Arweave (AR)

I posted about @WalrusProtocol 2 weeks back. While drafting that, I came across a research paper they dropped in April.

Finally sat down with it.

TL;DR ↓

• Every decentralized storage network has the same problem: How do you keep data safe without making 25 copies of everything?

• Arweave: Just make 25 copies. Safe, expensive.

• Storj/Sia: chop files into pieces, 3 copies worth. Cheap, but if a node dies you have to download the whole file to rebuild its piece. Brutal at scale.

• Walrus: 4.5 copies worth, but when a node dies, the replacement only downloads a tiny fraction of the file to rebuild what was lost.

• Imagine the file as a grid instead of a list. Each node holds one row and one column. Lose a node and the new node grabs one cell from each surviving node, enough to mathematically reconstruct the missing row and column.

When storage nodes swap in or out, most protocols pause writes during the handoff. Walrus doesn't. New writes go to the new nodes, old reads stay with the old.

Every file is

Imagine an AI agent kept a diary:

Monday: "Started new project with user. Seems cool."
Tuesday: "Started new project with user. Seems cool."
Wednesday: "Started new project with user. Seems cool."

This is how most production AI agents work today: no real memory between sessions, no awareness that you’ve talked before.

@WalrusProtocol spent its first year on mainnet fixing this.

Here's a quick TL;DR of their progress ↓

1. What got built in 12 months

→ 510+ TB stored (passed Arweave's 385 TB inside a year)
→ 200+ projects building on it
→ 2nd-largest decentralized storage protocol by volume
→ Core research paper accepted at ACM CCS 2026, a rare academic stamp for any blockchain project
→ Zero downtime since day one

The interesting stuff is what they built on top of it.

Seal → Lets you encrypt data and control who can read it, enforced onchain. Made Walrus usable for healthcare records, financial data, and private AI datasets that can't sit on a public network.

Quilt → Bundles small files together to ma