LLM-Insights, local demo for people comments and ideas
LLM InSights is a local-first tool designed for iterative content creation and optimization. It allows users to run multi-model A/B tests, refine prompts automatically, and generate synthetic data while keeping all data on local hardware. The system features customizable grading rubrics, automatic prompt optimization, and detailed session analysis capabilities.
- ▪LLM InSights enables users to write prompts and compare outputs from competing LLM models.
- ▪The tool automatically rewrites prompts based on feedback and grades them using customizable rubrics.
- ▪It produces structured records of prompts and scores, which can be used for content quality benchmarks.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,341 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/yuvhaim-gif/LLM_InSight |
| Publication time | Mon, 25 May 2026 14:58:18 +0000 |
| Retrieval time | 2026-05-25T15:27:38.202Z |
| Last seen | 2026-05-25T15:27:38.202Z |
| 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 | NrQh4lDxS3eN |
| 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
LLM InSights A local-first testing and optimization harness for iterative content creation — run multi-model A/B tests, refine prompts automatically with rubric-based grading, and generate scored synthetic data. Built for brand content workflows, prompt engineering, and LLM evaluation on your own hardware. Walkthrough (~1.5 min) What It Does You write a prompt — a piece of brand copy, a product description, a creative brief, or any content task. The tool sends it to two competing LLM models, grades both answers against a configurable rubric, optionally rewrites the prompt using grader feedback, and repeats the cycle — keeping the best answer each round.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.