One of the most practical things @primus_labs is working on is Proof of Reserves.
The problem is simple: an institution can say it has enough assets, but users still need a way to verify that claim especially when some reserves sit onchain while others are held inside centralized exchanges.
Primus combines zkTLS, TEE and zkVM to connect those pieces.
zkTLS verifies that offchain balances came from the legitimate source, TEE keeps sensitive information protected during processing, and zkVM generates a proof showing that the final reserve figure was correctly calculated.
That means a company can potentially publish verifiable reserve information without publishing every private account detail.
And this has already moved beyond a concept: Primus and Brevis have worked with protocols including Perena and Unitas on cryptographically verifiable reserve systems.
That's a different side of Primus: not just proving what a user owns, but helping prove what an institution actually holds.
One of the most practical things @primus_labs is working on is Proof of Reserves.
The problem is simple: an institution can say it has enough assets, but users still need a way to verify that claim especially when some reserves sit onchain while others are held inside centralized exchanges.
Primus combines zkTLS, TEE and zkVM to connect those pieces.
zkTLS verifies that offchain balances came from the legitimate source, TEE keeps sensitive information protected during processing, and zkVM generates a proof showing that the final reserve figure was correctly calculated.
That means a company can potentially publish verifiable reserve information without publishing every private account detail.
And this has already moved beyond a concept: Primus and Brevis have worked with protocols including Perena and Unitas on cryptographically verifiable reserve systems.
That's a different side of Primus: not just proving what a user owns, but helping prove what an institution actually holds.
What makes @K25dotai more interesting is how deeply AI sits inside the product.
Most prediction markets still rely heavily on manual setup choosing events, structuring markets, and resolving outcomes.
K25 is building toward a different model where AI handles much of that process automatically.
Its AI layer is designed to identify live events, generate relevant markets from those streams, extract useful data in real time, and monitor outcomes as they happen.
That creates a faster cycle where markets can appear and settle with far less human input.
The bigger shift here is scalability. Manual market creation limits how many events a platform can support at once.
With AI driven market generation, K25 can potentially expand across sports, esports, creator content, and entertainment without needing a large operational team behind every event.
If executed well, this could make prediction markets feel far more dynamic not just reacting to headlines, but evolving directly alongside live content itself.
This model introduces a subtle form of onchain privacy by allowing users to permanently destroy ETH and later prove it happened without revealing identity or amounts. Built on ideas from @EIP7503, WORM uses zeroknowledge proofs to mint scarce tokens backed by real, irreversible