Sacred Forecast — TiboReset

The description states that Sacred Forecast — TiboReset is a two-model, three-view forecasting system for Codex reset planning. It separates:

1. Reset Policy – reports whether official evidence supports continuing resets.
2. Reset Watch Score – an operational readiness score (0–100), not a probability.
3. Calibrated next-36-hour probability – estimates the chance of an official reset announcement.

It uses:

• GPT-5.6 for structured evidence extraction from public X posts
• Deterministic TypeScript code for final calculations
• Supabase Postgres for data storage
• Next.js and React for frontend delivery

The system does not fetch parent threads, media, or additional profiles beyond the bounded pipeline of official X account posts.

The author claims that Sacred Forecast is an explainable reset-watch system, designed to answer three distinct questions:

1. Does official evidence support continuing resets?
2. How elevated is the current operational situation?
3. What is the modeled probability of a reset within 36 hours?

It deliberately avoids collapsing these into one misleading percentage.

The product is positioned as a reset planning tool, not a general-purpose forecasting engine or AI assistant.

Not evidenced.

The description does not identify specific customer segments, use cases beyond personal utility, or target industries. It only describes the author’s own experience and motivation.

Not evidenced.

There is no mention of pricing models, monetization strategies, or commercial relationships. The system appears to be a personal project submitted for a hackathon.

The system is built with:

• Next.js App Router
• React
• TypeScript
• Supabase Postgres
• OpenAI Responses API and GPT-5.6
• Zod for schema validation
• Recharts, GSAP, Tailwind CSS
• Vitest, Playwright, Vercel

It uses a pipeline that:

• Reads bounded public X posts
• Screens irrelevant posts locally
• Sends candidates to GPT-5.6 for structured evidence extraction
• Stores original source, confidence, uncertainty, and provenance
• Recalculates canonical forecast snapshots

The system does not include parent threads or media beyond the configured pipeline.

Not evidenced.

There is no mention of:

• Users or adopters
• Revenue or monetization
• Customer feedback or usage metrics
• Product iteration history or roadmap
• Public deployment or accessibility beyond the author’s own use

The system was built for a hackathon and submitted as a project, not as a commercial product.

Not evidenced.

There is no mention of competitors, market analysis, or how this compares to other tools or systems in the space. The description does not reference any existing solutions or platforms that might address similar needs.

1. No external validation or adoption – The system is described only as a personal project with no evidence of real-world use.
2. Unverified claims about GPT-5.6 performance – The author states GPT-5.6 extracts structured evidence but does not provide data on accuracy, recall, or false positive rates.
3. Limited scope and domain specificity – It is built for Codex resets only, with no indication of scalability or generalization.
4. Self-reported maturity – No evidence of testing, performance metrics, or long-term stability.

1. What was the actual frequency of reset events during the development period?
2. How does the system handle false positives or ambiguous signals from GPT-5.6?
3. Has the system been tested on historical data or validated against known reset events?
4. Are there any plans to expand beyond Codex resets or integrate with other platforms?
5. What is the source of the “policy-driven” and “discretionary” model branches, and how are they calibrated?