Before Evidence

The description states that Before Evidence is a system that:

• Guides users through a fixed flow for evaluating claims and evidence.
• Requires users to define:
  • A claim
  • Initial confidence
  • Rival explanations
  • Prediction
  • Refutation condition
  • Experiment description
• Locks the commitment before revealing evidence.
• Reveals predetermined evidence.
• Allows interpretation and confidence update.
• Uses GPT-5.6 for structured audit of consistency.

It is described as a tool that prevents users from rewriting their standards after seeing results, by comparing the sealed prior standard with later behavior.

Inference The product appears to be an experimental decision-making framework, not a commercial SaaS offering. It is built around a specific integrity mechanism: pre-commitment locking and post-hoc audit via AI.

The author claims that Before Evidence addresses the problem of people deciding what evidence means only after seeing whether they like the result. This leads to shifting thresholds, new exclusions, or confidence changes not justified beforehand.

It positions itself as a tool for those who interpret experiments or important decisions without wanting to quietly move the goalposts afterward.

Inference The positioning is narrow and targeted toward individuals or teams engaged in structured analysis or experimentation — e.g., product managers, researchers, analysts. It does not claim to be a general-purpose decision-making app.

The description states that Before Evidence is designed for people who interpret experiments and important decisions without wanting to quietly move the goalposts afterward.

Potential use cases mentioned include:

• Product experiments
• Research and analysis
• Investment decisions
• Hiring evaluations
• Policy decisions
• Personal decisions with explicit evidence thresholds

Inference The target customer profile is likely early-stage professionals or researchers who value consistency in evaluation, but no evidence of actual users or customer segments is provided.

Not evidenced. The description does not mention any pricing model, monetization strategy, or business model.

The author states that the system was built using:

• Next.js
• TypeScript
• PostgreSQL
• OpenRouter
• Zod
• GitHub Actions
• Vercel

It uses Codex for implementation and includes features such as:

• Server-enforced immutable locking
• Predetermined evidence isolation
• Structured GPT-5.6 schemas
• SHA-256 record fingerprints
• JSON/Markdown exports
• Responsive UX
• 56 automated tests

Inference The technical stack suggests a modern, production-ready web application with strong integrity controls and AI integration. However, no evidence of deployment beyond the demo or user base is provided.

Not evidenced. There is no mention of:

• Revenue
• Customers
• User adoption
• Product usage metrics
• Any form of traction beyond the prototype submission

The project is described as a hackathon submission, and the author explicitly states that the current version uses one fixed scenario to prove the mechanism clearly.

Not evidenced. No information is provided about:

• Competitors
• Market landscape
• Existing tools in this space
• Prior art or similar products

1. No real-world usage: The product is described as a prototype, not a commercial offering.
2. Unverified AI role: GPT-5.6 is used for auditing but the description does not clarify how effective or reliable this audit mechanism is in practice.
3. Limited scope: The tool only works within one fixed scenario, and no evidence of scalability or broader application is shown.
4. Self-reported maturity: There is no independent verification of the system’s integrity or performance claims.

1. Has anyone outside of the development team actually used this tool?
2. How does the GPT-5.6 audit handle edge cases or ambiguous interpretations?
3. What are the actual use cases you've seen in practice, if any?
4. Are there plans to expand beyond the current fixed scenario?
5. How would you monetize or scale this product, if at all?