MT-07 Live Signals App

The description states that Live Signals is a non-predictive, correction-aware situational monitor built from the Myth-Tech Delta / MT-07 experimental architecture. It processes incoming information through a six-stage Signal Cycle:

1. Incoming sources
2. Cluster and compare
3. Admit or hold out
4. Update metrics
5. Mirror correction audit
6. Tree of Relief

It includes:

• A Visible Event Tape for clusters that pass visibility rules.
• A Held-Out Field for clusters that do not pass, showing their confidence, source support, and suppression reasons.
• Descriptive metrics such as Signal Velocity, Volatility Index, Source Diversity, Escalation Pressure, Correction Rate, Cross-Source Coherence, and Uncertainty Index.
• A Tree of Relief, summarizing what formed, held, resolves, and remains open.

The interface uses static HTML/JavaScript with a Node/Express backend and SQLite persistence. It supports Mock, Degraded, Empty, and Live API states for testing purposes.

Inference This is a simulation-style tool designed to show how information might be filtered and presented under certain rules, not necessarily a live operational system.

The author positions Live Signals as an instrument that does not merely extract signal from wording but governs how wording becomes visible signal. It emphasizes:

• A non-predictive approach, where the system does not forecast future events.
• Correction awareness, meaning it tracks and displays corrections made to data over time.
• Explainability at the metric level, with each metric having an explanation surface showing definition, inputs, contributing evidence, and limitations.
• Transparency in rejection, by keeping held-out information visible alongside accepted signals.

The project evolved from a prior "MT-07 experimental architecture" and was extended using AI tools like Codex and GPT-5.6 during the hackathon process.

Inference This is a conceptual tool aimed at demonstrating how transparency in filtering and correction can be implemented, rather than a commercial product or service.

Not evidenced.

The description does not identify any specific customer base or target user group. It refers to the system as an experimental instrument for monitoring information flows but does not name who would use it or how they would benefit.

Not evidenced.

There is no mention of pricing models, monetization strategies, revenue streams, or business structure. The project is described as a hackathon submission with no indication of commercial intent or deployment.

The system uses:

• Static HTML/JavaScript frontend
• Node.js/Express backend
• SQLite database for persistence
• GDELT as a first ingestion path
• REST API and Server-Sent Events
• Integration with OpenAI Codex and GPT-5.6 during development

Key features include:

• Six-stage Signal Cycle
• Held-Out Field with metadata
• Correction tracking
• Mock, Degraded, Empty, and Live API states
• Regression tests (11 passing)
• Deterministic correction fixture (9% Correction Rate)

Inference The tool appears to be a prototype built for demonstration rather than production use. It includes simulation capabilities and test states, suggesting it was not intended for real-time operational deployment.

Not evidenced.

There is no evidence of:

• Revenue
• Customers
• User adoption
• Product-market fit
• Deployment in live environments
• Iteration history or versioning beyond the hackathon submission

Not evidenced.

The description does not reference any competitors or similar products. It does not discuss market positioning or competitive advantages relative to existing tools for data monitoring, filtering, or correction tracking.

1. Unproven commercial viability: The project is described as a hackathon submission with no evidence of traction or monetization.
2. Unclear operational status: There is no indication whether the system is running in production, tested with real data, or used by others.
3. Limited scope and maturity: Built for demonstration only; lacks features like independent origin analysis, historical replay, or deep source-lineage visualization.
4. AI dependency without runtime use: GPT-5.6 was used during development but not as a runtime engine, raising questions about long-term technical sustainability.
5. Single-person team: The entire project is attributed to one developer (Nelson Williams), which may limit scalability or ongoing maintenance.

1. What is the actual purpose of this tool? Is it intended for internal use, demonstration, or as a foundation for further development?
2. Has this system been tested with real-world data feeds or only simulated inputs?
3. How does the Held-Out Field contribute to decision-making in practice?
4. Are there plans to expand beyond the current mock and live states?
5. What is the long-term vision for the project? Is it intended to evolve into a commercial product?
6. Can you clarify how the correction logic works in practice, especially when dealing with real-time data?
7. How does the system handle multilingual or non-English sources?
8. What are the limitations of the current Signal Cycle and how might they be addressed?