Show HN: A gate for AI agents that ships a board of its own worst flaws
Lotor A local-first, MCP-native receipt layer for AI agent sessions. What it is This tool writes a signed, tamper-evident log of what an agent did during a session: actions performed, files touched, messages sent, costs incurred, failures encountered. The log lives on your machine, in a format you can inspect, verify, and archive.
- ▪Lotor A local-first, MCP-native receipt layer for AI agent sessions.
- ▪What it is This tool writes a signed, tamper-evident log of what an agent did during a session: actions performed, files touched, messages sent, costs incurred, failures encountered.
- ▪The log lives on your machine, in a format you can inspect, verify, and archive.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,381 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/githubscum/lotor |
| Publication time | Fri, 24 Jul 2026 20:56:50 +0000 |
| Retrieval time | 2026-07-24T21:16:36.814Z |
| Last seen | 2026-07-24T21:16:36.814Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | cluQqR7__kY8 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Lotor A local-first, MCP-native receipt layer for AI agent sessions. What it is This tool writes a signed, tamper-evident log of what an agent did during a session: actions performed, files touched, messages sent, costs incurred, failures encountered. The log lives on your machine, in a format you can inspect, verify, and archive. It does not attempt to prove the agent's actions were correct, only to record them faithfully and make any subsequent tampering detectable. The other half of reliability The argument getting loud right now is that you cannot have agentic systems that are reliable unless they can predict the consequences of their actions. That is true, and it is only the front half. Prediction is the front of reliability. The record is the back.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.