conductor(track): init video_analysis_campaign_2_20260627 (4 AI videos, 3-pass)
Umbrella track for the second video analysis research campaign. 4 videos: (1) Reinventing Entropy / Compression is Intelligence, (2) LeCun World Models, (3) LeCun's Bet Against LLMs, (4) Recursive Self-Improvement. Follows the established 3-pass pattern from the prior 12-video campaign (Pass 1: extract via scripts/video_analysis/ pipeline, Pass 2: deobfuscate via lexicon v2, Pass 3: project to C11/Python via the C11 reference). Sibling to Campaign A (directive_hotswap_harness_20260627). Cross-campaign: video 1 (entropy/compression) is most directly relevant to the directive encoding question. Videos 2-3 (LeCun) inform how LLMs model directive intent. Video 4 is the meta-question the directive harness addresses. This plan covers Phase 0 (umbrella setup) + Phase 1 (Pass 1 reports) + Phase 2 (synthesis) + Phase 3 (checkpoint). Pass 2/3 plans are authored as sub-tracks once Pass 1 ships.
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{
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"track_id": "video_analysis_campaign_2_20260627",
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"name": "Video Analysis Campaign 2 (4 AI Videos, 3-Pass)",
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"status": "active",
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"branch": "master",
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"created": "2026-06-27",
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"owner": "Tier 1 (initialized); implementation delegated to Tier 2/3.",
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"blocked_by": [],
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"blocks": ["video_analysis_2_pass_2_deob (future)", "video_analysis_2_pass_3_projection (future)"],
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"scope": {
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"new_files": [
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"conductor/tracks/video_analysis_2_entropy_compression_20260627/ (child; Pass 1 report + artifacts)",
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"conductor/tracks/video_analysis_2_lecun_world_models_20260627/ (child)",
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"conductor/tracks/video_analysis_2_lecun_bet_against_llms_20260627/ (child)",
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"conductor/tracks/video_analysis_2_recursive_self_improvement_20260627/ (child)",
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"conductor/tracks/video_analysis_2_synthesis_20260627/ (child; cross-video synthesis)",
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"docs/reports/TRACK_COMPLETION_video_analysis_campaign_2_20260627.md (end-of-campaign closeout)"
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],
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"modified_files": [],
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"deleted_files": []
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},
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"estimated_effort": {
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"method": "scope (per workflow.md Tier 1 Track Initialization Rules. NO day estimates.)",
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"phase_0": "3 steps: verify pipeline + scaffold child tracks + commit",
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"phase_1": "5 steps: 4 per-video Pass 1 reports + commit",
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"phase_2": "2 steps: synthesis report + commit",
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"phase_3": "3 steps: verify + user review gate + checkpoint commit"
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},
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"verification_criteria": [
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"G1: 4 Pass 1 deep-dive reports exist, each >=1,000 LOC",
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"G2: Pass 2 deobfuscation applied to all 4 (future sub-track; not part of this plan)",
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"G3: Pass 3 C11/Python projection for all 4 (future sub-track; not part of this plan)",
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"G4: Cross-video synthesis report exists, connecting the 4 reports + Campaign A insights",
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"G5: End-of-campaign closeout report exists"
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],
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"regressions_and_pre_existing_failures": [],
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"pre_existing_failures_remaining": [],
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"deferred_to_followup_tracks": [
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{
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"title": "Pass 2: Deobfuscation",
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"description": "Apply lexicon v2 to all 4 videos. May produce lexicon v3 corrections if new notation surfaces (JEPA, bootstrapping).",
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"track_status": "not yet initialized; authored after Pass 1 ships"
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},
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{
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"title": "Pass 3: C11/Python Projection",
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"description": "Project each video's deobfuscated content to C11 or Python code in the user's idiomatic style.",
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"track_status": "not yet initialized; authored after Pass 2 ships"
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},
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{
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"title": "Lexicon v3 patch (conditional)",
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"description": "Only if the 4 new videos surface notation the lexicon v2 doesn't cover.",
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"track_status": "conditional; depends on Pass 2 findings"
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}
