Walrus (WAL)

Correct me if I’m wrong, but I think walrus-2:native just passed $DEEP in market cap for the first time. İnteresting 🫪 https://t.co/elmiOYtxGf

Walrus has already stored 450TB+ of data since launch. That makes it one of the largest decentralized storage networks by data volume. $WAL still sits near a ~$83M market cap. Why is the market barely valuing a storage network already seeing production-scale usage? @WalrusProtocol is a decentralized storage protocol built by Mysten Labs, the team behind Sui. The network focuses on storing large datasets efficiently while keeping them verifiable and programmable. Its latest release, Walrus Memory, introduces persistent storage for AI agents across sessions. The idea is giving agents long-term memory without relying on centralized databases. That positions Walrus closer to AI infrastructure than traditional storage networks. Compared to older storage projects, Walrus focuses on: • Fast access to large data objects • Smart contract composability • AI-related storage workloads • Native integration with the Sui ecosystem There are still important risks. Storage demand needs to keep growing for the network's economics to strengthen over time. At the same time: • Investor unlocks are already underway • Ecosystem allocations continue vesting • Supply emissions remain a factor until storage revenue grows further Unlike many infrastructure projects, adoption cannot rely on narrative alone. Long-term value depends on users continuing to store and retrieve real data at scale. At the same time: • No major exploit history surfaced • No public governance controversies emerged • Development remains closely tied to the broader Sui ecosystem Tokenomics • Price: ~$0.034 • Market cap: ~$83.3M • Circulating supply: 2.4B • Total supply: 5B (max) Always take whatever you read on the internet with a pinch of salt, do your own research, NFA.

How far can we damage the Poseidon sea elephant? Template collapse experiment @WalrusProtocol @at_bellyseal Starting from a Poseidon‑concept sea elephant character → Adding impossible underwater architectural structures → Inducing style collisions → Gradually accumulating recursive reflections, overlapping timelines, and non‑Euclidean structures ▶Result?? We tried to maintain the character’s identity (mother, face, sea‑god concept) to the end, but the facial structure began to show duplication, overly dense details, and anatomical collapse. In the end, this experiment was a process to determine where the boundary lies between "maintaining identity" and "inducing hallucination".