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Hey all, we’ve recently heard questions from our community around Telegram API connection. This feedback is important, and we’re paying close attention. To clarify: - ORO's connection to Telegram is powered by the official Telegram API, which is the most secure method available.

What is zkTLS, and why does it matter? zkTLS proves data came from a trusted source without exposing the sender’s identity or contents. It’s like a sealed envelope for your data - cryptographically proving it was delivered, without showing what’s inside. ORO uses zkTLS to make

What ORO is doing is simple. We're enabling you to get value out of previously untapped IP to AI companies for $$ you wouldn't have earned otherwise. When you connect your data to ORO, you’re earning a stake in a revolution where your data works for you.

AI is learning to see, hear, and speak at once. But syncing audio, text, and video is still a mess. Audio, text, video, and context need to sync. In the wild, that’s rare. ORO fixes this at the source with structured multimodal quests that collect clean, aligned data across

Frontier models don’t just need answers, they need reasoning steps. CoT traces. RLHF. ToT. ORO’s active quests collect structured rationales and thought processes from real users. It’s the kind of high-signal supervision next-gen models are starving for.

AI is moving past static training. With test-time compute, models reason on demand — not just on what they saw months ago. ORO makes this shift possible: unlocking fresh, private data to power context-aware intelligence at inference time, when it actually matters.

In the previous generation of AI data, pre-ChatGPT: text-focused data labeling. Labeling images, like images of cars or animals. That will disappear in the next 2 years. The models are becoming too smart. Frontier AI requires a new paradigm. High-quality, net-new datasets.

The old consent model doesn’t work for AI. Click a box, agree to fine print… that might’ve worked for static data. But not for recursive training where your data keeps being used, again and again. You can’t revoke it. You can’t trace it. You can’t prove it was ever yours.

In most AI systems, user contributions vanish into the system with no trace. No receipts. No recognition. No link between input and outcome. ORO is changing that with onchain attribution. 🔸 Your actions are logged 🔸 Your contributions are credited 🔸 Your rewards are

As model architectures converge, what sets them apart won’t be code — it’ll be data provenance. Where your data came from. Who contributed it. What guarantees were in place. ORO turns provenance into infrastructure, logging each contribution with a cryptographic receipt that

We’ve launched missions for Fitbit, Garmin, Oura, and Coros. But this is just the beginning. Can you guess which mission is coming next? 👀 Drop your predictions below 👇

The next generation of AI agents won’t just complete tasks - they’ll need to remember how you think and what you prefer. That requires memory, built from real user interaction over time. ORO is building that memory layer. You contribute it → you earn from it.

The story of AI started at a summer workshop at Dartmouth in 1956. A group of researchers - John McCarthy, Marvin Minsky, Claude Shannon, and others proposed that “every aspect of learning or any other feature of intelligence can in principle be so precisely described that a

Frontier AI is being trained on behavior, language, and signals from billions of people. But only a handful of companies own the outcomes. We have to ask: is intelligence infrastructure for everyone, or just another asset to be extracted? If AI is built on collective input,

Architectures are converging. Compute is commoditizing. The next moat for frontier AI isn’t scale — it’s provenance: knowing where every bit of training data came from, with guarantees. Regulators will demand it. Users will expect it. Labs will compete on it. ORO is the

How well does an AI’s behavior match human values, goals, and expectations? That’s the core question behind alignment. Most of today’s alignment efforts focus on the model level - adjusting parameters, tweaking reward models, fine-tuning outputs. But that’s only part of the

Frontier models don’t just need right answers. They need reasoning paths: how humans make decisions, weigh tradeoffs, and adapt to feedback. Chain-of-thought traces. Reward modeling traces. Multi-hop reasoning. This is the real signal that will separate next-gen agents from

The shift is already underway: LLMs are being repurposed as strategic automation tools across sectors. It’s parsing SEC filings, modeling private valuations, and drafting investment memos inside firms like BlackRock, KKR, and even the U.S. Air Force. These aren’t chatbots.