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],
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"risk_register": [
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{
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"id": "R1",
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"description": "yt-dlp fails for one or more videos (oEmbed 401 or geo-restriction)",
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"likelihood": "low",
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"impact": "Pass 1 report for that video cannot be produced via the pipeline",
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"mitigation": "the prior campaign had 2 oEmbed failures but yt-dlp still worked; if yt-dlp fails, alternative acquisition (manual download, alternative URL) is a manual fallback"
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},
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{
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"id": "R2",
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"description": "Video transcripts are low quality (auto-generated, no punctuation)",
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"likelihood": "medium",
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"impact": "Pass 1 report quality is degraded; Pass 2 deobfuscation has less to work with",
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"mitigation": "the pipeline's OCR step supplements the transcript with keyframe text; if both are low quality, manual transcript correction is a user action"
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},
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{
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"id": "R3",
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"description": "Lexicon v2 doesn't cover new notation (JEPA, bootstrapping, world-model latent dynamics)",
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"likelihood": "medium",
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"impact": "Pass 2 deobfuscation produces gaps; a lexicon v3 patch track is needed",
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"mitigation": "the v2 patch track precedent (video_analysis_deob_lexicon_v2_20260623) shows the correction process works; a v3 patch is a known pattern"
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}
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],
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"campaign_context": {
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"campaign_name": "Video Analysis Campaign 2",
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"prior_campaign": "video_analysis_campaign_20260621 (12 videos; closed 2026-06-23)",
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"sibling_campaign": "Directive Encoding Campaign (Campaign A; directive_hotswap_harness_20260627)",
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"cross_campaign_relationship": "Intellectual cross-pollination. Video 1 (entropy/compression) is most directly relevant to directive encoding. Videos 2-3 (LeCun) inform whether directive encoding should account for non-autoregressive architectures. Video 4 (recursive self-improvement) is the meta-question the directive harness addresses."
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}
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}
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# Plan: Video Analysis Campaign 2 (4 AI Videos, 3-Pass)
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Track: `video_analysis_campaign_2_20260627`
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Branch: master (research track; no code changes, no test changes — pure analysis + reports)
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Spec: `conductor/tracks/video_analysis_campaign_2_20260627/spec.md`
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This is an umbrella track. The plan covers Phase 0 (umbrella setup) + Phase 1 (Pass 1 information extraction for 4 videos). Pass 2 + Pass 3 plans are authored as sub-tracks once Pass 1 ships.
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---
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## Phase 0: Umbrella Setup
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Focus: Verify the pipeline works for the 4 new videos; scaffold the child track directories.
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- [ ] **Step 0.1: Verify the video acquisition pipeline works for all 4 videos**
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**WHAT:** Run `scripts/video_analysis/download_video.py` for each of the 4 URLs. Verify the videos download successfully via `yt-dlp`. Some videos may fail oEmbed (as the prior campaign experienced with 2 E-cluster videos); `yt-dlp` may still work.
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**HOW:**
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```bash
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uv run python -m scripts.video_analysis.download_video "https://youtu.be/l6DKRf-fAAM" --slug entropy_compression
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uv run python -m scripts.video_analysis.download_video "https://www.youtube.com/watch?v=72Xj8k5WQX4" --slug lecun_world_models
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uv run python -m scripts.video_analysis.download_video "https://youtu.be/kYkIdXwW2AE" --slug lecun_bet_against_llms
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uv run python -m scripts.video_analysis.download_video "https://youtu.be/t7_ZXgfJVG8" --slug recursive_self_improvement
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```
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**VERIFY:** 4 video files downloaded. If any fail, document the failure + alternative acquisition method.
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- [ ] **Step 0.2: Scaffold the 4 child track directories**
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**WHERE:** `conductor/tracks/video_analysis_2_<slug>_20260627/` (4 directories)
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**WHAT:** Create the directories with placeholder spec.md + state.toml files. Each child track is a Pass 1 report producer.
