Trustless Distributed Compute for web3

Lilypad is a powerful trustless distributed compute platform which leverages key features of blockchain to enable developers to call arbitrary verifiable compute jobs directly from their smart contracts! Lilypad's initial focus is on bringing together the current demand for GPUs (from AI & ML) with decentralised physical infrastructure networks like Filecoin which can supply this demand.


The ability to perform off-chain decentralised compute over data from smart contracts opens the door to a multitude of possible applications including:

  • Inference AI jobs

  • ML training jobs

  • Invoking & supporting generic ZK computations

  • Cross-chain interoperability complement to bridge protocols

  • Utilising inbuilt storage on IPFS

  • Federated Learning consensus (with Bacalhau insulated jobs)

  • IOT & Sensor Data integrations

  • Providing a platform for Digital twins

  • Supply chain tracking & analysis

  • ETL & data preparation jobs


Lilypad 'Layers'

How we see Lilypad network operating on a high level

Testnet Implementations

Lilypad v1 is currently in early testnet phase and is a custom implementation of the ideas & code contained in the paper: "Mechanisms for Outsourcing Computation via a Decentralized Market", which proposed a mediator approach to resolving consensus of deterministic jobs, and also offers insights into running non-deterministic jobs.

See the Litepaper for more info

Currently, two testnets are functional, both of which allow arbitrary untrusted nodes to join, but use a set of mutually trusted mediators to check jobs using verification by replication (see MODICUM paper for details).

  1. Lalechuza - a testnet built on geth

  2. Larana - a testnet built on Filecoin IPC (an advanced scaling solution for blockchain that implements a subnet pattern)

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