Ally is the founder of Lilypad, a serverless distributed computing network for AI, ML and general compute. Stanley is a dedicated scientist, passionate about leveraging bioinformatics for transformative breakthroughs.
Embracing Decentralized Computing
Ally began her journey by running a café in Australia before diving into tech, mechatronics, and software engineering. Her eclectic experience led her from leading engineering projects to important roles at IBM and Protocol Labs, where her fascination with decentralized technologies was cemented. It was here she met Stanley, a bioinformatician and researcher, who would later join her in building Lilypad Tech.
Stanley’s path is equally unconventional. With a decade of experience in bioinformatics, his focus has primarily been on developing high-performance computing systems for medicine and research. His dedication to open science and connecting distributed computational power to global researchers aligned perfectly with Ally's vision. Together, they aim to solve critical bottlenecks in computing through their startup.
The Origin of Lilypad Tech
Lilypad Tech was born out of frustration with the limitations of traditional centralized computing systems. Ally and Stanley identified a critical need: researchers across various fields, especially those in academia and startups, lacked affordable and scalable access to computing power. Lilypad offers an on-demand distributed compute network that is open to both Web3 and Web2 communities. Through Lilypad, users can tap into available GPUs and CPUs for a wide range of tasks, from training machine learning models to simulating quantum algorithms.
Ally explains, “We’re creating a protocol where compute jobs are matched dynamically with nodes based on their specifications, ensuring optimized resource allocation without middlemen overheads.” This peer-to-peer marketplace for computational tasks leverages reputation systems and multi-verification methods to guarantee job quality and security, ultimately aiming to lower the cost of computing by significant margins compared to services like AWS.
The Vision of Lilypad: Modularity and Flexibility
One of the defining features of Lilypad Tech’s architecture is its modular approach, which allows for the integration of cutting-edge technologies as plugins. Ally emphasizes the potential for integrating privacy measures such as Fully Homomorphic Encryption (FHE) into Lilypad’s protocol. While FHE remains computationally intensive, the modularity of Lilypad enables users to choose and implement privacy solutions as they become feasible.
Bioinformatics and Open Science: Stanley’s Story
For Stanley, Lilypad is not just a project but a platform to advance his lifelong mission of applying machine learning to biological research. He shares a compelling story about his early work in bioinformatics and its intersection with AI. From language processing at Google to developing high-performance computing for medical research, Stanley has witnessed first-hand how distributed systems can democratize scientific discovery.
One of Stanley’s current projects involves leveraging Lilypad’s computing power for genome sequencing, specifically through collaborations like the Human Pan Genome Consortium. He notes that the cost of sequencing a single T2T genome currently stands at half a million dollars, with only fifty patients having been sequenced globally. By drastically lowering these costs, Lilypad aims to accelerate breakthroughs in personalized medicine and genetic research.
Lilypad’s first incentivized testnet is already live, with plans to launch on the mainnet in early 2025.
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