","Container image with all dependencies pre-installed","Job specification format (JSON/YAML) with resource requirements and entry point"],"input_types":["Docker image (URI or local image)","job configuration (CPU, GPU type, memory, duration)","training data (uploaded or referenced via IPFS/S3)","environment variables and hyperparameters"],"output_types":["job ID and status tracking","execution logs and metrics (real-time streaming)","trained model artifacts and checkpoints","cost breakdown and resource utilization metrics"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tensorplex__cap_5","uri":"capability://data.processing.analysis.real.time.job.monitoring.and.resource.utilization.tracking","name":"real-time job monitoring and resource utilization tracking","description":"Tensorplex provides a monitoring dashboard and API that streams real-time metrics (GPU utilization, memory usage, network I/O, temperature) from executing nodes, with on-chain logging of resource consumption for billing and audit purposes. The platform uses a pull-based monitoring architecture where nodes periodically report metrics to a decentralized oracle network, which aggregates and publishes results on-chain. This enables transparent, verifiable resource tracking without relying on centralized monitoring infrastructure.","intents":["I want to see real-time GPU utilization and memory usage while my training job is running","I need to verify that I'm being charged fairly based on actual resource consumption, with on-chain proof","I want to detect and respond to performance bottlenecks or node failures in real-time"],"best_for":["Developers running long-running training jobs who need visibility into resource consumption","Organizations with strict cost accountability requirements and audit trails","Teams optimizing workload performance and resource efficiency"],"limitations":["Monitoring data is aggregated and published on-chain with 30-60 second latency, not true real-time (vs 1-5 second latency in centralized systems)","Oracle network consensus adds computational overhead and potential data staleness if nodes disagree on metrics","Limited historical data retention — on-chain storage is expensive, so detailed metrics are typically retained for 7-30 days only","Monitoring granularity is coarser than centralized systems — per-GPU metrics available, but not per-process or per-kernel-level profiling"],"requires":["Access to Tensorplex monitoring API or dashboard","Job ID and authentication credentials","Understanding of GPU metrics and performance interpretation"],"input_types":["job ID","time range for historical metrics","metric type filters (GPU, memory, network, temperature)"],"output_types":["real-time metric streams (GPU %, memory MB, network Mbps)","aggregated statistics (average, peak, min utilization)","on-chain billing records with resource consumption proof","performance alerts and anomaly detection"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tensorplex__cap_6","uri":"capability://memory.knowledge.decentralized.model.registry.and.versioning.with.ipfs.integration","name":"decentralized model registry and versioning with ipfs integration","description":"Tensorplex provides a decentralized model registry where users can upload, version, and share ML models using IPFS content addressing, with metadata stored on-chain (model name, version, hash, owner, access permissions). The registry uses content-addressed storage where model files are identified by cryptographic hash, enabling deduplication and verifiable integrity. Users can publish models publicly or restrict access via smart contract permissions, and the registry integrates with the job orchestration layer to enable one-click model deployment.","intents":["I want to store and version my trained models in a decentralized registry without relying on Hugging Face or GitHub","I need to share models with specific collaborators while maintaining ownership and access control","I want to ensure model integrity and prevent tampering by using cryptographic content addressing"],"best_for":["Researchers and teams prioritizing data sovereignty and decentralization over convenience","Organizations with IP concerns who want to avoid centralized model repositories","Developers building decentralized ML applications with on-chain model governance"],"limitations":["IPFS storage is not guaranteed persistent — models may become unavailable if no nodes are pinning them, requiring explicit pinning service subscription","Model discovery and search are limited compared to centralized registries (Hugging Face) — no full-text search or recommendation system","Large model uploads (>100GB) are slow and unreliable over IPFS, typically requiring 1-24 hours depending on network conditions","Metadata queries require on-chain lookups, adding latency (12-60 seconds) compared to centralized database queries (milliseconds)","No built-in versioning or rollback mechanism — users must manually manage version history"],"requires":["IPFS node or access to IPFS gateway (Pinata, Infura, or self-hosted)","Model files in standard formats (PyTorch .pt, TensorFlow .pb, ONNX)","Cryptocurrency wallet for on-chain metadata transactions","Understanding of content addressing and IPFS mechanics"],"input_types":["model file (PyTorch, TensorFlow, ONNX, or custom format)","metadata (name, version, description, tags)","access control permissions (public or specific addresses)","pinning configuration (duration, redundancy)"],"output_types":["IPFS content hash (CID)","on-chain registry entry with metadata","shareable model URI (ipfs://CID or Tensorplex-specific URL)","access control confirmation"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tensorplex__cap_7","uri":"capability://safety.moderation.privacy.preserving.inference.with.encrypted.model.execution","name":"privacy-preserving