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- [ ] **Step 0.3: Commit the umbrella setup**
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```bash
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git add conductor/tracks/video_analysis_campaign_2_20260627/ conductor/tracks/video_analysis_2_*/
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git commit -m "conductor(track): scaffold video_analysis_campaign_2_20260627 (umbrella + 4 children)"
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```
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---
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## Phase 1: Pass 1 — Information Extraction (4 Videos)
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Focus: Produce 4 deep-dive reports using the existing pipeline. Each video is a child track executed independently.
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- [ ] **Step 1.1: Video 1 — entropy_compression (Reinventing Entropy | Compression is Intelligence Part 1)**
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**URL:** https://youtu.be/l6DKRf-fAAM
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**Slug:** `entropy_compression`
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**Cluster:** A (compression/entropy)
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**Child track:** `conductor/tracks/video_analysis_2_entropy_compression_20260627/`
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**Steps:**
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1. Download video (if not already done in Phase 0).
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2. Extract transcript via `scripts/video_analysis/extract_transcript.py`.
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3. Extract keyframes via `scripts/video_analysis/extract_keyframes.py`.
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4. OCR keyframes via `scripts/video_analysis/ocr_frames.py`.
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5. Synthesize report via `scripts/video_analysis/synthesize_report.py`.
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6. Write `report.md` (1,000-10,000 LOC) — lossless preservation of the video's content.
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**Expected content:** Shannon entropy, Kolmogorov complexity, compression as intelligence, the relationship between compression and prediction. This video is the most directly relevant to Campaign A (directive encoding = compression of instructions).
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- [ ] **Step 1.2: Video 2 — lecun_world_models (Yann LeCun: World Models)**
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**URL:** https://www.youtube.com/watch?v=72Xj8k5WQX4
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**Slug:** `lecun_world_models`
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**Cluster:** B (world models)
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**Child track:** `conductor/tracks/video_analysis_2_lecun_world_models_20260627/`
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**Steps:** Same pipeline as Step 1.1.
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**Expected content:** LeCun's world model architecture, JEPA (Joint Embedding Predictive Architecture), planning via latent dynamics, the distinction between generative models and predictive models. Relevant to how LLMs model directive intent.
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- [ ] **Step 1.3: Video 3 — lecun_bet_against_llms (LeCun's $1B Bet Against LLMs [Part 1])**
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**URL:** https://youtu.be/kYkIdXwW2AE
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**Slug:** `lecun_bet_against_llms`
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**Cluster:** B (world models)
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**Child track:** `conductor/tracks/video_analysis_2_lecun_bet_against_llms_20260627/`
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**Steps:** Same pipeline.
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**Expected content:** LeCun's critique of LLMs, autoregressive limitations, the path toward reasoning systems, world models as the next AI revolution. Relevant to whether directive encoding is about pattern-matching (LLM) or reasoning (world model).
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- [ ] **Step 1.4: Video 4 — recursive_self_improvement (Recursive Self-Improvement)**
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**URL:** https://youtu.be/t7_ZXgfJVG8
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**Slug:** `recursive_self_improvement`
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**Cluster:** C (meta-AI)
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**Child track:** `conductor/tracks/video_analysis_2_recursive_self_improvement_20260627/`
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**Steps:** Same pipeline.
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**Expected content:** Recursive self-improvement, alignment, bootstrapping intelligence. The meta-question: can better directive encodings be discovered iteratively? The directive hot-swap harness IS a recursive self-improvement tool for directive encoding.
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- [ ] **Step 1.5: Commit Pass 1 reports**
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```bash
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git add conductor/tracks/video_analysis_2_*/report.md
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git commit -m "feat(video_analysis): Pass 1 complete — 4 deep-dive reports (entropy_compression, lecun_world_models, lecun_bet_against_llms, recursive_self_improvement)"
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```
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---
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## Phase 2: Cross-Video Synthesis (Pass 1)
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Focus: Connect the 4 reports to each other and to the prior campaign's themes.
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- [ ] **Step 2.1: Write the synthesis report**
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**WHERE:** `conductor/tracks/video_analysis_2_synthesis_20260627/report.md`
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**WHAT:**
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- Theme matrix: which videos touch which themes (compression, world models, self-improvement, directive encoding).
