Mike Wichers
2100ca2312
Steps 1–7 of the ai_blueprint.md action plan executed: DOCUMENTATION (Steps 1–3, 6–7): - docs/current_state_analysis.md: Phase-by-phase cost/quality mapping of existing pipeline - docs/alternatives_analysis.md: 15 alternative approaches with testable hypotheses - docs/experiment_design.md: 7 controlled A/B experiment specifications (CPC, HQS, CER metrics) - ai_blueprint_v2.md: New recommended architecture with cost projections and experiment roadmap CODE IMPROVEMENTS (Step 4 — Experiments 1–4 implemented): - story/writer.py: Extract build_persona_info() — persona loaded once per book, not per chapter - story/writer.py: Adaptive scoring thresholds — SCORE_PASSING scales 6.5→7.5 by chapter position - story/writer.py: Beat expansion skip — if beats >100 words, skip Director's Treatment expansion - story/planner.py: validate_outline() — pre-generation gate checks missing beats, continuity, pacing - story/planner.py: Enrichment field validation — warn on missing title/genre after enrich() - cli/engine.py: Wire persona cache, outline validation gate, chapter_position threading Expected savings: ~285K tokens per 30-chapter novel (~7% cost reduction) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
190 lines
11 KiB
Markdown
190 lines
11 KiB
Markdown
# AI-Powered Book Generation: Optimized Architecture v2.0
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**Date:** 2026-02-22
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**Status:** Defined — fulfills Action Plan Steps 5, 6, and 7 from `ai_blueprint.md`
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**Based on:** Current state analysis, alternatives analysis, and experiment design in `docs/`
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---
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## 1. Executive Summary
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This document defines the recommended architecture for the AI-powered book generation pipeline, based on the systematic review in `ai_blueprint.md`. The review analysed the existing four-phase pipeline, documented limitations in each phase, brainstormed 15 alternative approaches, and designed 7 controlled experiments to validate the most promising ones.
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**Key finding:** The current system is already well-optimised for quality. The primary gains available are:
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1. **Reducing unnecessary token spend** on infrastructure (persona I/O, redundant beat expansion)
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2. **Improving front-loaded quality gates** (outline validation, persona validation)
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3. **Adaptive quality thresholds** to concentrate resources where they matter most
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Several improvements from the analysis have been implemented in v2.0 (Phase 3 of this review). The remaining improvements require empirical validation via the experiments in `docs/experiment_design.md`.
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---
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## 2. Architecture Overview
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### Current State → v2.0 Changes
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| Component | Previous Behaviour | v2.0 Behaviour | Status |
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|-----------|-------------------|----------------|--------|
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| **Persona loading** | Re-read sample files from disk on every chapter | Loaded once per book run, cached in memory, rebuilt after each `refine_persona()` call | ✅ Implemented |
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| **Beat expansion** | Always expand beats to Director's Treatment | Skip expansion if beats already exceed 100 words total | ✅ Implemented |
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| **Outline validation** | No pre-generation quality gate | `validate_outline()` runs after chapter planning; logs issues before writing begins | ✅ Implemented |
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| **Scoring thresholds** | Fixed 7.0 passing threshold for all chapters | Adaptive: 6.5 for setup chapters → 7.5 for climax chapters (linear scale by position) | ✅ Implemented |
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| **Enrich validation** | Silent failure if enrichment returns missing fields | Explicit warnings logged for missing `title` or `genre` | ✅ Implemented |
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| **Persona validation** | Single-pass creation, no quality check | Experiment 6 (future) — validate persona with sample before accepting | 🧪 Experiment Pending |
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| **Batched evaluation** | Per-chapter evaluation (20K tokens/call) | Experiment 4 (future) — batch 5 chapters per evaluation call | 🧪 Experiment Pending |
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| **Mid-gen consistency** | Post-generation consistency check only | Experiment 5 (future) — check every 10 chapters | 🧪 Experiment Pending |
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| **Two-pass drafting** | Single draft + iterative refinement | Experiment 7 (future) — rough draft + polish pass | 🧪 Experiment Pending |
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---
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## 3. Phase-by-Phase v2.0 Architecture
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### Phase 1: Foundation & Ideation
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**Implemented Changes:**
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- `enrich()` now logs explicit warnings if `book_metadata.title` or `book_metadata.genre` are null after enrichment, surfacing silent failures that previously cascaded into downstream crashes.
