Show HN: Multi-LLM – review plans and code across 12 coding CLIs, many models
multi-llm - Harness the harnesses Wisdom of crowds for your codebase. Use this plugin to improve planning, implementation, and code review by running the same work through multiple AI coding tools and LLMs in parallel - then consolidating their feedback so you catch bugs, blind spots, and improvements a single model might miss. Two harnesses running the same model can still surface different findings, so combining both axes widens the crowd further. 📺 Watch the video tutorial for a walkthrough of the plugin in action.
- ▪multi-llm - Harness the harnesses Wisdom of crowds for your codebase.
- ▪Use this plugin to improve planning, implementation, and code review by running the same work through multiple AI coding tools and LLMs in parallel - then consolidating their feedback so you catch bugs, blind spots, and improvements a singl
- ▪Two harnesses running the same model can still surface different findings, so combining both axes widens the crowd further. 📺 Watch the video tutorial for a walkthrough of the plugin in action.
Hacker News (AI / LLM) files mainly under ai. We currently carry 2,816 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 | GitHub |
| Canonical URL | https://github.com/beastlabai/multi-llm-plugin |
| Publication time | Wed, 29 Jul 2026 19:26:37 +0000 |
| Retrieval time | 2026-07-29T19:45:57.217Z |
| Last seen | 2026-07-29T19:45:57.217Z |
| 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 | A9WQtynX0-qf · 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
multi-llm - Harness the harnesses Wisdom of crowds for your codebase. Use this plugin to improve planning, implementation, and code review by running the same work through multiple AI coding tools and LLMs in parallel - then consolidating their feedback so you catch bugs, blind spots, and improvements a single model might miss. The diversity works on two axes: not only do you get different LLM models (Opus, GPT, Gemini, Grok, Composer, ...), you also get different code harnesses (Codex, OpenCode, Cursor Agent, Gemini CLI, Grok Build, Cline, goose, Aider, Antigravity CLI, Pi, ...) - each with its own prompting, tooling, and context-gathering behavior. Two harnesses running the same model can still surface different findings, so combining both axes widens the crowd further.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.