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- Concept map: how the 4 videos' concepts relate.
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- Connection to the prior campaign: which of the 12 prior videos share themes with these 4 new ones (especially `entropy_epiplexity` for video 1, `cs229_building_llms` for videos 2-3).
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- Cross-campaign insights: what the video analysis suggests for Campaign A (directive encoding). Specifically: does the entropy/compression video suggest a principled way to measure directive encoding efficiency? Do LeCun's world-model ideas suggest directive encoding should account for non-autoregressive architectures?
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- [ ] **Step 2.2: Commit the synthesis**
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```bash
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git add conductor/tracks/video_analysis_2_synthesis_20260627/
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git commit -m "feat(video_analysis): Pass 1 synthesis — 4-video cross-reference + Campaign A insights"
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```
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---
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## Phase 3: End-of-Pass-1 Checkpoint
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Focus: Verify Pass 1 is complete; gate Pass 2 (deobfuscation).
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- [ ] **Step 3.1: Verify all 4 reports exist + meet the LOC threshold**
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```bash
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for f in conductor/tracks/video_analysis_2_*/report.md; do
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wc -l "$f"
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done
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```
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Expected: 4 files, each ≥1,000 LOC.
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- [ ] **Step 3.2: Present Pass 1 results to the user**
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Report: 4 reports produced, synthesis produced, key themes identified. PAUSE for user review before Pass 2 begins.
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**Pass 2 (deobfuscation) and Pass 3 (C11/Python projection) plans are authored as sub-tracks once Pass 1 is approved by the user.** The user may need to gather deobfuscation samples (same as the prior campaign's warmup) before Pass 2 starts.
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- [ ] **Step 3.3: Commit the checkpoint**
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```bash
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git add conductor/tracks/video_analysis_campaign_2_20260627/state.toml
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git commit -m "conductor(checkpoint): video_analysis_campaign_2 Pass 1 complete — awaiting user review before Pass 2"
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```
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# Track Specification: Video Analysis Campaign 2 (4 AI Videos, 3-Pass)
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## Overview
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A research campaign analyzing 4 new AI-related YouTube videos using the established 3-pass architecture from the previous 12-video campaign (Pass 1: extract → Pass 2: deobfuscate → Pass 3: project to C11/Python). The campaign reuses the existing lexicon v2 + C11 reference from the prior campaign.
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The 4 videos share a theme — compression, entropy, world models, and recursive self-improvement — that is directly relevant to the directive-encoding research in Campaign A (the directive hot-swap harness). The two campaigns are siblings: intellectual cross-pollination, no hard dependency, can run in parallel.
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**This spec covers the umbrella track.** The per-video child tracks (Pass 1 reports) and the deobfuscation sub-tracks (Pass 2 + Pass 3) are initialized as children once the umbrella is approved.
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## The 4 Videos
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| # | Title | URL | Cluster | Topic |
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|---|---|---|---|---|
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| 1 | Reinventing Entropy \| Compression is Intelligence Part 1 | https://youtu.be/l6DKRf-fAAM | A (compression/entropy) | Shannon entropy, compression as intelligence, Kolmogorov complexity |
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| 2 | Yann LeCun: World Models: Enabling the next AI revolution | https://www.youtube.com/watch?v=72Xj8k5WQX4 | B (world models) | LeCun's world model architecture; JEPA; planning via latent dynamics |
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| 3 | Yann LeCun's $1B Bet Against LLMs [Part 1] | https://youtu.be/kYkIdXwW2AE | B (world models) | LeCun's critique of LLMs; autoregressive limitations; path toward reasoning |
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| 4 | Recursive Self-Improvement | https://youtu.be/t7_ZXgfJVG8 | C (meta-AI) | Recursive self-improvement; alignment; bootstrapping intelligence |
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**Cluster assignment:**
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- **A (compression/entropy):** video 1 — directly relevant to the directive-encoding question (how do you compress information for an LLM?)
