15:I[981,["658","static/chunks/658-a698693f56d0116a.js","183","static/chunks/183-6beeeb801c274aaa.js","474","static/chunks/app/%5Btype%5D/layout-631c4730b83c8223.js"],"Posts"] 16:T457,I felt frustrated finding high-signal discussions on HN, and I started this project to better understand how this community actually works.

That led me to build readhn, an MCP server that helps with three things:

- Discover: find relevant stories/comments by keyword, score, and time window

- Trust: identify credible voices using EigenTrust-style propagation from seed experts

- Understand: show why each result is ranked, with explicit signals instead of a black-box score

It includes 6 tools: discover_stories, search, find_experts, expert_brief, story_brief, and thread_analysis.

I also added readhn setup so AI agents can auto-configure it (Claude Code, Codex, Cursor, and others) after pip install.

I’d love feedback on:

1) whether these ranking signals match how you evaluate HN quality,

2) trust-model tradeoffs,

3) what would make this useful in your daily workflow.

If this is useful to you, starring the repo helps others discover it: https://github.com/xodn348/readhn17:T58d,I’ve been wrestling with a philosophical and ethical question regarding authorship in the age of LLMs, and I’m curious where the HN community draws the line.

Suppose you spend months deeply researching a niche topic. You make your own discoveries, structure your own insights, and feed all of this tightly curated, highly specific context into an LLM. You essentially build a custom knowledge base and train the model on your exact mental framework.

When you finally want to write a post or comment sharing your findings, you outline your specific thoughts and use that meticulously primed LLM to structure and generate the final prose.

My questions for you:

Is it unethical to post this without an "AI-generated" disclaime

SaaSpocalypse Now