GROUNDING Methodology

GROUNDING turns an aggregated AI stream into public Daily Briefs, Concepts, Topics, Entities, and Knowledge Gaps using signal/noise scoring, source links, and an allowlisted publishing pipeline.

The goal is not to mirror every AI announcement. The goal is to identify what AI builders should understand next.

Scoring model

Each candidate item is evaluated for:

• Practical relevance to builders.
• Impact on AI products, agents, coding workflows, retrieval, evaluation, infrastructure, or model behavior.
• Learning value: whether the item teaches a durable concept or reveals a useful failure mode.
• Knowledge-gap fit: whether it connects to a recurring gap the community should study.
• Actionability: whether builders can change a workflow, architecture, evaluation, or research direction because of it.

Items below the public relevance threshold are filtered as noise. The public brief keeps signal items and records the signal/noise counts.

Publishing pipeline

GROUNDING uses an allowlisted static-site pipeline:

• Only selected content folders are copied into the public site.
• Dead wiki links are flattened so public pages do not point to unpublished notes.
• Entity pages receive clean descriptions, authoritative sameAs links when curated, and short visible ledes.
• Concept pages receive answer-first definitions when the definition is curated.
• Public update sections are rotated so stale feed fanout does not dominate the page.
• Machine-readable files are emitted for search engines and AI agents.

Machine-readable surfaces

• /sitemap.xml for search discovery.
• /index.xml for full-content RSS freshness.
• /robots.txt for explicit search, citation, user-fetch, and training bot policy.
• /llms.txt for the compact AI-agent index.
• /llms-full.txt for expanded machine-readable grounding context.

FAQ

How does GROUNDING score AI signals?

GROUNDING scores items by practical relevance, impact, learning value, knowledge-gap fit, and actionability for AI builders, while discounting hype and low-value duplicates.

What does signal mean in GROUNDING?

Signal is an item the radar kept as community-relevant for AI builders; noise is an item filtered below the public relevance threshold.

What machine-readable files does GROUNDING publish?

GROUNDING publishes a sitemap, RSS feed, robots.txt, llms.txt, and llms-full.txt so search engines and AI agents can discover fresh and durable content.