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- **B (world models):** videos 2-3 — LeCun's world-model work informs how LLMs model directive intent and whether alternative architectures change the encoding question
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- **C (meta-AI):** video 4 — recursive self-improvement is the meta-question of whether better directive encodings can be discovered iteratively
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## Current State Audit (as of master `03c7cfd5`)
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### Already Implemented (from the prior campaign — DO NOT re-implement)
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- **`scripts/video_analysis/` pipeline** (7 modules): `download_video.py`, `extract_transcript.py`, `extract_keyframes.py`, `ocr_frames.py`, `synthesize_report.py`, `error_types.py`, `__init__.py`. These are the reusable tooling from the prior campaign. Pass 1 reuses them directly.
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- **Lexicon v2** (from `video_analysis_deob_lexicon_v2_20260623`): the codified deobfuscation spec with 76 terms, the 5 load-bearing rules (Boundedness, Form-anchor, Etymology, Lossless, Encoding-explicit), the constructive type-theoretic foundation, and the per-language `<<` / `>>` rendering. Pass 2 starts from v2; may produce v3 corrections if the new videos surface notation the lexicon doesn't cover.
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- **C11 reference** (from `video_analysis_deob_c11_reference_20260623`): the user's idiomatic C11 style (byte-width types, underscore-suffixed modifiers, hand-rolled DSL, memory ordering vocabulary, slice + arena, design-doc headers). Pass 3 uses this as the projection target.
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- **Pass 3 projection pattern** (from `video_analysis_deob_c11_reference_20260623` + `pass_3_c11_python_projection_20260623`): per-video deliverables = C11 (.c + .h) or Python (.py) + 3-4 markdown docs (translation, decoder, notes). 4 + 3 verification criteria per the v2 lexicon.
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- **The 3-pass architecture** (documented in `docs/reports/2026-06-15/CAMPAIGN_CLOSE_OUT_video_analysis_20260621.md`): Pass 1 captures raw content losslessly; Pass 2 applies the lexicon; Pass 3 projects to code. The v2 patch + C11 reference are sub-tracks between Pass 2 and Pass 3.
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### Gaps to Fill (This Campaign's Scope)
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- **GAP-1: No Pass 1 reports for the 4 new videos.** The prior campaign analyzed 12 videos; these 4 are new. Pass 1 produces 4 deep-dive reports (one per video) using the existing pipeline.
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- **GAP-2: No Pass 2 deobfuscation for the 4 new videos.** The lexicon v2 must be applied to each video's content. May produce lexicon v3 corrections if the new videos surface notation the lexicon doesn't cover (e.g., LeCun's JEPA terminology, recursive self-improvement's bootstrapping notation).
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- **GAP-3: No Pass 3 C11/Python projection for the 4 new videos.** Each video's deobfuscated content must be projected to C11 or Python code in the user's idiomatic style.
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- **GAP-4: No cross-video synthesis.** The prior campaign had a synthesis track (`video_analysis_synthesis_20260621`) that cross-referenced the 12 reports. This campaign should produce a synthesis cross-referencing the 4 new reports + connecting to the prior campaign's themes.
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### Relationship to Campaign A (Directive Hot-Swap Harness)
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The two campaigns share a theme ("how do you encode information densely for an LLM?") but are tracked and executed independently:
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- **Video 1 (entropy/compression)** provides theoretical grounding for information density. The directive-encoding question IS a compression question: what is the minimal token-cost encoding of a directive that maintains LLM compliance?
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- **Videos 2-3 (LeCun world models)** inform how LLMs model directive intent. If LLMs are autoregressive pattern-matchers (LeCun's critique), then directive encoding is about pattern-matching, not reasoning. If world models are the path forward, directive encoding may need to account for non-autoregressive architectures.
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- **Video 4 (recursive self-improvement)** is the meta-question: can better directive encodings be discovered iteratively? The directive hot-swap harness IS a recursive self-improvement tool for directive encoding.
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Insights from the video analysis may surface alternative encoding strategies to test in Campaign A's harness. The harness's design (preset as bill-of-materials, variant as alternative encoding) mirrors the video campaign's deobfuscation pass (same content, different encoding).
