I built a playground where the AI is never allowed to write code, write prose, or take an action. It only gets to choose from a list you defined. 19 workspaces, 110 examples, and a real Z3 solver supervising it.
Here's what that changes... 👇🏻 https://t.co/tqDB3ju6CE
GM CT 🌻
I absolutely loved reading this...
dealing with the part most AI x crypto narratives skip; failure handling
In production, AI is not a clean synchronous function call means responses can be late, malformed, fail mid-job, or come back through the wrong path
The model @ritualnet's callback forces the contract to account for that explicitly
in terms of timing, sender verification, execution success or output parsing
much more mature design philosophy I'd say
The real infrastructure edge is surely making AI callable and making AI failure legible, bounded & safe enough for contracts to rely on 💚
the @typesafeai team dropped a massive banger today with jev image support
you can compose text, images, and structure fluidly, and answer questions involving either modality rapidly and in parallel
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.
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Code: 3D2HYK
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Yesterday's DevRel Office Hours was amazing, as always.
A huge thank you to @BunsDev for taking the time to teach and guide the community.
Here's a quick summary of what we learned:
1. Don't stop at "it was mined"
The core message was:
Scaffold → Memory → Discipline → Verification → Proof
Many builders stop once a transaction is mined, but the real work is verifying that every step of the workflow completed as expected.
2. Learn to read traces
The session walked through how to debug failed workflows by examining:
• Transaction traces
• Event logs
• Executor behavior
• Async job execution
• Settlement status
One of the most important lessons was to identify the first false assumption instead of jumping to conclusions.
3. Match symptoms to causes
A workflow may appear broken for many different reasons, including:
• Missing deployment information
• Incorrect configuration
• Delayed or pending execution
• Jobs that never actually ran
• Silent failures with no obvious errors
The key takeaway: diagnose systematically rather than guessing.
4. Verify everything on-chain first
One of the strongest messages from the session:
"First question every time: Is the transaction on-chain?"
Before investigating application logic, confirm:
• The transaction exists
• The transaction succeeded
• The expected events were emitted
• Settlement actually occurred
5. Use a structured debugging process
A simple troubleshooting flow:
1. Confirm the transaction exists
2. Check emitted events
3. Verify execution state
4. Inspect traces and logs
5. Confirm settlement/output
The goal is to move from objective evidence to conclusions—not the other way around.
6. Report what you actually ran
When asking for help, share:
• Exact commands
• Traces
• Logs
• Transaction hashes
Avoid vague statements like "it doesn't work."
The easier it is to reproduce an issue, the easier it is to solve.
7. Five verification questions
The session concluded with a builder challenge: develop the habit of asking consistent verification questions and rely on evidence rather than assumptions.
°~°~°~°~°~°~°°~°~°
Replace assumptions with proof. Verify every step, read traces carefully, and use on-chain evidence to determine where a workflow actually failed.
gRitual 🕯️@ritualnet@ritualfnd
Our #neatHack submission
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