conductor(creikey_dl_cv): Phase 4 Synthesis - report.md (1422 lines, 81KB) + summary.md (~380 words)

conductor/tracks/video_analysis_creikey_dl_cv_20260621/summary.md

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+# Summary: Creikey — DL/CV for Game Developers (BSC 2025)
+
+**Source:** https://youtu.be/yxkUvXs-hoQ
+**Author:** Cameron Wrights (Creikey)
+**Track:** Child #12 of `video_analysis_campaign_20260621` (LAST CHILD)
+**Cluster:** D (Applied / practical)
+**Pass:** 1 of 3 (research-only deep-dive)
+
+---
+
+## One-paragraph synthesis
+
+Creikey (Cameron Wrights, indie game developer and DL hobbyist) presents a practitioner's view of deep learning for game developers at BSC 2025 — the **applied capstone** of the campaign. The talk frames ML as **automatic programming**: the architecture is the language, the training data is the spec, the optimization is the compiler, and the trained model is the program. The speaker built **Dante's Cowboy**, an LLM-controlled NPC game from scratch in C, but never released it because "games are about predicting and understanding systems, and an LLM is an unpredictable black box" — the **composability problem**. This maps to Kumar's FER hypothesis: current LLMs are Fractured Entangled Representations, lacking the Unified Factored Representations needed for compositional game behavior. The talk also discusses John Carmack's pivot to AGI and Python, the LLM vending machine failure (LLM-controlled businesses convinced to stock tungsten cubes at a loss), Grok's recent Arc AGI jump, and the speaker's skepticism of interpretability research. **Backward connections:** cs229 (foundational ML), platonic_intelligence_kumar (composability = FER), free_lunches_levin (bioelectric patterns), brain_counterintuitive (reservoir for NPC), generic_systems_fields (generic systems), neural_dynamics_miller (mixed selectivity for NPC), multiscale_hoffman (trace logic for compositional behavior), cs336_architectures (Transformer architecture).
+
+---
+
+## Three key takeaways
+
+1. **ML is automatic programming** — architecture is the language, training data is the spec, optimization is the compiler. The "vast majority of performance is in numerical calculations to find the program" (GPU compute), not in setup or queueing. This explains why Python dominates.
+2. **The composability problem** — LLMs are great at single tasks but bad at compositional game behavior. The speaker's Dante game (LLM-controlled NPCs) was never released because LLMs are "unpredictable black boxes." Maps to Kumar's FER hypothesis.
+3. **The indie developer epistemic stance** — pragmatic, skeptical, hands-on, honest. Building real systems (Asteris, Dante's Cowboy) is the test. "Everything's probably fine, but sometimes bad things happen."
+
+---
+
+*Pass 2 (de-obfuscation via user's mathematical encoding) to follow.*
+
+---
+
+## CAMPAIGN STATUS
+
+**All 12 children of `video_analysis_campaign_20260621` are now shipped.** Only the synthesis track remains: `video_analysis_synthesis_20260621`.