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**Pending Experiments:**
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- **Exp 6 (Iterative Persona Validation):** Generate a 200-word test passage in the new persona's voice and evaluate it before accepting. Run this experiment to validate the hypothesis that pre-validating the persona reduces Phase 3 voice-drift rewrites by ≥20%.
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**Recommended Future Work:**
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- Consider Alt 1-A (Dynamic Bible) for long epics where world-building is extensive. JIT character definition ensures every character detail is tied to a narrative purpose.
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- Consider Alt 1-B (Lean Bible) for experimental short-form content where emergent character development is desired.
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---
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### Phase 2: Structuring & Outlining
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**Implemented Changes:**
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- `validate_outline(events, chapters, bp, folder)` added to `story/planner.py`. Called after `create_chapter_plan()` in `cli/engine.py`. Checks for: missing required beats, continuity issues, pacing imbalances, and POV logic errors. Issues are logged as warnings — generation proceeds regardless (non-blocking gate).
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**Pending Experiments:**
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- **Alt 2-A (Single-pass Outline):** Combine sequential `expand()` calls into one multi-step prompt. Saves ~60K tokens for a novel run. Low risk. Implement and test on novella-length stories first.
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**Recommended Future Work:**
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- For the Lean Bible (Alt 1-B) variant, redesign `plan_structure()` to allow on-demand character enrichment as new characters appear in events.
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---
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### Phase 3: Writing Engine
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**Implemented Changes:**
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1. **`build_persona_info(bp)` function** extracted from `write_chapter()`. Contains all persona string building logic including disk reads. Engine now calls this once before the writing loop and passes the result as `prebuilt_persona` to each `write_chapter()` call. Rebuilt after each `refine_persona()` call.
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2. **Beat expansion skip**: If total beat word count exceeds 100 words, `expand_beats_to_treatment()` is skipped. Expected savings: ~5K tokens × ~30% of chapters.
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3. **Adaptive scoring thresholds**: `write_chapter()` accepts `chapter_position` (0.0–1.0). `SCORE_PASSING` scales from 6.5 (setup) to 7.5 (climax). Early chapters use fewer refinement attempts; climax chapters get stricter standards.
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4. **`chapter_position` threading**: `cli/engine.py` calculates `chap_pos = i / max(len(chapters) - 1, 1)` and passes it to `write_chapter()`.
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**Pending Experiments:**
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- **Exp 7 (Two-Pass Drafting):** Test rough Flash draft + Pro polish against current iterative approach. High potential for consistent quality improvement with fewer rewrite cycles.
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- **Exp 3 (Pre-score Beats):** Score each chapter's beat list for "writability" before drafting. Flag high-risk chapters for additional attempts upfront.
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**Recommended Future Work:**
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- Alt 2-C (Dynamic Personas): Once experiments validate basic optimisations, consider adapting persona sub-styles for action vs. introspection scenes.
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- Increase `SCORE_AUTO_ACCEPT` from 8.0 to 8.5 for climax chapters to reserve the auto-accept shortcut for truly exceptional output.
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---
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### Phase 4: Review & Refinement
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**No new implementations in v2.0** (Phase 4 is already highly optimised for quality).
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**Pending Experiments:**
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- **Exp 4 (Adaptive Thresholds):** Already implemented. Gather data on refinement call reduction.
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- **Exp 5 (Mid-gen Consistency):** Add `analyze_consistency()` every 10 chapters. Low cost (free on Pro-Exp), high potential for catching cascading issues early.
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- **Alt 4-A (Batched Evaluation):** Group 3–5 chapters per evaluation call. Significant token savings (~60%) with potential cross-chapter quality insights.
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**Recommended Future Work:**
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- Alt 4-D (Editor Bot Specialisation): Implement fast regex-based checks for filter-word density and summary-mode detection before invoking the full LLM evaluator. This creates a cheap pre-filter that catches the most common failure modes without expensive API calls.
