---
title: "Public Knowledge Router: Turning Raw AI Conversations into Publishable Knowledge Assets"
type: lab
description: "An interactive simulation of how raw personal knowledge is routed into public, AI-readable assets."
summary: "Route nine fictional raw inputs — chat transcripts, meeting notes, prompt ideas — through four boundary zones and watch the safe ones become typed public assets with canonical pages, markdown mirrors, and llms-full.txt entries."
ai_summary: "Interactive lab prototype demonstrating the public/private routing pipeline: raw input to sanitized summary, frontmatter, canonical page, markdown mirror, and llms-full.txt entry. Static, no backend."
status: public
visibility: public
language: en
topics: [knowledge-capture, publishing-pipeline]
tags: [experiment, interactive, boundary-safe]
audience: [ai-practitioners, builders]
publishedAt: 2026-07-08
updatedAt: 2026-07-08
featured: true
related: [labs/chat-conversation-mining-for-public-knowledge, templates/public-private-boundary-checker]
sourceStatus: original
sourceSensitivity: public
tool: [Astro, vanilla JS]
hypothesis: "The public/private routing pipeline can be shown, not just described, in a static-site interactive."
setup: "Self-contained Astro component at /labs/public-knowledge-router — keyboard-first routing, pointer drag as enhancement, agent-view pipeline, SVG asset map; no backend."
result: in-progress
nextIteration: "Collect feedback on whether the agent view makes the markdown-mirror and llms.txt concepts legible to first-time visitors."
artifactUrl: "/labs/public-knowledge-router/"
canonicalUrl: "https://wbeen-personal-kb.vercel.app/labs/public-knowledge-router"
---

## Hypothesis

This site's core claim — raw personal knowledge becomes public, reusable,
AI-readable assets only after an explicit boundary decision — is easier to
show than to explain. A small interactive simulation should teach the
routing model faster than the policy document does.

## Setup

A self-contained Astro component embedded at
[/labs/public-knowledge-router](/labs/public-knowledge-router/). Nine
fictional raw inputs (chat transcript, social post draft, prompt idea,
internal meeting note, side project demo, reference article, workflow
checklist, personal note, agent experiment) are routed into four zones:
Public, Generalize first, Private source, Discard. Safe placements generate
typed assets; an agent-view toggle exposes the machine-readable path for
each one: raw input → sanitized summary → frontmatter → canonical page →
markdown mirror → llms-full.txt entry. A final button draws the resulting
asset map as a small knowledge graph.

Constraints: static only — no backend, no storage, no network calls, no
dependencies beyond what the site already ships. All sample card text is
fictional and generalized.

## What I tried

- Keyboard-first interaction as the core path: select a card, choose a zone
  with buttons or the 1–4 keys. Pointer drag-and-drop is layered on top as
  an enhancement, using pointer events instead of the HTML5 drag API (which
  has no touch support and poor assistive-technology behavior).
- A vertical stepper below 620px instead of shrinking the routing canvas:
  one card at a time, full-width zone buttons, same state either way.
- Pre-rendering every DOM node in the Astro template and letting the script
  only move nodes and toggle attributes, so scoped styles survive without a
  client framework.
- Letting "wrong" placements stand. The router keeps the user's decision and
  shows a quiet coaching note instead of bouncing the card back — the
  boundary is a judgment call, and the simulation should feel like one.

## Result

In progress. The routing loop, agent view, stepper, and asset map work
without a backend, and the interaction still reads correctly with
animations disabled (`prefers-reduced-motion` drops the drag settle and the
graph draw-in). What failed first: an early version that generated card
markup from script broke the scoped-style contract and was rewritten to the
render-static-then-move approach. The open question is legibility — whether
the six-step agent view actually explains the mirror and llms.txt concepts
to someone who has never seen them.

## Next iteration

Collect feedback on the agent view's legibility, then consider routing a
real (already-published) item through the simulation as a worked example
instead of fully fictional cards.

## Related resources

- The interactive prototype: [/labs/public-knowledge-router](/labs/public-knowledge-router/)
- How this site's AI-readable layer works: [/ai-readable](/ai-readable/)

---

Canonical page: https://wbeen-personal-kb.vercel.app/labs/public-knowledge-router
This is the AI-readable Markdown mirror. Public, curated content only.