Overview
This weekly report aggregates routine reviews from two knowledge vaults:
- AI Research Vault — Agent architecture, RAG experiments, AI infra optimization
- GameDev Vault — UE5 engine analysis, MCP tooling, shader & rendering
Period: 2026-06-29 ~ 2026-07-05 (W27)
AI Research Vault
Theme
Agent 能力建设周：Harness 骨架复现 → RAG 实验大纲 → AI-Infra 备忘录 → 技术雷达更新
Completed Items
| Task | Theme | Quality | Notes |
|---|---|---|---|
| Agent Harness × Game AI paper reading | Agent architecture | B+ | 3 papers + literature map |
| Agent Harness 6-component implementation | Agent framework | B | 24/27 tests passed |
| 8 RAG experiment docs | Retrieval-augmented | C+ | Markdown only, 0 .py files |
| AI-Infra performance memo (4 docs) | Inference infra | A- | Directly guides tech selection |
| 2026-07 Tech Radar — GI/ReSTIR | Tech strategy | B+ | Clear quadrants, Agent infra missing |
| AI 3D Video + AI-Native Game history | Cross-frontier | A- | 423+405 lines, high academic quality |
Key Findings
- Finding 1: AI-Infra memo is the highest-value output this week — directly explains TTFT/ITL/concurrency/cost for Agent systems.
- Finding 2: Agent Harness is a high-quality teaching skeleton but lacks "brain cortex" — reward=0.0, no Learning, no Planner/MCP/Multi-agent.
- Finding 3: RAG experiments are "executable notes" not "reproducible experiments" — all 8 marked "☐ pending", 0 standalone .py files.
- Finding 4: Tech Radar is benchmark-level within its declared boundary, but has systematic strategic blind spots (Agent core infra completely missing).
Next Week Plan (W28)
- P0: Fix Agent Harness reward function, connect to real LLM
- P0: Convert RAG #01 baseline Pipeline to runnable .py
- P1: Add "Agent Infrastructure" sub-radar to tech radar
- P1: AI-Infra memo: KV Cache structured compression + math derivation appendix
GameDev Vault
Week Snapshot
25 commits / 128 files / +45,442 / -887 (2026-06-28 ~ 2026-07-03)
Three Main Lines + One Archive Line
Line A: UE5 Extending MCP (Real Engineering Loop)
- Phase 1-9 complete — 15 tools + 7 smoke test iterations + real Fatal error capture
- Key fixes: Game thread deadlock, JSON format bug, Class lookup 3-level fallback
- Alignment value: MCP = Agent TCP/IP — real production-grade tool implementation
Line B: UE5 Training MCP Pipeline (Data + Fine-tuning)
- 6-phase pipeline: mcp_data_generator → data_pruner → data_prep → train_small_model → eval_model → export_to_excel
- 4113-line grounding.json as real MCP call ground truth
- Goal: 3B model approaches 70B level in UE5 vertical domain
Line C: UE5 Expert AI Training Research (Career Narrative)
- Complete guide (552 lines) + 3 research docs (1332 lines total)
- Represents "game engine expert participating in AI model training" job story
Line D: Routine Knowledge Base (Second Brain)
- Shader cases: SSR / Lumen GI diffuse / Lumen reflection / Nanite material / UE5.8 Sky
- Source analysis: UE5 Cook pipeline / Godot Jolt Physics / Sky / Cloud / SkyPass / Lumen / Nanite
- Tech Radar: Q3 README restructured + 7 P0 radar cards + 4 new entries
Capability Matrix Alignment Check
| Capability | Self-score | Day-job score | Gap |
|---|---|---|---|
| AI training pipeline | 7/10 | 7/10 | Factory built, no product shipped |
| UE5 vertical domain | 7/10 | 5/10 | VSM single point, Mac zero coverage |
| Engineering | 8/10 | 8/10 | Missing CI/CD |
| Weighted | 7.3/10 | 6.5/10 | Day-job prep insufficient |
Risk & Blind Spots
- Commit density vs review depth: 25 commits / 128 files / 45K lines — exceeds comfortable review threshold
- Career review log & diary both broken — 0 formal content before this review
- 3 of 6 AI-native capability poles are blind spots: interactive gameplay, classical RL, classical ML character pipeline
- 16K lines of smoke test JSON committed to git — should be .gitignored
Next Week Plan (W28)
- Keep: Daily Routine note, Friday tech radar update, continue P1 Batch 3-6
- Improve: Commit granularity < 500 lines / 3 files, git rm smoke test JSON + .gitignore
- Add: NVIDIA ACE demo, Hunyuan3D first try, W28 weekly review
- Pause: Performance optimization memo, Godot engine
Sources
- AI Research Vault:
AIResearchVault/document/Routine/06-研究复盘日志/2026-W27-复盘.md - GameDev Vault:
GameDevVault/Routine/06-职业复盘日志/2026-07-05-过去一周repo修改研究复盘-AI时代对齐.md - GameDev Vault (Capability Check):
GameDevVault/Routine/06-职业复盘日志/2026-07-05-三能力对账-是否满足-AI训练pipeline-UE5垂直领域-工程化.md
Generated from routine knowledge vaults. For full details, refer to the original markdown files.