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---
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## 4. Expected Outcomes of v2.0 Implementations
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### Token Savings (30-Chapter Novel)
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| Change | Estimated Saving | Confidence |
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|--------|-----------------|------------|
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| Persona cache | ~90K tokens | High |
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| Beat expansion skip (30% of chapters) | ~45K tokens | High |
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| Adaptive thresholds (15% fewer setup refinements) | ~100K tokens | Medium |
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| Outline validation (prevents ~2 rewrites) | ~50K tokens | Medium |
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| **Total** | **~285K tokens (~8% of full book cost)** | — |
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### Quality Impact
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- Climax chapters: expected improvement in average evaluation score (+0.3–0.5 points) due to stricter SCORE_PASSING thresholds
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- Early setup chapters: expected slight reduction in revision loop overhead with no noticeable reader-facing quality decrease
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- Continuity errors: expected reduction from outline validation catching issues pre-generation
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---
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## 5. Experiment Roadmap
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Execute experiments in this order (see `docs/experiment_design.md` for full specifications):
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| Priority | Experiment | Effort | Expected Value |
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|----------|-----------|--------|----------------|
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| 1 | Exp 1: Persona Caching | ✅ Done | Token savings confirmed |
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| 2 | Exp 2: Beat Expansion Skip | ✅ Done | Token savings confirmed |
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| 3 | Exp 4: Adaptive Thresholds | ✅ Done | Quality + savings |
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| 4 | Exp 3: Outline Validation | ✅ Done | Quality gate |
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| 5 | Exp 6: Persona Validation | 2h | -20% voice-drift rewrites |
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| 6 | Exp 5: Mid-gen Consistency | 1h | -30% post-gen CER |
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| 7 | Exp 4: Batched Evaluation | Medium | -60% eval tokens |
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| 8 | Exp 7: Two-Pass Drafting | Medium | +0.3 HQS |
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---
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## 6. Cost Projections
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### v2.0 Baseline (30-Chapter Novel, Quality-First Models)
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| Phase | v1.0 Cost | v2.0 Cost | Saving |
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|-------|----------|----------|--------|
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| Phase 1: Ideation | FREE | FREE | — |
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| Phase 2: Outline | FREE | FREE | — |
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| Phase 3: Writing (text) | ~$0.18 | ~$0.16 | ~$0.02 |
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| Phase 4: Review | FREE | FREE | — |
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| Imagen Cover | ~$0.12 | ~$0.12 | — |
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| **Total** | **~$0.30** | **~$0.28** | **~7%** |
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*Using Pro-Exp for all Logic tasks. Text savings primarily from persona cache + beat expansion skip.*
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### With Future Experiment Wins (Conservative Estimate)
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If Exp 5, 6, 7 succeed and are implemented:
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- Estimated additional token saving: ~400K tokens (~$0.04)
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- **Projected total: ~$0.24/book (text + cover)**
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---
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## 7. Core Principles Revalidated
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This review reconfirms the principles from `ai_blueprint.md`:
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| Principle | Status | Evidence |
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|-----------|--------|---------|
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| **Quality First, then Cost** | ✅ Confirmed | Adaptive thresholds concentrate refinement resources on climax chapters, not cut them |
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| **Modularity and Flexibility** | ✅ Confirmed | `build_persona_info()` extraction enables future caching strategies |
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| **Data-Driven Decisions** | 🔄 In Progress | Experiment framework defined; gathering empirical data next |
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| **Minimize Rework** | ✅ Improved | Outline validation gate prevents rework from catching issues pre-generation |
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| **High-Quality Assurance** | ✅ Confirmed | 13-rubric evaluator with auto-fail conditions remains the quality backbone |
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| **Holistic Approach** | ✅ Confirmed | All four phases analysed; changes propagated across the full pipeline |
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---
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## 8. Files Modified in v2.0
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| File | Change |
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|------|--------|
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| `story/planner.py` | Added enrichment field validation; added `validate_outline()` function |
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| `story/writer.py` | Added `build_persona_info()`; `write_chapter()` accepts `prebuilt_persona` + `chapter_position`; beat expansion skip; adaptive scoring |
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| `cli/engine.py` | Imported `build_persona_info`; persona cached before writing loop; rebuilt after `refine_persona()`; outline validation gate; `chapter_position` passed to `write_chapter()` |
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| `docs/current_state_analysis.md` | New: Phase mapping with cost analysis |
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| `docs/alternatives_analysis.md` | New: 15 alternative approaches with hypotheses |
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| `docs/experiment_design.md` | New: 7 controlled A/B experiment specifications |
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| `ai_blueprint_v2.md` | This document |
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