Release Truth

The description states that Release Truth is:

• A multi-user, PostgreSQL-backed evidence gate for release decisions.
• Designed to compute a deterministic GO/NO-GO/CONDITIONAL GO/NOT EVALUABLE assessment from linked claims and evidence.
• Integrates with GitHub via an app, importing issues as claims and pull requests, commits, check runs, and commit statuses as evidence.
• Uses GPT-5.6 for explanation only — not for decision-making.
• Exports tamper-evident JSON signed with Ed25519.
• Enforces append-only records for all decisions, claims, and evidence.
• Implements server-side checks to ensure reviewers actually read the evidence.

Inference The system appears to be a tool for managing release risk through structured claims, evidence, and review processes. It is not a general-purpose AI assistant or platform but a specialized safety mechanism for software releases.

The description states:

• The product aims to eliminate "shipping on vibes" by requiring linked evidence for every claim.
• It positions itself as a tool that makes it physically impossible to ignore gaps between planning and production.
• It emphasizes that reviewers must prove they read the evidence, not just click a checkbox.
• It claims to be a deterministic system where the server computes the assessment, not humans or models.

Inference The positioning is rooted in safety, accountability, and process rigor. The claim evolution suggests a shift from subjective judgment to structured, verifiable decision-making — with an emphasis on preventing incidents caused by unverified assumptions.

The description states:

• It targets engineering teams that ship software.
• It is designed for teams that have shipped on gut feelings and been burned by it.
• It is a multi-user system, implying enterprise or team-level adoption.

Inference The target customer likely includes mid-to-large engineering teams in SaaS or tech companies where release safety is critical. The ICP appears to be teams seeking to reduce risk through structured evidence-based decision-making.

Not evidenced.

The description states:

• Built end-to-end by one developer (Andrei Domashkevich) using Codex.
• Uses Docker, PostgreSQL, Next.js, GPT-5.6, GitHub App API, ed25519, Playwright, and drizzle-orm.
• Includes a GitHub App integration with idempotent retries.
• Implements server-side enforcement of review acknowledgment.
• Uses append-only records for all data.
• Exports tamper-evident JSON signed with Ed25519.
• Has real end-to-end Playwright tests against production.

Inference The technical stack is modern and focused on reliability, security, and correctness. The delivery signals suggest a strong focus on correctness and safety, with attention to edge cases like session cookies and rate limiting.

Not evidenced.

Not evidenced.

• Single-person development: The entire system was built by one person, which raises questions about scalability, long-term maintenance, and team structure.
• No evidence of adoption: There is no indication that the tool has been used beyond the author’s own testing or hackathon submission.
• Unverified claims: The GPT-5.6 integration is described as a "structured Responses API call + server-side grounding check", but there is no verification or data on how often it's used or how accurate its outputs are.
• No pricing or monetization model: The lack of any business model information makes it unclear whether this will ever be commercialized.
• Limited scope: The system appears to be a proof-of-concept or prototype, not yet a production-ready product.

1. What is the actual use case you're solving for? Is there any evidence of real-world adoption?
2. How does the GPT-5.6 integration work in practice? Are there logs or examples of how it's used?
3. How do you plan to scale this beyond a single developer’s session?
4. What is your roadmap for monetization or commercial viability?
5. Have you tested the system with real engineering teams, or is it still experimental?