Joey

Still figuring things out but the process looks good 😊

[Joey @JoeySeid_]

I want internship before the year runs out

[ĸolαde @twitgameboy]

Make a Christmas wish

[Joey @JoeySeid_]

Diving into Subnet 2 has given me a view of the future of Verifiable AI. Reaching 2.2B+ verified proofs on-chain is a milestone.

Subnet 2 is empowering builders to develop trustworthy AI with zero-knowledge-powered verifiable oracles.

#Bittensor #Subnet2 #zkML #Verifiable

[Joey @JoeySeid_]

Diving into Subnet 2 has given me a view of the future of Verifiable AI. Reaching 2.2B+ verified proofs on-chain is a milestone.

Subnet 2 is empowering builders to develop trustworthy AI with zero-knowledge-powered verifiable oracles.

#Bittensor #Subnet2 #zkML #Verifiable

[Zeconomy @zeconomy_x]

A world does not become real because people are told to pay attention.

It becomes real when the structure holds up, the roles make sense, and the mission is clear.

That is what Zeconomy's Phase 1 is there to show.

[Joey @JoeySeid_]

@purintaxyz As more real-world assets move on-chain, projects like @zeconomy_x are making tokenization feel much more practical. The infrastructure side of DeFi is getting really interesting.

[Joey @JoeySeid_]

#Verifiable

[David Oscar VAYLA @DavidOscar110]

Exploring Subnet 2 made me appreciate where Verifiable AI is heading. 2.2B+ verified proofs on-chain is an incredible milestone.

Subnet 2 is helping builders create trusted AI with zero-knowledge-powered verifiable oracles.

#Bittensor #Subnet2 #zkML #VerifiableAI

[Joey @JoeySeid_]

@vorq_ai A future with distributed compute, lower inference costs, and no single point of failure is exactly what AI needs to scale.

[Joey @JoeySeid_]

@vorq_ai I just have a feeling that this is gonna be huge

[Joey @JoeySeid_]

@inference_labs Pilots prove potential, but production demands accountability. Once AI drives real decisions, trust depends on outputs that are traceable, explainable, and verifiable not just accurate.

[Inference Labs @inference_labs]

Enterprise AI is moving past pilot theater. The harder phase is production, where outputs affect workflows, audits, contracts, and operational decisions.

Sertn is built for that layer: not just running AI, but preserving verifiable records of what happened.

[Joey @JoeySeid_]

After reading about DSperse, my biggest takeaway is that targeted verification is a practical step toward making zkML scalable.

@inference_labs #VerifiableAI #zkM

[Joey @JoeySeid_]

Smart engineering often isn’t about doing more it’s about proving the right things.

[Joey @JoeySeid_]

After reading about DSperse, my biggest takeaway is that targeted verification is a practical step toward making zkML scalable.

@inference_labs #VerifiableAI #zkM

[Joey @JoeySeid_]

Instead of proving every computation, DSperse focuses on verifying the parts that matter most reducing costs while maintaining strong guarantees. That makes verifiable AI much more realistic for real-world deployment.

[Joey @JoeySeid_]

Cryptography transformed how we verify transactions online. It feels like Verifiable AI could do something similar for AI inference moving us from “I trust the model” to “I can verify the result.” That’s an exciting direction.

[Thomas @thomas_1073]

Computer vision can identify aircraft, engines, wings, cockpit, and runway markings in a single frame.

The next step is verifiable AI.

Sertn binds every detection to the model, input, output, and a cryptographic proof bringing trust to aviation AI.

[Joey @JoeySeid_]

Hey @DavidOscar110 and @LIGHTLIGHT44730 , if you’re interested in the future of AI, especially Verifiable AI and zkML, you should check out the new Inference Labs X Community. Great place to learn, connect, and stay updated.

https://t.co/Z3rQKsT7s6

[Joey @JoeySeid_]

Inference Labs really changed the assignment:

❌ “I’m probably right.”
✅ “Here’s the proof.”

@inference_labs

[Joey @JoeySeid_]

I’ve been exploring the @inference_labs ecosystem through the Zealy campaign and it’s clear the team is thinking deeply about real-world AI verification problems. Targeted verification is a smart direction for scaling zkML efficiently.

[Joey @JoeySeid_]

This has huge implications for industries where accountability matters.

Civil Aviation:Verify AI-powered inspections and maintenance decisions.
Industrial Manufacturing:Audit automated quality control processes.

The future of AI is both accurate and verifiable. @inference_labs

[Joey @JoeySeid_]

Deploying AI in industries like aviation, manufacturing, and agriculture isn’t just about accuracy it’s about trust, accountability, and auditability.

Traditional AI often gives predictions, but how do you prove how or why a decision was made? 🧵

[Joey @JoeySeid_]

So, what are cryptographic proofs?

Think of them as secure mathematical receipts for AI decisions.

They securely connect the model version, acting like an unalterable digital notary. They’re also highly optimized, with proofs typically under 500 KB.