- Price extraction: 22,632/22,632 HTML files extracted (100% success). Methods: packageList JSON (72.9%), old JSON (15.2%), dollar fallback (11.2%), HTML span (0.7%). Output:
data/pilot/pilot-prices.csv. - Item clustering: 1,908 unique gigs clustered into 150 service items (TF-IDF + agglomerative, k=150, silhouette=0.114). Output:
data/pilot/gig-items.csv,data/pilot/item-clusters.csv. - AI benchmark dataset: Created
data/ai-benchmarks.csvwith 8 benchmarks (HumanEval, SWE-bench, WMT BLEU, AlpacaEval, Chatbot Arena, FID, GSM8K, Whisper WER) spanning 2017–2025. - IPI construction (cross-sectional): Script
code/11-build-ipi.py— Laspeyres-style index, 9 categories. Revealed platform-wide price inflation masking AI effects. - IPI construction (panel): Script
code/12-panel-ipi.py— Matched-model Jevons/Törnqvist index tracking same-gig prices. Key results:- IPI: 100 (2019Q1) → peak 312 (Q4 2024) → 246 (Q2 2025), −21% from peak in 2025.
- Price elasticity of intelligence: audio β=−0.49 (substitution), writing β=+0.21, coding β=+0.30, marketing β=+0.70, design β=+1.10 (complementarity). All significant p<0.01.
- Novel concept: "shadow deflation" — AI effect masked by platform inflation, visible only as deceleration.
- Full paper drafted: All 8 sections written (abstract, introduction, related work, methods, findings, discussion, limitations, conclusion).
- Self-review and polish: Fixed number inconsistencies (312% → "peaked at 312"), section numbering (8→7 sections), missing data flow explanation (14,938→1,908 gigs), added 4 missing categories to elasticity table, trimmed CPI analogy and survivorship bias redundancy, fixed broken cross-references in related work.
- Key outputs:
data/pilot/panel-ipi.csv,data/pilot/panel-summary.md,data/pilot/panel-elasticity.csv, all drafts indrafts/sections/.
- Phase 1 (CDX filtering) complete: Steps 1.1–1.6 all done.
- Fixed OOM crashes in dedup/filter scripts by switching from in-memory dicts to external sort + streaming.
- Full census: 5.6M unique gigs, 822K unique sellers across 10 categories + uncategorized.
- 60M raw CDX → 22.7M deduped → classified by category → longitudinal filter applied.
- Sampling strategy refined toward CPI-style index:
- User wants to track price impact of AI, weight by transaction volume (like CPI basket).
- Decided to sample at user level (preserves within-seller panel for upskilling analysis).
- Survivorship bias is acceptable — gig disappearance is part of the AI impact signal.
- Wayback Machine coverage bias acknowledged as limitation (over-represents popular gigs).
- Pilot: 500 users sampled (from 48,643 qualifying users with ≥5 monthly snapshots spanning ≥2 years).
- 500 users, 14,938 gigs, 26,603 monthly snapshots.
- Download launched (~5 GB compressed, ~30–45 min).
- Scripts:
code/06c-pilot-longitudinal.py,code/07-pilot-500.py,code/08-download-html.py.
- Key outputs:
data/pilot/pilot-500-manifest.tsv,data/pilot/html/(downloading).
- Added
hajimiprint directive to confirm CLAUDE.md is loaded (helps verify config in VS Code sessions). - Added Philosophy #6: User prompts as first-class test inputs. Instructional prompts about paper content become test entries in
tests/<section>.test.mdunder## User Requirements.
- ~2.5M unique gig URLs on Wayback Machine, 4–20 TB raw (too large for full download).
- Recommended strategy: two-phase filtered download — Tier 1 categories only (writing, coding, design, translation) with 3+ snapshots spanning 2+ years → ~275 GB compressed.
- Report saved to
runs/archive-size-estimation/report.md. - Plan updated:
plans/active/03-fiverr-archive-download.md— Step 1 complete, Step 2 (download) pending.
Data Feasibility Pilot — GO:
- Wayback Machine has 50+ Fiverr snapshots per category spanning 2012–2025.
- Price extraction: 100% success (20/20 pages) via embedded JSON
packageList. - Worker tracking: 6 sellers tracked with 3+ snapshots each. Key finding: froggy92 (architecture) dropped from $50 → $20 (−60%) over 4 years.
- Upwork/Freelancer checked as fallback — not needed; Fiverr is best.
- Plan moved to
plans/completed/01-data-feasibility-pilot.md.
Scoping & Taxonomy — Complete:
- 12-category taxonomy created in
data/task-taxonomy.md(3 priority tiers). - Benchmarks mapped per category with historical data sources verified.
- Related work drafted: ~4k words, 5 subsections, 30+ citations. Covers AI-labor, gig economy evidence, benchmarks, scaling laws, positioning table.
- 5 critique-and-improve iterations run. 18 reviewer simulation items in
tests/related-work.test.md. - Plan moved to
plans/completed/02-scoping-and-taxonomy.md.
Next: Build scraping pipeline, collect benchmark histories, construct panel dataset.
- Converted
paper-plan.md→plans/project-brief.md(reference doc: positioning, structure, risks). - Created two concrete execution plans:
plans/active/01-data-feasibility-pilot.md— Wayback Machine + Fiverr viability with clear pass/fail criteria and decision gate.plans/active/02-scoping-and-taxonomy.md— task taxonomy, benchmark mapping, related work draft.
- Updated
plans/todo.md: 2 active items linking to plans, backlog includes all draft sections. - These two plans can run in parallel.
- Created execution plan:
plans/active/paper-plan.md. - Analyzed model paper (GPTs are GPTs): identified strengths, gaps, and what we must exceed.
- Updated
tests/model-paper.test.mdwith detailed benchmark comparison (10 dimensions). - Plan has 6 phases: Scoping & Lit Review → Pilot → Full Data Collection → Core Analysis → Index & Forecasting → Paper Completion.
- Key innovation: price elasticity of intelligence (continuous, not binary exposure); longitudinal Fiverr data via Wayback Machine; forward-looking IPI under AI scaling scenarios.
- Key risk identified: Wayback Machine coverage — must pilot before committing to full collection.
- Decoupled
CLAUDE.mdinto three files:CLAUDE.md— agent philosophy and operating instructions only.setup.md— agent bootstrapping and session-start checklist.README.md— human-facing project overview and contributor guide.
- Restructured tests into three layers:
tests/master.test.md— cross-section quality criteria (applies to all sections).tests/<section>.test.md— reviewer simulation only (removed model paper comparison from individual sections).tests/model-paper.test.md— standalone model paper benchmark (replaces oldmodel-paper.md).
- Added Philosophy #5: Paper test infrastructure with two lenses (reviewer simulation + model paper comparison).
- Created
tests/directory with per-section test files (*.test.md) mirroringdrafts/sections/. - Created
tests/model-paper.mdfor model paper analysis. - Test files use PASS/FAIL/BLOCKED/N/A status for each critique and quality dimension.
- Clarified human workflow: user primarily edits plans, drafts, and test files; agents handle execution.
- Added Philosophy #4: Plans as first-class artifacts.
- Created
plans/active/,plans/completed/,plans/tech-debt-tracker.md. - Updated
CLAUDE.mdwith plan file format, lifecycle (active → completed), and conventions.
- Created
CLAUDE.mdwith three core principles: minimize interruption, auditable progress, agile process. - Set up drafts infrastructure:
drafts/main.md,drafts/sections/,drafts/render.py. - Created
progress.md(this file) for reverse-chronological audit trail. - Created project directories:
code/,data/,runs/. - Placeholder section files created for paper draft.