Thomas

Ambassador @inference_Labs

[Thomas @thomas_1073]

DSperse makes zero-knowledge proofs practical for real ML.
It verifies only the important parts of a model, not the whole thing faster, cheaper, and easier to use.

Smart verification for real-world AI.

@inference_labs
https://t.co/mNgFZOlDiE

[Thomas @thomas_1073]

Key highlights from the project 👇

✔ Real-world damaged package detection use case
✔ End-to-end model training on SERTN
✔ Dataset integrated from Roboflow
✔ Successfully published and deployed model

Excited to keep building more AI vision systems 🚀

[Thomas @thomas_1073]

Just completed a Damage Package Detection model using SERTN 🚀

🔹 Model:

https://t.co/blU28UpH9F

🔹 Dataset:

https://t.co/qRlm6X1JOW

Built and trained a computer vision pipeline for detecting damaged packages using real dataset workflow.

[Thomas @thomas_1073]

Insurance companies are rapidly integrating AI across critical workflows.

Most still cannot cryptographically prove which model produced a given output.

That stops being a technical gap at scale.

It becomes a liability.

[Thomas @thomas_1073]

750M+ proofs completed on SN2.

~200 proofs/sec is no longer experimental infrastructure.

Verifiable inference is starting to look like production-scale compute.

The next phase of AI will not just be intelligent.

It will be provable.

[Thomas @thomas_1073]

AI is shifting from “can the model do this?” to “can the organization trust this system operationally?”

Accuracy alone doesn’t solve auditability, accountability, or infrastructure reliability.

This is where verifiable inference starts becoming essential.

[Thomas @thomas_1073]

Most AI systems are optimized for demos.

Real-world environments bring bad lighting, noisy signals, and operational pressure.

Infrastructure matters as much as the model.

Inference Labs is building AI systems designed for reality, not benchmarks.

[Thomas @thomas_1073]

AI systems are getting faster at generating outputs.

The harder challenge is preserving trust after outputs leave the system.

Logs change. Context disappears.

Sertn creates a verifiable record for every inference, linking model, input, and output into a trusted proof chain.

[Thomas @thomas_1073]

Most AI systems show outputs. Few show how the model reached them.

Sertn’s Proof Inspector visualizes the inference path with cryptographic verification attached directly to computation.

Not just AI outputs. Verifiable intelligence.

[Thomas @thomas_1073]

Sertn is live.

Deploy verifiable computer vision with cryptographic proof on every inference.

Describe what you want to detect, connect any feed, and launch in minutes no code or specialized hardware required.

Trust the output because you can verify it.

[Thomas @thomas_1073]

Introducing https://t.co/bwEuVReSuP deploy vision AI on any camera and generate cryptographic proof for every inference.

From idea to auditable proof in minutes.

No new hardware. Just verifiable computer vision at scale.

[Thomas @thomas_1073]

Inference Labs has joined the Oracle Partner Network (OPN), bringing verifiable AI into the enterprise ecosystem.

As computer vision scales across complex environments, trust isn’t enough proof matters.

Enterprise AI, backed by cryptographic verification.

[Thomas @thomas_1073]

Most AI stops at generation.

DSperse moves beyond outputs enabling coordination across agents and workflows. Real-world AI isn’t about responses, it’s about execution.

Execution demands trust.

The future isn’t just agentic AI. It’s verifiable agentic AI.

[Thomas @thomas_1073]

As the agent economy scales, coordination becomes critical.

Inference Labs is building open, composable, and verifiable infrastructure for auditable inference and decentralized AI.

From isolated agents → to coordinated, trustworthy systems.

[Thomas @thomas_1073]

Vision models don’t see images all at once.

They split into tiles → process patches → rebuild frames.

With Subnet-2 + DSperse, every tile becomes a provable unit processed and verified.

From trust pipelines → verifiable AI compute. ⚡

[Thomas @thomas_1073]

@hexaCrypto8324 B independent verification

[Thomas @thomas_1073]

Ramadan Mubarak 🌙
A month of discipline, reflection, and quiet strength.

Reset your intentions. Refine your character. Elevate your purpose.
May this Ramadan bring clarity to your heart and barakah to your journey. ✨

[Thomas @thomas_1073]

AI is moving at machine speed.
Accountability should too.

Inference Labs powers verifiable autonomy distributed compute, cryptographic proofs, and on-chain identity for every model decision.
Don’t trust the output.

Verify the process.

#zK

[Thomas @thomas_1073]

AI agents and robots don’t just need intelligence they need accountability at machine speed.

Inference Labs introduces verifiable autonomy.DSperse distributes trusted compute,JSTprove generates proofs, and the network anchors identity for every model action

Prove, don’t assume

[Thomas @thomas_1073]

Quantum computing won’t wait.

Q-day challenges the cryptographic foundations behind ZK systems. At Inference Labs, we’re building zkML to be crypto-agile and quantum-ready engineered for post-quantum migration without breaking trust.

Future-proof by design.

#zkML

[Thomas @thomas_1073]

We’re partnering with @webuildscore SN44 and DSperse SN2 to bring verified vision at scale.

SCORE makes cameras intelligent, reporting events instead of raw video.
DSperse verifies models with zero-knowledge proofs fast, private, provable.

Prove before it moves.

#zkML