1/ A lot of the AI world is focused on bigger models and better interfaces. Inference Labs is focused on something just as important: trust infrastructure for AI systems that need to operate in the real world.

As agent ecosystems grow, verification becomes infrastructure, not a feature. With zkML + Proof of Inference, we can: • Prove model execution • Verify agent outputs • Enable trustless automation between systems Clawdbot-style agents + verifiable execution = real autonomous

Trustless computer vision is no longer theoretical. Join us for the first Inference Labs AMA as we break down "PROOF OF INFERENCE" how to verify what vision models actually saw and computed, not just what they claim. 📅 Feb 26 ⏰ 2pm UTC 🎙️ With co-founder @colingagich If AI can

As agents start touching money, logistics, and infrastructure, “trust me” stops working. Critical systems need proof, not promises. That’s why we’re building verifiable inference at Inference Labs. Join the our community → Discord / TG: https://t.co/KzMgRbJfJw

Trust in autonomy should not depend on vendors. It should be built on cryptographic proof that stands on its own. Connect with our community on Discord and Telegram. https://t.co/5g1aFlFN9a https://t.co/KzMgRbJfJw

Most LLM benchmarks assume the world is static. Reality isn’t. In TruthTensor, we evaluate models in live prediction markets under drift and show that models can follow instructions locally while violating them over time. Same prompts. Same data. Very different reasoning

1/6 Season 2 of the TruthTensor Crucible is now closed! The seasonal leaderboard locked. The bonuses are credited. The data is being reviewed. What's been accomplished so far has been extraordinary. 🎉

JSTprove is going live on Subnet-2 mainnet. After a week of successful testnet proving, we’re now scaling to thousands of proofs per week and unlocking verifiable inference for models that weren’t feasible before. This is a big step for Proof of Inference.

If your zkML system requires full-model proving, it won’t reach production. Modularity wins. Distribution wins. DSperse + JSTprove win.

🙌 Don Federico Santiago Valverde Dipetta 🙌

https://t.co/4ePt9SyUBm

1/ Frontier AI is powerful, but without proof it remains a black box. Industries need verifiable systems, from robotics to finance to autonomous intelligence.

Community-Driven What if your taste was the algorithm? POPcast® PEERstream™ proves culture is better when creators and viewers build together. This is people-powered media. @POPOLOGYNetwork

Think of it as the engine that transforms complex AI logic into something mathematically provable.

DSperse focuses on creating high-integrity cryptographic infrastructure. It enables large AI computations to be efficiently mapped into constraint systems suitable for zero-knowledge proofs.

That’s where DSperse and JSTprove enter the picture two complementary technologies advancing verifiable machine learning.

Circuit Complexity A tangled web (“Unsliced Circuit”) beside a clean grid (“Sliced Circuit”). 🧩 DSperse simplifies circuit structure, enabling modular verification and flexible proof boundaries.

Memory Usage A bulky, heavy container labeled “Unsliced” vs. a sleek, compact one labeled “Sliced.” 💡 Targeted verification lowers the overall memory footprint — DSperse is lightweight and efficient.

Runtime (Speed) 📊 Visual: Two bars, one tall (“Unsliced / Full Circuit”), one short (“Sliced / DSperse”). ⏱️ DSperse dramatically reduces runtime by verifying only key subcomputations instead of the full model. Faster proofs, same trust scope.