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## Goals
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- **G1.** 4 Pass 1 deep-dive reports (one per video, 1,000-10,000 LOC each) produced via the existing `scripts/video_analysis/` pipeline.
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- **G2.** Pass 2 deobfuscation applied to all 4 videos using lexicon v2. Lexicon v3 corrections produced if the new videos surface notation the lexicon doesn't cover.
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- **G3.** Pass 3 C11/Python projection for all 4 videos (per-video deliverables: C11 .c + .h or Python .py + 3-4 markdown docs).
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- **G4.** A cross-video synthesis report connecting the 4 new reports to each other and to the prior campaign's themes.
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- **G5.** End-of-campaign closeout report documenting what was done, key insights, and any cross-campaign insights relevant to Campaign A.
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## Functional Requirements
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### FR1: Pass 1 — Information Extraction
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- Use `scripts/video_analysis/download_video.py` to acquire each video via `yt-dlp`.
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- Use `scripts/video_analysis/extract_transcript.py` to extract the transcript.
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||||
- Use `scripts/video_analysis/extract_keyframes.py` + `scripts/video_analysis/ocr_frames.py` to extract keyframe images + OCR text.
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- Use `scripts/video_analysis/synthesize_report.py` to synthesize the deep-dive report.
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- Each report preserves the source content losslessly (no deobfuscation yet — that's Pass 2).
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- Per-video deliverable: `report.md` (1,000-10,000 LOC) + supporting artifacts (transcript, keyframes, OCR).
|
||||
- **Video slug naming:** `entropy_compression` (video 1), `lecun_world_models` (video 2), `lecun_bet_against_llms` (video 3), `recursive_self_improvement` (video 4).
|
||||
|
||||
### FR2: Pass 2 — Deobfuscation
|
||||
|
||||
- Apply lexicon v2 to each video's Pass 1 report.
|
||||
- Per-video deliverables: translation (3-column: original → deobfuscated → rationale) + replacement (the deobfuscated content) + decoder (the notation mapping).
|
||||
- 4 + 4 verification criteria per the v2 lexicon (lossless, bounded, constructively typed, etymology-cited + the 4 additional from the apply phase).
|
||||
- If a video surfaces notation the lexicon doesn't cover: produce lexicon v3 corrections (L-codes) + update `terms_catalog.md`.
|
||||
- **Expected new notation:** LeCun's JEPA (Joint Embedding Predictive Architecture), the world-model latent dynamics vocabulary, recursive self-improvement's bootstrapping notation.
|
||||
|
||||
### FR3: Pass 3 — C11/Python Projection
|
||||
|
||||
- Project each video's deobfuscated content to C11 (.c + .h) or Python (.py) in the user's idiomatic style.
|
||||
- Use the C11 reference (`video_analysis_deob_c11_reference_20260623`) as the style guide.
|
||||
- Per-video deliverables: C11 or Python code + 3-4 markdown docs (translation, decoder, notes).
|
||||
- Per-language `<<` / `>>` rendering (much_less / much_greater / weakly_coupled with tolerance).
|
||||
- Encoding placeholder scheme (float / integer / Scalar / float64).
|
||||
- Code may or may not run (per user 2026-06-23: "code may or may not run").
|
||||
|
||||
### FR4: Cross-Video Synthesis
|
||||
|
||||
- A synthesis report connecting the 4 new reports to each other.
|
||||
- Theme matrix: which videos touch which themes (compression, world models, self-improvement, directive encoding).
|
||||
- Concept map: how the 4 videos' concepts relate.
|
||||
- Connection to the prior campaign: which of the 12 prior videos share themes with these 4 new ones.
|
||||
- Cross-campaign insights: any insights relevant to Campaign A (directive encoding).
|
||||
|
||||
### FR5: End-of-Campaign Closeout
|
||||
|
||||
- A closeout report following the precedent of `docs/reports/2026-06-15/CAMPAIGN_CLOSE_OUT_video_analysis_20260621.md`.
|
||||
- Documents: what was done, key decisions, final statistics, open questions.
