Show HN: Mirrors – Improve AI agents with staging environments
Mirrors is a tool designed to improve AI agents by providing staging environments. It learns from existing tools and rebuilds them as runnable services with their own schema and seed data. This allows for testing and development without the need for vendor test instances or production traces.
- ▪Mirrors learns from tools using traces, code, or documentation.
- ▪It rebuilds tools as runnable services with their own schema and seed data.
- ▪No vendor test instance or production traces are required to start using Mirrors.
Hacker News (AI / LLM) files mainly under ai. We currently carry 2,813 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
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 | Mirrors |
| Canonical URL | https://www.runmirrors.com/ |
| Publication time | Wed, 29 Jul 2026 18:45:33 +0000 |
| Retrieval time | 2026-07-29T19:01:35.146Z |
| Last seen | 2026-07-29T19:01:35.146Z |
| 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 | iA_5bifCe8dj · 1 stories |
| 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
That is the case Mirrors is built for. It learns each tool from whatever you have (traces, code, or docs), then rebuilds it as a runnable service with its own schema and seed data. No vendor test instance, no staging licence, no ticket to another team, and no production traces required to start. Your agent calls it exactly the way it calls the real thing.
Excerpt limited to ~120 words for fair-use compliance. The full article is at Mirrors.
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