CRF ProofOps

The description states that CRF ProofOps is a "local-first Evidence Immune System for computational and software-release evidence." It accepts local JSON packages or four included samples. It separates:

• what was claimed;
• what deterministic checks verified;
• what evidence is still missing;
• which status remains authoritative;
• whether prompt-like content was detected and treated only as data;
• the source hash and portable evidence outputs.

The system generates outputs including:

• Proof of Non-Promotion
• Minimal Evidence Delta
• Immunity Capsule
• Decision JSON and Markdown report
• Evidence-Supported Promotion Receipt

It operates through a browser-based interface (static, local-first) and a Python CLI/CI gate with GitHub Actions integration. The system treats all evidence as untrusted data and does not grant AI authority over evidence-backed status.

The description states that CRF ProofOps was built to make "this boundary visible, deterministic, testable, and reusable." It positions itself as a solution for pipelines that can produce confident results while the underlying proof or evidence is incomplete or inconsistent.

The author claims:

• AI can summarize evidence but confidence is not evidence
• Prompt-like instructions embedded inside evidence must never be allowed to change an authoritative status
• The system makes the boundary between claimed and verified evidence explicit

The project evolved from a research foundation into a product during Build Week, with the author stating that "the underlying CRF research foundation existed before the submission period" but that they created "the operational CRF ProofOps product" during the hackathon.

Not evidenced. The description does not identify specific target customers or personas. It describes the system's functionality but does not state who would use it or for what purpose beyond general software development and release processes.

Not evidenced. There is no mention of pricing, licensing, monetization strategy, or business model in the self-reported description.

The description states that CRF ProofOps:

• Operates locally-first with no backend, cloud execution, or solver
• Uses strict JSON parsing and 512 KiB input limit
• Includes schema and field validation
• Protects against unsafe prototype keys
• Implements deterministic domain policies
• Enforces fail-closed status transitions
• Uses source and decision hashing
• Generates downloadable Markdown and JSON reports
• Is available through browser interface and Python CLI/CI gate
• Integrates with GitHub Actions workflows
• Has a repository-local Codex Skill
• Includes adversarial prompt-like-evidence testing

The system is described as having:

• 75 Python tests passed
• 58 subtests passed
• JavaScript and browser-core checks passed
• GitHub Actions validation passed
• A selected-instance real-world pilot that passed
• Human owner QA test: PASS
• Live production smoke test passed all four scenarios

Not evidenced. The description provides no evidence of revenue, customers, or adoption beyond the author's own claims. It mentions a "selected-instance real-world pilot" but does not specify how many instances or what the pilot demonstrated in terms of real-world usage.

The project is described as having been submitted to a hackathon and includes a live demo, but there is no evidence of ongoing usage or traction beyond that context.

Not evidenced. The description does not mention any competitors or competitive landscape. It does not describe how CRF ProofOps relates to existing tools in the software verification or CI/CD space.

• No evidence of real-world adoption: The project is described as a hackathon submission with only a "selected-instance real-world pilot" and no mention of broader usage
• Limited scope: The system is described as handling only four specific scenarios, with claims that it's not universal or production-certified
• Self-reported nature: All evidence is self-reported and unverified; there are no third-party validations or independent assessments
• No commercial traction: No revenue, customers, or business model information provided
• Limited testing scope: The "real-world pilot" is described as limited to one captured CRF instance
• AI dependency without clear authority boundaries: While AI is said to assist in implementation and explanation, the system explicitly states that AI authority remains NONE

1. What specific real-world use cases has CRF ProofOps been applied to beyond the hackathon demo?
2. How many instances of the system are currently being used in practice?
3. What is the actual business model or monetization strategy for CRF ProofOps?
4. Can you provide evidence of any customers or paying users?
5. What are the specific limitations of the current implementation that prevent broader adoption?
6. How does CRF ProofOps handle edge cases not covered by the four included samples?
7. What is the roadmap for expanding beyond the current evidence domains?
8. How do you plan to scale from a single developer tool to enterprise adoption?