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AI Tech Radar — Agentic-AI knowledge wiki

A self-maintaining knowledge graph on agentic AI, compiled from technical literature, blog posts, and industry/consulting papers. Scanners pull sources in; they're compiled into an interlinked wiki that compounds over time. It follows the Karpathy "LLM-wiki" pattern and is an Obsidian vault, so wikilinks and graph view work natively.

It serves a senior AI/tech exec persona — signal over textbook: what's genuinely new, why it matters, and the "so what." Voice and node-quality rules live in wiki/_templates/STYLE.md; architecture and operating conventions live in CLAUDE.md.

Layout

Path What it holds
raw/ Immutable source captures — raw/arxiv/, raw/blogs/, raw/news/, plus per-year censuses. Scanners only ever ADD here; never edited. Not part of the graph.
wiki/ The maintained graph: concepts/, entities/, sources/, comparisons/, overview.md, and _templates/ (node structures + style guide).
index.md Catalog of every wiki page — read first, then drill in.
log.md Append-only timeline of every ingest / lint pass.
CLAUDE.md The schema: architecture, ingest/query/lint operations, graph-density rules.
scanners.md Source list + agents-centric focus filter the scanners read.
skills/ The automations — arxiv-scanner/, blog-scanner/, kg-curator/ (see skills/README.md).
briefings/ · trackers/ · radar-notes/ On-demand exec deliverables, one doc type per folder (created on first use).

Three ingest tiers (coverage → depth)

  1. Census — every matching paper as metadata in raw/arxiv/<year>-census/. Exhaustive, not in the graph.
  2. Browsable — abstract-only auto-notes in wiki/sources/<year>-census/, keyword-linked, filtered out of the global graph view.
  3. Deep — full-text reads with analysis (depth: full-text), first-class graph nodes.

How it works

Three scanners feed the graph, each a different layer of truth: arXiv = what's true (research), news = what happened (events), blogs = what it means / who thinks what (attributed opinion + debates). After a scan, the ingest operation writes/refreshes source notes, integrates findings into the relevant concept/entity pages (no duplicates; contradictions flagged inline), promotes node-worthy references to their own linked nodes, then updates index.md and appends to log.md. The kg-curator skill periodically tends the graph — mining new nodes, enriching citation-ranked sections, normalizing to templates, and linting for orphans, broken links, and stale framing.

Two other operations: query (answer from the graph with citations to the pages used; file durable answers back as new pages) and lint (health-check: contradictions, stale claims, orphans, missing cross-references).

Conventions

Kebab-case filename slugs; Obsidian [[slug|Display]] wikilinks (the links are the graph). Every node has ≥1 inbound and ≥1 outbound link — no orphans — and every relationship is reciprocal (back-link on both ends). Concept and entity pages are synthesized, compounding articles grounded in full text, not abstracts; source notes are one per ingested item. Full node structure and style are in wiki/_templates/.

Current size

57 concepts · 157 entities · ~1,700 source notes · 2025 + 2026 agents-centric censuses (~23k paper records). See index.md for the live catalog and log.md for the running history.

About

LLM wiki based Knowledge Graph on agentic AI — scanners ingest research, events, and the voices of key people (named researchers, VCs, lab leaders).

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