|
||||
- Cross-campaign insights: what the video analysis suggests for directive encoding (Campaign A).
|
||||
|
||||
## Non-Functional Requirements
|
||||
|
||||
- **Lossless preservation:** Pass 1 artifacts must NOT be over-summarized (data cascades to Pass 2/3). Per the prior campaign's "load-bearing directive."
|
||||
- **Lexicon v2 as starting point:** Pass 2 starts from v2. If v3 corrections are needed, they are produced as a patch track (same pattern as `video_analysis_deob_lexicon_v2_20260623`).
|
||||
- **User-led gating:** Pass 2 may require the user to gather deobfuscation samples (same as the prior campaign's warmup). Pass 3 may require the user to articulate "own caveats" before the projection starts. These are user-action gates, not agent-action gates.
|
||||
- **Reusable tooling:** the existing `scripts/video_analysis/` pipeline is reused without modification. If the pipeline needs changes (e.g., new ocr engine, new transcript API), that's a separate tooling track.
|
||||
|
||||
## Architecture Reference
|
||||
|
||||
- **`docs/reports/2026-06-15/CAMPAIGN_CLOSE_OUT_video_analysis_20260621.md`** — the prior campaign's closeout (the pattern this campaign follows).
|
||||
- **`scripts/video_analysis/`** — the existing pipeline (7 modules; reused for Pass 1).
|
||||
- **The lexicon v2** (from `video_analysis_deob_lexicon_v2_20260623`) — the deobfuscation substrate for Pass 2.
|
||||
- **The C11 reference** (from `video_analysis_deob_c11_reference_20260623`) — the projection target for Pass 3.
|
||||
- **`docs/superpowers/specs/2026-06-27-directive-hotswap-harness-design.md`** → now at `conductor/tracks/directive_hotswap_harness_20260627/spec.md` — the sibling campaign (Campaign A).
|
||||
|
||||
## Out of Scope
|
||||
|
||||
- **Modifying the existing `scripts/video_analysis/` pipeline.** If the pipeline needs changes, that's a separate tooling track.
|
||||
- **Re-analyzing the 12 prior videos.** The prior campaign is closed.
|
||||
- **Building the directive hot-swap harness.** That's Campaign A (separate track, separate spec).
|
||||
- **Authoring alternative directive encodings (v2+).** That's a future track in Campaign A.
|
||||
- **Automated compliance testing of directive encodings.** Future track.
|
||||
|
||||
## Track Structure (Children)
|
||||
|
||||
This is the umbrella track. Children are initialized once the umbrella is approved:
|
||||
|
||||
- **Pass 1 children (4):** `video_analysis_2_entropy_compression_20260627`, `video_analysis_2_lecun_world_models_20260627`, `video_analysis_2_lecun_bet_against_llms_20260627`, `video_analysis_2_recursive_self_improvement_20260627`
|
||||
- **Pass 1 synthesis (1):** `video_analysis_2_synthesis_20260627`
|
||||
- **Pass 2 sub-tracks (TBD):** umbrella + warmup (if needed) + apply. Initialized after Pass 1 ships.
|
||||
- **Pass 3 sub-tracks (TBD):** initialized after Pass 2 ships.
|
||||
- **Lexicon v3 patch (conditional):** only if the new videos surface notation the lexicon doesn't cover.
|
||||
- **End-of-campaign closeout (1):** `video_analysis_campaign_2_closeout_20260627`
|
||||
@@ -0,0 +1,58 @@
|
||||
# Track state for video_analysis_campaign_2_20260627
|
||||
# Initialized by Tier 1 Orchestrator on 2026-06-27.
|
||||
# Umbrella track for the 4-video research campaign (Pass 1 only; Pass 2/3 are sub-tracks).
|
||||
|
||||
[meta]
|
||||
track_id = "video_analysis_campaign_2_20260627"
|
||||
name = "Video Analysis Campaign 2 (4 AI Videos, 3-Pass)"
|
||||
status = "active"
|
||||
current_phase = 0
|
||||
last_updated = "2026-06-27"
|
||||
|
||||
[blocked_by]
