Commit Graph

5 Commits

Author SHA1 Message Date
45cb13fbe8 feat: add IETF landscape paper source (LaTeX + BibTeX + Makefile)
New LaTeX paper analyzing the AI-agent standardization landscape across
IETF Internet-Drafts. Includes bibliography, updated Makefile for
pdflatex+bibtex build, and gitignore entries for build artifacts.
2026-04-12 12:43:15 +00:00
e247bfef8f Run pipeline, write Post 08, commit untracked files
Pipeline:
- Extract ideas for 38 new drafts → 462 ideas total
- Convergence analysis: 132 cross-org convergent ideas (33% rate)
- Fetch authors for 102 drafts → 709 authors (up from 403)
- Refresh gap analysis: 12 gaps across full 474-draft corpus
- Update verified counts with new totals

Post 08:
- Complete rewrite of "Agents Building the Agent Analysis" (2,953 words)
- Covers 3 phases: writing team → review cycle → fix cycle
- Meta-irony table mapping team coordination to IETF gap names
- Specific examples from dev journal (SQL injection, consent conflation, ideas mismatch)

Untracked files committed:
- scripts/: backfill-wg-names, classify-unrated, compare-classifiers, download-relevant-text, run-webui
- src/ietf_analyzer/classifier.py: two-stage Ollama classifier
- src/webui/: analytics (GDPR-compliant), auth, obsidian_export
- tests/test_obsidian_export.py (10 tests)
- data/reports/: wg-analysis, generated draft for gap #37

Housekeeping:
- .gitignore: exclude LaTeX artifacts, stale DBs, analytics.db

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-08 15:31:30 +01:00
be9cf9c5d9 v0.2.0: visualizations, interactive browser, arXiv paper, gap analysis
New features:
- 12 interactive visualizations (ietf viz): t-SNE landscape, similarity
  heatmap, score distributions, timeline, bubble explorer, radar charts,
  author network graph, category treemap, quality vs overlap, org bar chart,
  ideas chart, and interactive draft browser
- Interactive draft browser (browser.html): filterable by category, keyword,
  score sliders with sortable table and expandable detail rows
- arXiv paper (paper/main.tex): 13-page manuscript with all findings
- Gap analysis: 12 identified under-addressed areas
- Author network: collaboration graph, org contributions, cross-org analysis
- Draft generation from gaps (ietf draft-gen)
- Auto-load .env for API keys (python-dotenv)

New modules: visualize.py, authors.py, draftgen.py
New reports: timeline, overlap-matrix, authors, gaps
New deps: plotly, matplotlib, seaborn, scipy, scikit-learn, networkx, python-dotenv

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-28 13:37:55 +01:00
f44f9265bd Add SQLite database with 260 analyzed drafts
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>
2026-02-28 00:49:18 +01:00
6771a4c235 IETF Draft Analyzer v0.1.0 — track, categorize, and rate AI/agent drafts
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>
2026-02-28 00:36:45 +01:00