Cross-Examine

The description states that Cross-Examine:

• Resolves two Git revisions into separate detached worktrees
• Catalogues candidate definitions in changed Python files
• Uses GPT-5.6 to emit schema-constrained Claims about behavior that must be preserved
• Executes these claims against both base and head revisions using layered testing (Layer A: actual behavior; Layer B: Hypothesis examples)
• Assigns findings as SAFE, RISKY, or BROKEN with executable receipts

The system is described as having a five-stage pipeline with:

1. GPT-5.6 reading bounded diff and source context
2. Layer A capturing how the base revision actually behaves
3. Layer B running 60 derandomized Hypothesis examples
4. Repository tests running against both revisions
5. An aggregate() function mapping findings to SAFE, RISKY, or BROKEN

The tool is described as being built with Python, FastAPI, SQLite, React, Vite, and Tailwind.

The description states that Cross-Examine:

• "Codex writes the code. Cross-Examine puts it on the stand"
• Replays base revision's real behavior against PR
• Backs every SAFE, RISKY, or BROKEN with an executable receipt
• Demonstrates a plausible optimization that returns None for an empty list while its happy-path test stays green
• Returns BROKEN with [] as reproducing input in about two seconds

The positioning appears to be:

• A tool for code review and regression detection
• A system that separates model-generated claims from execution-based verdicts
• A tool that provides executable evidence for findings rather than just assertions

Not evidenced. The description does not state who the target customer is or what the ideal customer profile (ICP) might be.

Not evidenced. There is no mention of pricing, business model, monetization strategy, or any commercial aspects beyond the project description itself.

The description states that Cross-Examine:

• Uses Python, FastAPI, SQLite, React, Vite, and Tailwind
• Has bounded host subprocess execution
• Uses Git worktrees
• Uses Hypothesis for testing
• Uses OpenAI Responses API with structured Pydantic output
• Has 138 Python tests and 32 frontend tests
• Runs on Windows, macOS, and Ubuntu
• Uses shell=False, executable allowlist, minimal child environment, deadlines, 2 MB output cap, and receipt redaction for security
• Has a five-stage pipeline with schema and validation layer
• Uses SQLite persistence
• Has FastAPI service with SSE stage progress
• Has CLI surface and deterministic hero construction

The system is described as having:

• Human-provided doctrine that constrains what the code may conclude
• Split between human-provided doctrine and codex-chosen implementation
• Abstain-toward-risk policy
• Trusted-input execution boundary
• Evidence doctrine and submission story

Not evidenced. There is no evidence of revenue, customers, adoption, or any traction beyond the project description itself.

Not evidenced. The description does not mention any competitive landscape or existing products in this space.

The description states:

• This is a hackathon project submitted to the OpenAI 2026 hackathon
• No revenue, customer or traction data is available beyond what they state
• The tool executes target repository's code and should only be pointed at repositories you trust
• Production use needs real isolation
• The public evidence explorer serves a labeled, checked-in fixture so the report UI and its receipts can be inspected without installing anything
• Executing a repository needs Git and a local runtime, so that path runs locally by design

Key risks include:

• Lack of commercial viability or traction beyond hackathon context
• Dependency on trust in target repositories
• Limited scope (Python-only)
• No evidence of any business model or monetization strategy

1. What is the intended commercial application of this tool?
2. How does it plan to scale beyond the current hackathon prototype?
3. What are the specific use cases that would drive adoption?
4. Is there a plan for production deployment and security hardening?
5. What are the technical limitations or constraints that prevent broader adoption?
6. How does it handle edge cases or failures in repository execution?
7. What is the roadmap for expanding beyond Python?
8. Are there any existing users or pilot programs?