Recently, @inference_labs has been hitting the mark quite accurately. The current question is no longer whether AI will become stronger, but when AI starts to act independently, can we still see clearly what it’s doing.

Many systems claim to be auditable, but in reality, they just provide you with a record. Frankly, it’s still a trust issue. Inference Labs does things that don’t require you to trust it blindly; they give you verifiable evidence. AI can make judgments and execute actions on its own, but every step leaves proof that anyone can verify and reproduce—no need for explanations or endorsements.

Once this approach is established, many scenarios can be implemented. Trading bots in DeFi, DAO automatic execution, prediction markets, and even more practical applications like autonomous driving and industrial systems—at least these won’t just shift blame to the model when problems occur.

The current project they’re running, TruthTensor, essentially adds a layer of reconciliation capability to AI Agents. It’s not about whether what they say is correct, but whether they follow the rules. What I appreciate about this team is their mindset. They’re not the kind of project that constantly talks about AI narratives and full roadmaps; their background is more engineering-oriented, and their pace is slow. They build things piece by piece.

They have institutional backing for funding, but it’s clear they’re not rushing to pitch valuation stories. The community’s current focus on points and testing feels more like screening genuine users rather than just boosting hype. If I had to describe my impression of #inferencelabs: it’s more about preparing solutions in advance for systems that will inevitably encounter problems in the future.

This kind of project might not be the hottest in the short term, but when AI fully automates, you’ll realize that the core issue it’s addressing is unavoidable.

#Yap @KaitoAI #KaitoYap #Inference