Tech, Web3, Software, fund increase, seed funding The seed spherical was led by 1kx and included Robot Ventures, Finality Capital, Spartan and extra.
Optimum, a decentralized, performance-enhancing reminiscence layer for any blockchain, raised an $11 million seed spherical, inviting its creators from establishments like Harvard and MIT to leap from the world of academia into the industrial crypto enviornment.
The seed spherical was led by 1kx with participation from Robot Ventures, Finality Capital, Spartan, CMT Digital, SNZ, Triton Capital, Big Brain, CMS, Longhash, NGC, Animoca, GSR, Caladan, Reforge and others.
Optimum is constructing what it calls the lacking reminiscence layer of blockchains, making the best way information is saved, accessed and propagated, quicker, cheaper and really decentralized, in keeping with a press launch.
At the core of Optimum’s innovation is a technique of decentralized coding for distributed methods, referred to as Random Linear Network Coding (RLNC), developed by Muriel Médard, an MIT professor who’s talking at Consensus Toronto 2025.
“If you think of Web3 as a decentralized world computer, people have done an amazing job on the compute part; let’s say, the operating system,” Médard mentioned in an interview. “But anybody who’s put together a computer knows that you also need a bus, which is the data propagation, and you need a memory, which we call the random access memory, as opposed to more static memory like a disk or the cloud.”
Without a scalable reminiscence layer, blockchains face systemic inefficiencies, in keeping with Médard, akin to outdated gossip networks that redundantly propagate information, congested memepools that trigger unpredictable delays and bloated nodes that make retrieval expensive and complicated.
Optimum’s reminiscence infrastructure tackles inefficient information propagation, redundant storage and gradual entry, utilizing Médard’s RLNC coding scheme.
Optimum is now reside on a personal testnet and is inviting L1s, L2s, validators and node operators to expertise its decentralized reminiscence layer in motion.
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