OpenLine – a tiny receipt layer for agents (MCP/LangGraph ready)

Hacker News

• What: Given a small argument graph (nodes/edges), OpenLine returns a verifiable receipt (claim/because/but/so + guardrails + telemetry). • Why: Tracing is descriptive; receipts are normative. They make agent steps auditable & safe by default. • How: stdlib Python; MCP JSON-RPC stub; LangGraph node; schema + validator; optional signing step; GitHub Actions + Pages. Repo: https://github.com/terryncew/openline-core Demo: https://terryncew.github.io/COLE-Coherence-Layer-Engine-/nca... Would love feedback on the guard checks and the OLP schema—where should it grow?

Visit product View on Hacker NewsSep 21, 2025

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, mcp · Missing: mac, macos, cursor

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

25%25% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

16%16% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

11%11% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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