Release prep:
- Version bump to 0.3.0 (pyproject.toml, cli.py)
- Rewrite README.md with current stats (475 drafts, 713 authors, 501 ideas)
- Add CONTRIBUTING.md with dev setup and code conventions
Blog site:
- Add scripts/build-site.py (markdown → HTML with clean CSS, dark mode, nav)
- Generate static site in docs/blog/ (10 pages)
- Ready for GitHub Pages deployment
Academic paper (paper/main.tex):
- Update all counts: 474→475 drafts, 557→710 authors, 1907→462 ideas, 11→12 gaps
- Add false-positive filtering methodology (113 excluded, 361 relevant)
- Add cross-org convergence analysis (132 ideas, 33% rate)
- Add GDPR compliance gap to gap table
- Add LLM-as-judge caveats to rating methodology and limitations
- Add FIPA, IEEE P3394, W3C WoT to related work with bibliography entries
- Fix safety ratio to show monthly variation (1.5:1 to 21:1)
Pipeline:
- Fetch 1 new draft (475 total), 3 new authors (713 total)
- Fix 16 ruff lint errors across test files
- All 106 tests pass
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Blog posts (all 10 files updated):
- Update all counts to match DB: 434 drafts, 557 authors, 419 ideas, 11 gaps
- Fix EU AI Act timeline to August 2026 (5 months, not 18)
- Reframe growth claim from "36x" to actual monthly figures (5→61→85)
- Add safety ratio nuance (1.5:1 to 21:1 monthly variation)
- Fix composite scores (4.8→4.75, 4.6→4.5)
- Add OAuth/GDPR consent distinction (Art. 6(1)(a), Art. 28)
- Add EU AI Act Annex III + MDR context to hospital scenario
- Add FIPA, IEEE P3394, eIDAS 2.0 references
- Add GDPR gap paragraph (DPIA, erasure, portability, purpose limitation)
- Rewrite Post 04 gap table to match actual DB gap names
Methodology:
- Expand methodology.md: pipeline docs, limitations, related work
- Add LLM-as-judge caveats and explicit rating rubric to analyzer.py
- Add clustering threshold rationale to embeddings.py
- Add gap analysis grounding notes to analyzer.py
- Add Limitations section to Post 07
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Tighten idea extraction prompts (1-4 ideas, no sub-features) reducing
1,907 ideas to 468 across 434 drafts (78% reduction)
- Add embedding-based dedup (ietf dedup-ideas) for same-draft similarity
- Add novelty scoring (ietf ideas score) and filtering (ietf ideas filter)
using Claude to rate ideas 1-5, removing 49 generic building blocks
- Final count: 419 high-quality ideas (avg 1.1/draft)
- Web UI: gap explorer with live draft generation and pre-generated demos
- Web UI: D3.js author collaboration network (498 nodes, 1142 edges,
68 clusters, org filtering, interactive zoom/pan)
- Academic paper: 15-page LaTeX workshop paper analyzing the 434-draft
AI agent standards landscape
- Save improvement ideas backlog to data/reports/improvement-ideas.md
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Fixed W3C fetcher to paginate /specifications endpoint (group
endpoints use type prefixes like cg/, wg/ that weren't in config)
- Fetched 72 new IETF drafts + 1 W3C spec, all analyzed and embedded
- Regenerated dashboard with updated data
- Total: 434 docs, 11 gaps, 1907 ideas
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Includes all draft metadata, full text, Claude ratings (cached),
and nomic-embed-text embeddings. This is the expensive data —
~114k tokens of Claude analysis + 260 Ollama embeddings.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Python CLI tool that fetches AI/agent-related Internet-Drafts from the IETF
Datatracker, rates them using Claude, generates embeddings via Ollama for
similarity/clustering, and produces markdown reports.
Features:
- Fetch drafts by keyword from Datatracker API with full text download
- Batch analysis with Claude (token-optimized, responses cached in SQLite)
- Embedding-based similarity search and overlap cluster detection
- Reports: overview, landscape by category, overlap clusters, weekly digest
- SQLite with FTS5 for full-text search across 260 tracked drafts
Initial analysis of 260 drafts reveals OAuth agent auth (13 drafts) and
agent gateway/collaboration (10 drafts) as the most crowded clusters,
while AI safety/alignment is underserved with the highest quality scores.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