inference with encrypted model execution","description":"Tensorplex supports encrypted model inference where model weights and input data are encrypted end-to-end, and computation occurs on encrypted data using homomorphic encryption or trusted execution environments (TEEs). The platform abstracts the cryptographic complexity, allowing users to submit encrypted inference requests that nodes process without decrypting intermediate values. Results are returned encrypted and decrypted only on the client side, ensuring node operators never access plaintext models or data.","intents":["I want to run inference on sensitive data without exposing it to node operators or the network","I need to protect proprietary model weights from being copied or inspected by node operators","I want to comply with privacy regulations (GDPR, HIPAA) by ensuring data never exists in plaintext on remote nodes"],"best_for":["Healthcare and financial organizations with strict data privacy requirements","Companies with proprietary models who want to monetize inference without exposing weights","Developers building privacy-critical applications (medical diagnosis, financial analysis)"],"limitations":["Homomorphic encryption adds 100-1000x computational overhead, making encrypted inference 100-1000x slower than plaintext inference","TEE-based execution is limited to specific hardware (Intel SGX, AMD SEV) with smaller memory enclaves (typically <1GB), restricting model size","Encrypted inference costs are significantly higher than plaintext due to computational overhead — typically 10-100x more expensive","Limited model architecture support — only models compatible with HE or TEE constraints can be encrypted (no dynamic control flow, limited precision)","Key management complexity — users must securely manage encryption keys and handle key rotation"],"requires":["Cryptographic library for client-side encryption/decryption (libhomomorphic or TEE SDK)","Model compatible with homomorphic encryption or TEE constraints","Understanding of encryption, key management, and privacy threat models","Significantly higher compute budget due to encryption overhead"],"input_types":["encrypted model weights (HE-encrypted or TEE-sealed)","encrypted inference input (encrypted tensor or image)","encryption key or TEE attestation"],"output_types":["encrypted inference result","decrypted result (client-side only)","privacy attestation or proof of encrypted execution"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tensorplex__cap_8","uri":"capability://planning.reasoning.governance.and.protocol.upgrades.via.decentralized.voting","name":"governance and protocol upgrades via decentralized voting","description":"Tensorplex uses a token-weighted governance model where token holders vote on protocol changes, fee structures, and network parameters via on-chain voting contracts. Governance proposals are submitted as smart contracts that encode the proposed change, and voting occurs over a fixed period (typically 3-7 days) with results executed automatically if approved. This enables decentralized decision-making without relying on a central authority, though it introduces governance complexity and potential for voter apathy or plutocratic outcomes.","intents":["I want to participate in protocol governance decisions by voting with my staked tokens","I want to propose changes to network fees, validator rewards, or other parameters","I need transparency into how the network evolves and who controls protocol decisions"],"best_for":["Token holders and community members invested in long-term network governance","Decentralized autonomous organizations (DAOs) seeking community-driven decision making","Developers and operators who want influence over protocol evolution"],"limitations":["Voter apathy and low participation rates are common — typically only 5-20% of token holders vote, concentrating power among active participants","Plutocratic outcomes — voting power is proportional to token holdings, favoring large holders over distributed community","Governance attacks are possible — large token holders can coordinate to pass self-serving proposals","Voting delays (3-7 days) slow protocol evolution compared to centralized decision-making","Executed proposals are immutable — if a governance decision is harmful, reverting requires another vote and delay"],"requires":["Tensorplex tokens (minimum holding to vote, typically 1-100 tokens)","Cryptocurrency wallet with voting capability","Understanding of governance proposals and their technical implications","Time to participate in voting periods"],"input_types":["governance proposal (smart contract code or parameter change)","voting choice (yes/no or multiple options)","token balance for vote weight"],"output_types":["vote confirmation and transaction receipt","proposal status and voting results","executed protocol change or parameter update"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":36,"verified":false,"data_access_risk":"high","permissions":["Cryptocurrency wallet with sufficient tokens to stake or pay for compute","Understanding of smart contracts and blockchain transaction mechanics","Network connectivity to Tensorplex node infrastructure (may require VPN or specific firewall rules)","Containerized workload format (Docker) for compatibility with heterogeneous node environments","Cryptocurrency wallet with native Tensorplex tokens (minimum stake amount, typically 1-100 tokens)","Understanding of DeFi mechanics and smart contract interactions","Gas fees for staking transactions (typically $5-50 depending on network congestion)","Access to a Web3 wallet interface (MetaMask, Ledger, Trezor, or similar)","Cryptocurrency wallet with signing capability (MetaMask, Ledger, Trezor, or hardware wallet)","Understanding of blockchain 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