Widespread ZKML adoption won’t start with proofs — it starts with usability. Watch Colin Gagich breaks down the basics of DSperse and how it shifts ZK from pure security to real scalability.

Autonomous systems have no “biometrics.” No fingerprints. No Face ID. No way to prove their actions haven’t been tampered with. Ron Chan @sudo_ron explains why autonomous systems need the same guarantees humans use every day.

Bittensor Subnet 2 showed that incentives speed up proofs. Inference Network brings that energy to enterprise autonomy. Economics plus cryptography, not trust alone.

ONNX in → Proof out. That’s the experience JSTprove delivers. Paired with DSperse slicing, zkML finally escapes the lab.

3/ The result? Verifiable inference near real-time for: • autonomous systems • finance • medical AI • cybersecurity • on-chain agents This is the path to production zkML.

2/ DSperse: slice + distribute JSTprove: compile + prove Each slice becomes a tiny circuit that can be proven fast, in parallel, with the best backend.

1/ Most zkML tries to prove an entire model at once: circuit → witness → proof. Great for demos. Hard for real systems. We needed a rethink.

Airports, finance, healthcare, DeFi. High stakes domains need cryptographic audit trails. Inference Labs ships DSperse and JSTprove so every prediction and action is traceable without revealing private data or proprietary weights.

We just launched a $100K Galxe Starboard campaign to recognize the top contributors shaping verifiable AI. How it works: • Complete quests about Inference Labs, DSperse, JSTprove, Proof of Inference • Create high-quality posts on X • Earn points based on originality & reach

2/ The Inference Network rewards correct proofs and slashes dishonesty. Economics plus cryptography replaces trust with math. Join us in Discord and TG: Telegram: https://t.co/s57Z1c22s0 Discord server:

1/ Decentralized AI needs incentives, not promises. Central verifiers do not scale across vendors and borders.

2/ Inference Network answers with Proof of Inference. Identity, execution, and integrity captured as reusable proofs. Simple to check, hard to fake. Auditable Autonomy begins here.

1/ Enterprises ask three questions: - Which model acted. - What did it compute. - Was the output changed in transit.

Autonomous agents are leaving sandboxes. Inference Network gives them an identity made of math. No proof, no permission. With on chain proofs, agents can cross borders, vendors, and networks while staying accountable.

https://t.co/zlULffGvgD

DSperse proves what matters. Instead of end to end overhead, we verify slices and aggregate. JSTprove accelerates proofs with GPU and smart circuits. Together they make verifiable AI practical at internet scale.

2/ Inference Labs treats inference like identity. No proof, no permission. DSperse targets what to verify. JSTprove produces receipts for every decision. Proofs stored on chain give auditors, customers, and regulators confidence. That is the path to a Trustworthy Autonomous

1/ Security incidents keep stacking across clouds, extensions, local models, and laptops. Attackers target tokens, identity edges, and silent model tampering. If AI runs without evidence, you inherit the risk.

Congrats to the Irys team on reaching mainnet 👏 Programmable data is critical to the future of auditable autonomy.

The Inference Network is a public memory of machine behavior. Proofs are published, indexed, and composable so audits, insurance, and compliance become queries, not month long exercises.

Community is 🦾