# Farfaz Learning

- **Event:** [Meta Global AI Developer Hackathon](https://cerebralvalley.ai/e/meta-ai-developer-hackathon)
- **When:** Oct 26, 12:00 PM – Nov 4, 9:00 PM (EST)
- **Where:** Online
- **Team:** [FAZALILLAHI ABDUL MALIK](https://cerebralvalley.ai/u/farfazlearning)
- **GitHub:** https://github.com/farfazlearning-byte/rule-replay
- **Demo video:** https://rule-replay.farfazlearning.workers.dev/rule-replay-demo.mp4
- **Gallery:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/3350

A model can produce a fluent plan that violates one small rule. Rule Replay makes that gap visible before the plan is treated as valid.

The prototype uses a fixed fictional game. Meta Llama 3.3 70B (via Cloudflare Workers AI) proposes an action sequence from source rules and a goal. An independent, developer-authored simulator replays each action, preserves the state trace, and returns the exact failing step as a counterexample. The model gets one bounded repair attempt using that counterexample; the simulator checks the repair again. A second failure remains a failure - nothing is ever labeled verified unless it passed the replay.

The live demo shows source rules, proposed actions, counterexample and replay side by side. A deliberately authored negative test is labeled separately from actual model responses. Unsupported scenarios cause no model request, unknown actions are refused, and inference outages never turn into a simulated success.

Observed honest result: across repeated live runs, Llama's plans failed the replay (collecting on a square with no star, step 4), and the single counterexample-grounded repair also failed at the same step. The product reports not_verified with both counterexamples, exactly as designed: the checking mechanism works; the repair did not succeed in the observed runs. We show this outcome as-is.

Scope: a narrow experiment in verifiable planning, not a learning product. A passing trace proves conformance to the fictional fixture only - no real-world truth claim, no child-facing deployment, no learner data, no private code.

Live product: https://rule-replay.farfazlearning.workers.dev

## More from Meta Global AI Developer Hackathon

- [Hypa Intelligence](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/3347)
- [almona.eg](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/3348)
- [Derm Forge](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/3349)
- [Pinnacles](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/3351)
- [Ahoo](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/3352)
- [Blockparty](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/3353)

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Markdown version of https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/3350. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt