Zero1 Labs (𝟘,𝟷)

Powering Decentralized AI with Cypher. Partnered with @nvidia Ignition AI Accelerator

[Zero1 Labs (𝟘,𝟷) @zero1_labs]

AI depends on data. Blockchains depend on trust. To combine them, privacy has to be solved.

Cypher is Zero1 Labs’ FHE–EVM chain, designed for AI and LLM applications. It performs computations directly on encrypted data, ensuring inputs stay private while results remain correct.

Built for confidential workloads, Cypher preserves scalability, enforces compliance standards, and enables developers to deploy decentralized AI applications without exposing sensitive information.

Learn more: https://t.co/mWfctoIAiV

[Zero1 Labs (𝟘,𝟷) @zero1_labs]

Every intelligent system needs a common language for execution.

Cypher defines that layer for Zero1 Labs.

Bridging agents, protocols, and workflows through structured JSON calls and FHE-secured computation.

https://t.co/swG74rK82g

[Zero1 Labs (𝟘,𝟷) @zero1_labs]

With FHE, your data stays encrypted the entire time it is being used, not just while it is stored.

That is what makes confidential AI possible on Cypher: models can run inference on inputs they never actually see in plaintext.

[Zero1 Labs (𝟘,𝟷) @zero1_labs]

Cypher Testnet, May 2026

85.9M transactions processed end-to-end under FHE.

14.2M DEAI transfers.

7.3M wallets, 121.5K encrypted TXs/day, 37.8M blocks, 263.2 GB encrypted data.

Built for confidential compute.

[Zero1 Labs (𝟘,𝟷) @zero1_labs]

Zero1 Labs is fully open source on GitHub.

Cypher, tooling, and supporting
infrastructure, all public, all available to explore, run, and build on.

→ https://t.co/smdu2NfVrH

[Zero1 Labs (𝟘,𝟷) @zero1_labs]

Contextual GPT agent with Wallet Memory is on GitHub.

A step toward agents that can recall and reason based on your wallet context.

Open source, on GitHub: https://t.co/6QezVJgMOf

[Zero1 Labs (𝟘,𝟷) @zero1_labs]

Fully homomorphic encryption for privacy.

Concurrent processing for speed.

Composable configurations and modular design for flexibility.

Built natively for AI apps from the ground up.

Cypher is the execution layer that brings these together for on-chain AI.

[Zero1 Labs (𝟘,𝟷) @zero1_labs]

Cypher Testnet, April 2026

85.1M transactions processed end-to-end under FHE.

13.2M DEAI transfers.

7.1M wallets, 120.1K encrypted TXs/day, 36.9M blocks, 262.1 GB encrypted data.

Encrypted through execution.

[Zero1 Labs (𝟘,𝟷) @zero1_labs]

DAO governance data, now structured for AI.

The DAO Explainer is live on GitHub. A TypeScript + GraphQL server that connects to Tally and makes on-chain governance readable for agents and apps.

https://t.co/l249hlQ5qn

[Zero1 Labs (𝟘,𝟷) @zero1_labs]

Cypher enables encrypted computation for AI, DePIN, and DeSci workflows.

Process sensitive data without ever decrypting it.

Inputs, models, and state stay private throughout.

Purpose-built for AI data governance. https://t.co/mcaK8X0we3

[Zero1 Labs (𝟘,𝟷) @zero1_labs]

MCP Core SDK v0.1 is live on GitHub.

Build verifiable AI agents across chains and compute layers. Chain-agnostic. Pluggable transports. TypeScript-first.

https://t.co/y06lC7B5KB

[Zero1 Labs (𝟘,𝟷) @zero1_labs]

Cypher Testnet: March 2026

84.3M transactions processed entirely under FHE.

6.9M addresses, 121.2K encrypted TXs/day, 36.6M blocks, 259.6 GB encrypted data.

All staying encrypted through execution.

Fully homomorphic encryption at EVM scale.

[Zero1 Labs (𝟘,𝟷) @zero1_labs]

The full Zero1 Labs codebase is open-source.

Our GitHub includes encrypted compute primitives, on-chain coordination tools, MCP integrations, and governance modules.

Explore the repo → https://t.co/smdu2NfVrH

[Zero1 Labs (𝟘,𝟷) @zero1_labs]

Modern AI has a data ownership problem.

Training datasets are often scraped without consent, hard to audit, and controlled by centralized platforms.

Z1 is building infrastructure for decentralized AI with encrypted data pipelines and verifiable provenance.

[Zero1 Labs (𝟘,𝟷) @zero1_labs]

AI can now compute on data it never sees.

Cypher is the FHE-EVM chain, purpose-built for confidential AI.

Encrypted inputs, verifiable outputs, full privacy by default.

Deploy decentralized AI without ever exposing sensitive data.

[Zero1 Labs (𝟘,𝟷) @zero1_labs]

Turn AI chats into onchain NFTs.

Chat-NFT pins each session to IPFS, hashes context + model metadata via MCP, and mints an ERC-721 to your wallet.

Explore → https://t.co/1sB9BrooPI

[Zero1 Labs (𝟘,𝟷) @zero1_labs]

Cypher Testnet Milestone: February 2026

83.9M transactions on Cypher testnet, processed entirely under FHE.

Data stays encrypted through execution.

• 6.89M addresses
• 122.1K encrypted TXs/day
• 12.7M DEAI transfers
• 35.6M blocks produced
• 257.1 GB encrypted state processed
• 193.8 GB storage used
• 65,251 uploads in a single day

Cypher is the EVM-ready network for encrypted computation, bringing private smart contract execution to real-world scale.

[Zero1 Labs (𝟘,𝟷) @zero1_labs]

The MCP ZK Integrity Layer is live on Z1 GitHub.

It adds zero-knowledge verification to the Model Context Protocol.

Agents can generate proofs that:

• a computation was executed correctly
• context wasn’t altered
• outputs match valid inputs

All without revealing private data.

Verifiable integrity, built directly into MCP execution.

https://t.co/GiiX5WTCQt

[Zero1 Labs (𝟘,𝟷) @zero1_labs]

Built natively for AI apps.
Engineered for encrypted execution.

Cypher integrates FHE, concurrency, and composable architecture to support scalable, privacy-preserving computation on-chain.

Execution remains encrypted at every stage.

[Zero1 Labs (𝟘,𝟷) @zero1_labs]

Privacy is not about hiding data at rest.

It’s about eliminating the moment of exposure.

In most systems:
encrypted → decrypted → computed → exposed.

With Cypher:
encrypted → computed → encrypted.

Execution never transitions into plaintext.

That changes what autonomous systems can safely do on-chain.

[Zero1 Labs (𝟘,𝟷) @zero1_labs]

Fully Homomorphic Encryption (FHE) changes where privacy lives in a system.

In most architectures today:

1. Data is encrypted while stored or transmitted.
2. It is decrypted before computation.
3. Execution happens in plaintext.

That execution step is the exposure point. FHE removes that requirement.

Computation is performed directly on encrypted data.

The system never needs access to the raw inputs to produce a valid result.

The output remains encrypted and can only be decrypted by the key holder.

This makes confidential execution possible:

- Smart contract logic without exposing inputs
- AI inference without revealing model context or user data
- Agents operating without leaking internal state

Cypher is Z1’s implementation of this model, applying FHE at the execution layer so state does not need to be revealed for computation to occur.