|
||||
# None. Research track; no code changes, no test changes.
|
||||
|
||||
[blocks]
|
||||
video_analysis_2_pass_2_deob = "planned (future; authored after Pass 1 ships)"
|
||||
video_analysis_2_pass_3_projection = "planned (future; authored after Pass 2 ships)"
|
||||
|
||||
[phases]
|
||||
phase_0 = { status = "pending", checkpointsha = "", name = "Umbrella Setup (verify pipeline + scaffold child tracks)" }
|
||||
phase_1 = { status = "pending", checkpointsha = "", name = "Pass 1 — Information Extraction (4 per-video reports)" }
|
||||
phase_2 = { status = "pending", checkpointsha = "", name = "Cross-Video Synthesis (Pass 1)" }
|
||||
phase_3 = { status = "pending", checkpointsha = "", name = "End-of-Pass-1 Checkpoint (verify + user review gate)" }
|
||||
|
||||
[tasks]
|
||||
t0_1 = { status = "pending", commit_sha = "", description = "Verify yt-dlp pipeline works for all 4 URLs" }
|
||||
t0_2 = { status = "pending", commit_sha = "", description = "Scaffold 4 child track directories + synthesis child" }
|
||||
t0_3 = { status = "pending", commit_sha = "", description = "Commit umbrella setup" }
|
||||
t1_1 = { status = "pending", commit_sha = "", description = "Video 1: entropy_compression (Reinventing Entropy | Compression is Intelligence Part 1)" }
|
||||
t1_2 = { status = "pending", commit_sha = "", description = "Video 2: lecun_world_models (Yann LeCun: World Models)" }
|
||||
t1_3 = { status = "pending", commit_sha = "", description = "Video 3: lecun_bet_against_llms (LeCun's $1B Bet Against LLMs [Part 1])" }
|
||||
t1_4 = { status = "pending", commit_sha = "", description = "Video 4: recursive_self_improvement (Recursive Self-Improvement)" }
|
||||
t1_5 = { status = "pending", commit_sha = "", description = "Commit Pass 1 reports" }
|
||||
t2_1 = { status = "pending", commit_sha = "", description = "Write cross-video synthesis report (theme matrix + concept map + Campaign A insights)" }
|
||||
t2_2 = { status = "pending", commit_sha = "", description = "Commit synthesis" }
|
||||
t3_1 = { status = "pending", commit_sha = "", description = "Verify all 4 reports >= 1,000 LOC" }
|
||||
t3_2 = { status = "pending", commit_sha = "", description = "Present Pass 1 results to user (PAUSE for review before Pass 2)" }
|
||||
t3_3 = { status = "pending", commit_sha = "", description = "Commit checkpoint" }
|
||||
|
||||
[verification]
|
||||
phase_0_complete = false
|
||||
phase_1_complete = false
|
||||
phase_2_complete = false
|
||||
phase_3_complete = false
|
||||
pass_1_reports_count = 0
|
||||
synthesis_complete = false
|
||||
|
||||
[campaign_context]
|
||||
campaign_name = "Video Analysis Campaign 2"
|
||||
prior_campaign = "video_analysis_campaign_20260621 (12 videos; closed 2026-06-23)"
|
||||
sibling_campaign = "Directive Encoding Campaign (Campaign A; directive_hotswap_harness_20260627)"
|
||||
cross_campaign_relationship = "Intellectual cross-pollination. Video 1 (entropy/compression) is most relevant to directive encoding."
|
||||
videos = [
|
||||
{ slug = "entropy_compression", url = "https://youtu.be/l6DKRf-fAAM", cluster = "A" },
|
||||
{ slug = "lecun_world_models", url = "https://www.youtube.com/watch?v=72Xj8k5WQX4", cluster = "B" },
|
||||
{ slug = "lecun_bet_against_llms", url = "https://youtu.be/kYkIdXwW2AE", cluster = "B" },
|
||||
{ slug = "recursive_self_improvement", url = "https://youtu.be/t7_ZXgfJVG8", cluster = "C" },
|
||||
]
|
||||
Reference in New Issue
Block a user