How it Works
End-to-End Execution Flow
When a recipe is deployed on the W3.io network, here's exactly what happens:
1. Event Detection
Multiple nodes monitor for the recipe's trigger event:
Blockchain events: Transaction confirmed, block mined
Time-based events: Schedule elapsed, deadline reached
External events: API call received, price threshold crossed
Data events: Database updated, file uploaded
2. Node Selection & Consensus
The network automatically selects nodes to execute the recipe:
Consensus formation: Selected nodes agree on execution parameters
Resource allocation: Nodes with required capabilities are chosen
Load balancing: Work distributed across available nodes
Verification setup: Backup nodes assigned for verification
3. Recipe Execution
Nodes execute ingredients in sequence:
Variable pool initialization: Shared data storage created
Step-by-step processing: Each ingredient processes and passes data
Real-time logging: Every action hashed and recorded
Error handling: Failed steps trigger alternative workflows or rollbacks
4. Cross-Chain Coordination
For multi-chain recipes:
State synchronization: Consistent data across all chains
Atomic operations: All chains succeed or all fail together
Message passing: Secure communication between chain-specific nodes
Verification aggregation: Results combined from all participating chains
5. Verification & Recording
Results are cryptographically verified:
Merkle tree construction: All execution hashes aggregated
Consensus validation: Nodes agree on final results
On-chain recording: Merkle root stored on preferred blockchain
Decentralized storage: Full execution logs preserved long-term
Example: Cross-Chain RWA Swap
Let's trace a real RWA trading recipe execution:
Trigger: CitiBank user wants to swap $100 Digital Gold for $100 Digital Silver with JPMorgan user
Execution Flow:
🔔 Event Detected └─ User initiates RWA swap request │ ▼ 🔐 Identity & Compliance └─ Privado ID verifies both users └─ Cube3 screens for sanctions/compliance │ ▼ 💎 Asset Verification └─ Space & Time validates Digital Gold ownership └─ Confirms Digital Silver availability │ ▼ 🔒 Cross-Chain Asset Locking └─ Digital Gold locked on CitiBank chain └─ Digital Silver locked on JPMorgan chain │ ▼ ⚡ Atomic Swap Execution └─ Simultaneous asset transfer └─ Space & Time records transaction proof │ ▼ ✅ Trade Complete └─ Users receive swapped assets └─ Cryptographic proof stored on-chain
Result: Cryptographically verifiable proof that $100 Digital Gold from CitiBank's private chain was atomically swapped for $100 Digital Silver from JPMorgan's private chain, with full identity verification, compliance screening, and cross-chain coordination.
Intelligence in Action
Unlike static smart contracts that can only follow pre-programmed rules, W3.io recipes think and adapt during execution:
Traditional Smart Contract Approach
The Problem: Rigid, binary responses
❌ If condition not met → Execution fails
❌ If service unavailable → Hard stop
❌ One execution path for all scenarios
W3.io Intelligent Recipe Approach
The Solution: Adaptive, context-aware execution
Example: Cross-Chain Asset Transfer During Network Congestion
Traditional Smart Contract:
Attempts to execute on Ethereum
Gas fees are 200 gwei (extremely high)
Result: Transaction fails or costs $500 in gas
Process stops completely
W3.io Recipe:
Detects high gas fees on Ethereum (200 gwei)
Recipe adapts execution strategy:
Route through Polygon for lower fees
Bridge to destination chain
Complete transfer with 95% cost savings
Execution succeeds with alternative path
Traditional Approach: Fixed execution path → failure when conditions aren't ideal
W3.io Approach: Dynamic execution path → adapts to network conditions in real-time
Key Difference:
W3.io recipes can evaluate multiple execution strategies and automatically select the optimal path based on current network conditions, rather than blindly following a single predetermined route.
The Result: Higher success rates and more cost-effective execution across varying network conditions.
Network Coordination
The node network operates as a distributed system:
Node Communication
LibP2P networking: Decentralized peer-to-peer communication
Event propagation: Triggers broadcast across relevant nodes
State sharing: Nodes synchronize recipe execution state
Result aggregation: Multiple nodes contribute to final verification
Task Distribution
Recipe assignment: Network automatically assigns recipes to capable nodes
Load balancing: Work distributed based on node capacity and performance
Redundancy: Critical operations executed by multiple nodes for reliability
Verifiability Throughout
Every step is cryptographically verifiable:
Execution Proof
Each ingredient execution creates:
Input hash: Cryptographic fingerprint of input data
Process hash: Hash of the specific operation performed
Output hash: Cryptographic fingerprint of results
Timestamp: When the operation occurred
Node signature: Which node performed the work
Aggregated Proof
Recipe completion generates:
Merkle tree: All execution hashes combined into single proof
Consensus signatures: Multiple nodes attest to correctness
On-chain record: Immutable proof stored on blockchain
Verification data: Anyone can verify the execution happened correctly
Long-term Auditability
Permanent storage: Full execution logs on decentralized storage
Replay capability: Entire recipe execution can be replayed and verified
Compliance reporting: Automated generation of audit trails
Dispute resolution: Cryptographic proofs resolve any execution disputes
Network Effects
As the network grows, it becomes more powerful:
More Ingredients = More Possibilities
Composability: New ingredients combine with existing ones
Innovation: Novel recipes emerge from ingredient combinations
Specialization: Ingredients optimized for specific use cases
Standards: Common patterns emerge and become reusable templates
More Nodes = Better Performance
Better reliability: More redundancy and failover options
Geographic distribution: Nodes worldwide reduce latency
Specialized hardware: Different nodes optimized for different workloads
More Recipes = Network Intelligence
Pattern recognition: Network can learn optimal execution strategies
Performance optimization: Historical data can improves future execution
Resource allocation: Better matching of recipes to node capabilities
Cost efficiency: Network automatically finds most cost-effective execution paths
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