Four questions every Ritual callback should ask. 🧵 When your contract asks an AI model or a service for something, the answer comes back later. At Office Hours we drew the whole lesson as one picture: every answer, or non-answer, takes the same walk. Here it is.

GM CT 💚 Happy Thursday Yesterday, explored a simple idea revolving around whether a smart contract see beyond the blockchain? Contracts understand onchain data like balances and transactions, but live information such as solana's current price comes from the outside world Ritual Market Eye was built around bridging that gap Let's just reflect on whether a contract think? not saying a smart contract suddenly becoming conscious talking about something much simpler, whether a contract can take information, give that information to a model, and use the model's interpretation inside an onchain application? This is where another Ritual primitive becomes interesting LLM Precompile — 0x0802 the application can call an LLM through Ritual's execution environment rather than sending the data to some private AI invisible backend consider Ritual Market Eye again. First the contract sees: SOL: $__ 24h: +4.2% Volume: rising Volatility: high Then we give that information to the model and ask: Based only on this data, what type of market environment are we looking at? The model could return something structured: Market state as Bullish and high risk informing price and volume are increasing while volatility remains elevated Notice that yesterday our flow was: Internet ↓ HTTP ↓ JQ ↓ Data Now we add another step: Internet ↓ SEE — HTTP ↓ EXTRACT — JQ ↓ THINK — LLM ↓ ⛓️ USE THE RESULT ONCHAIN I find much more interesting saying: “Ritual has an LLM precompile” Because we're starting to see what the primitive actually enables The contract receives context and asked a model to interpret that context There is real research behind why this matters too Recent reasoning-model research, including work published in Nature around DeepSeek-R1, has explored things like self-reflection, verification and adapting reasoning strategies But research also keeps reminding us that LLM reasoning has limitations so our goal is to make the reasoning step visible, programmable and part of the application's execution instead of hiding it inside a Web2 server Crypto builders are already moving in this direction too .@ritualfnd has been talking about LLM calls, agents, scheduled compute and payments as pieces of the same builder stack and Ritual community builders are already experimenting with contracts that can effectively: see → think → act. So Ritual Market Eye becomes Ritual Market Brain Ritual Market Eye collects the market information. Then Ritual Market Brain interprets it. The simple experiment demonstrates something much bigger A contract can collect information from outside the blockchain and pass that information into a reasoning model. So we gave the contract eyes Today we give it a reasoning layer And once a contract can: SEE → THINK now the next question will be Can a Contract Act? Because seeing the world is useful and understanding the world is better But autonomy really begins when software can decide what to do next. Do y'all agree? ❖