Cournot is backed by @yzilabs through @EASYResidency Season 3
Cournot interprets real-world events by aggregating multiple sources in real time and applies multi-agent reasoning to reach a judgment. Every source, reasoning step, and conclusion is cryptographically provable onchain, making every judgment verifiable.
Together, we're powering a future where markets, autonomous agents, and onchain applications can securely settle against real-world truth on @BNBCHAIN.
We know there will be a lot of amazing builders and founders building in the upcoming BNB Bangkok Hackathon.
As a featured Oracle provider in the @BNBCHAIN Kickstart Program and as a part of @yzilabs family and @EASYResidency alumni, Cournot welcomes the builders and founders with:
🔸 Up to 3 months free access to Cournot's Resolution and Intelligence Oracle access for eligible BNB Chain projects
🔸 AI-native multi-source event resolution
🔸 Probability intelligence with claims + sources
🔸 Priority API / agent integration support
🔸 Support across prediction markets, agents, RWA, DeFi & gaming
🔸 Custom workflows and+ co-marketing for selected teams
Don't miss it out. Build and build!
Big ideas meet the Big Mango 🥭
BNB Bangkok brings builders and founders together Nov 25-27 for cash prizes, investor pitches, and fast-track interviews for @EASYResidency
Two tracks. Here’s where you fit 🧵👇
Today, anyone can make a prediction market on Melee.
Markets can be created by anyone in less than 1 minute, allowing creators to make money from the audience they already have.
The top 100 creators this week will earn a Gold Creator badge, with more campaigns on the way.
What if verifying AI-generated outcomes didn't require trusting the AI, exposing private data, or running the same computation across multiple nodes?
That's the foundation of Cournot's verifiable trust architecture.
Traditional oracle systems rely on multiple nodes independently executing computations to reach consensus. While effective, this introduces significant computational overhead, especially for AI-powered inference.
Cournot takes a different approach, combining three complementary technologies:
🔸 zkTLS | Verifiable Evidence
Cryptographically proves that data originates from an authentic source, without exposing the underlying sensitive information.
🔸 opML | Verifiable Reasoning
Enables AI inference to be challenged and verified through fraud proofs, rather than requiring every node to repeat the same expensive computation.
🔸 TEE | Confidential Computing
Protects sensitive inputs and AI reasoning within hardware-secured environments, enabling private data to be evaluated without exposing it to infrastructure operators.
Together, these technologies establish Cournot's One-Honest-Node Verification Model, where a single honest participant can challenge incorrect inference without requiring redundant execution across the network.
The result is a fundamentally different trust model:
Verified inputs → Reproducible reasoning → Deterministic computation → Verifiable on-chain outcomes.
The application of Cournot's architecture extends beyond prediction market resolution. Its underlying architecture can serve as a verifiable intelligence layer for financial systems, real-world assets, insurance, compliance, and autonomous AI agents.
Processed with less redundant computation, stronger privacy guarantees, and verifiable intelligence.
Because the future of decentralized intelligence shouldn't depend on trusting AI. It should be built on the ability to verify it.
Explore Cournot's architecture: https://t.co/n2viG5Y7bk
Miami came back, forced overtime, and finished the job against the Clippers.
Then Cournot went to work.
Within minutes of the result becoming available, Cournot’s Resolution Oracle collected evidence from multiple agents, checked the source and market rules, ran the outcome through its reasoning workflow, and returned a VERY HIGH confidence verdict: Miami Heat.
The full reasoning trail was then published with evidence, verdict, reasoning, and Proof of Reasoning commitments for auditability.
This is what real-world resolution should look like:
Event happens → evidence is gathered → evidence is verified → rules are interpreted → outcome is resolved.
Sports may look straightforward, but at scale, thousands of markets still need to be resolved quickly, consistently, and with evidence anyone can audit.
That’s what Cournot is building for.
In two weeks, @CournotProtocol’s Intelligence Oracle call's volume has doubled.
• 20K+ installs
• 14.4K+ active wallets
• 555K+ calls
What’s even more interesting: roughly 40% of this activity is coming from the @base ecosystem, showing that demand is expanding beyond a single chain or user base. https://t.co/J5zL3L9rqG
As agents become more capable economic actors, they’ll need reliable intelligence before they act. Cournot is building for that layer.
Hands down we'll keep on building.
The bottleneck for AI-native finance may not be intelligence itself (when to act, when to not act). It may be whether the evidence behind that intelligence can be trusted.
As more onchain applications rely on real-world events, the oracle problem gets harder. Prices and rates are structured; regulatory decisions, product launches, credit conditions, protocol milestones, and other real-world outcomes often depend on evidence spread across websites, APIs, filings, and private records.
For Cournot, that means the trust stack has to start before reasoning begins. A stronger architecture looks like:
Verified Evidence → Cournot Reasoning → Verifiable Judgment
If the source can be authenticated first, Cournot can then evaluate whether the evidence satisfies a defined condition and return an auditable outcome.
That matters for prediction markets, where resolution may depend on proving that a statement really came from the designated source.
It matters even more for private credit, RWA, insurance, and compliance, where the application may only need to verify that a condition is true without exposing the underlying data.
A lender may not need to see a borrower’s exact credit score. It may only need a verifiable answer to: Does the borrower meet the required threshold?
The same principle can apply to liquidity, income, covenants, asset eligibility, or other financial conditions.
For Cournot, the task is clear: strengthen the evidence layer, then reason over that evidence in a way applications and agents can inspect and trust.
That is what turns an AI oracle into infrastructure for verifiable financial intelligence. Let's build together.
Permissionless markets don’t just need permissionless creation. They need scalable resolution.
As anyone can create markets around increasingly niche real-world events, the hard part shifts from listing the question to reliably answering it.
That’s where Cournot comes in: interpreting evidence, comparing sources, and turning complex outcomes into auditable judgments.
Read more on how Cournot can help you to scale permissionless markets: https://t.co/GDFPh9jc1N