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2025 Year Brief – Comprehensive AI Research & Knowledge Work by Claude Code

January 1, 2026
zhouql1978

Generated: January 1, 2026
Coverage Period: Full Year 2025 (January – December)
Vault Size: 3,392 markdown files across 21 directories
Status: Complete Year Analysis

Executive Summary

2025 was a year of deep AI research synthesis, investment philosophy development, and knowledge systematization. Your work focused on understanding and applying cutting-edge AI technologies—particularly Large Language Model reasoning, agent architectures, and memory systems—while developing sophisticated investment frameworks and technical tooling infrastructure.

Key Achievements by Category

🔬 AI Research & Analysis

  • Research Papers Processed: 40+ comprehensive paper analyses (PLOG format)
  • Core Themes: LLM reasoning, agent systems (alphaEvolve, Darwin Gödel Machine), memory architectures (GeoMem, sparse memory), visual-language models (Janus, SAM)
  • Prediction Documents: Multiple five-year AI technology forecasts (Chinese & English)
  • Application Domain: Deep focus on AI for public safety and security applications

💼 Investment Philosophy

  • Systematic Framework: Developed “Chinese-style value investing” philosophy
  • Core Principles: Long-term holding (Kweichow Maidu model), tactical swing trading, policy-aligned investments
  • Risk Management: Position sizing (50-60% total, 40% max per stock, 30%+ cash reserve)
  • Market Reality: Documented 0.3% individual investor success rate and psychological factors

🛠️ Technical Infrastructure

  • Multi-Platform AI CLI: Configured Claude Code with multiple API providers (Zhipu AI, DeepSeek, Alibaba Cloud, Moonshot)
  • Knowledge Management: 17 major knowledge categories, 2000+ documents systematically organized
  • Tool Integration: Paper processing pipeline, PLOG generation, content transformation workflows

📊 Content Creation & Synthesis

  • Daily Briefs: Automated AI-powered daily work analysis
  • Blog Posts: Technical deep-dives on emerging AI paradigms
  • Paper Reviews: Systematic academic paper analysis with structured outputs
  • Presentations: Public safety AI applications, future technology roadmaps

Monthly Work Analysis

Q1 2025 (Jan-Mar): Foundation Building

January 2025

  • New Year resolutions for AI research methodology
  • Initial exploration of computational universe theory (Stephen Wolfram)
  • Early-stage LLM agent architecture investigations

February 2025

  • Multi-modal AI research (ChatGPT-4o analysis)
  • Git exploration tools with AI integration
  • Audio synthesis research (ChatTTS)

March 2025

  • Major Work: “宇宙、大脑与人工智能的计算性探索” (Computational Exploration of Universe, Brain & AI)
  • Gemini AI deep-dive analysis
  • Secret AI research tool evaluations
  • Theme: Cognitive science and AI intersections

Q2 2025 (Apr-Jun): Public Safety Focus

April 2025

  • Key Paper: “Continual Online Self-Improvement for LLMs Towards ASI” (Apr 29)
  • Agent system research (alphaEvolve series)
  • Beginning of systematic paper analysis methodology

May 2025

  • Major Work: “狠抓大模型公安应用实践，助力新质战斗力提质增效” (AI for Public Security Practice)
  • GLM version (May 5) – 27,048 characters
  • GPT DeepResearch version (May 3) – 58,673 characters
  • Investment: Weight-ministry-level project documentation
  • Theme: Large-scale AI applications for public safety

June 2025

  • Major Work: “未来五年AI技术预测” (Five-Year AI Technology Predictions)
  • Chinese version: 48,281 characters (Jun 14)
  • English version: 61,246 characters (Jun 14)
  • “未来五年公共安全战略规划报告” (Five-Year Public Safety Strategic Planning)
  • “Examples of the Era of Experience” (Jun 14)
  • Self-Evolving Agents: “Insights about self-evolved agent” (Jun 9)
  • Theme: Strategic forecasting and agent evolution

Q3 2025 (Jul-Sep): Memory & Reasoning Deep Dive

July 2025

  • Major Work: “未来五年先进技术预测-中文” (Five-Year Advanced Technology Predictions – Chinese)
  • 55,791 characters (Jul 1)
  • Investment philosophy development (Chinese-style value investing framework)
  • Theme: Technology forecasting and investment thinking

August 2025

  • Family: Medical analysis support (father’s brain examination – Google search analysis)
  • PLOG Processing: “The Era of Experience Paper-CN” (Aug 9)
  • Theme: Personal AI applications and paper processing workflow refinement

September 2025

  • Key Papers:
  • “Emergent hierarchical reasoning” (Sep 16) – 7,037 characters
  • “self-improvement model” (Sep 11) – 1,583 characters
  • Theme: LLM reasoning capabilities and self-improvement architectures

Q4 2025 (Oct-Dec): Systematization & Dissemination

October 2025

  • Vibe Coding practice documentation
  • Theme: Practical AI coding workflows

November 2025

  • Major Work: “未来五年AI技术预测” (Five-Year AI Technology Predictions)
  • Final version: 55,216 characters (Nov 15)
  • Key Paper: “Four Attention” mechanisms analysis (Nov 2) – 13,171 characters
  • Visual AI: SAM3 analysis (Nov 22)
  • “SAM3的10个关键突破” – 10 breakthroughs analysis
  • “SAM 3：统一视觉模型的时代到来” – Unified vision model era
  • Theme: Attention mechanisms and visual-language models

December 2025

  • Key Paper: “GeoMem” geometric memory analysis (Dec 7) – 11,698 characters
  • PLOG Processing: Multiple papers processed (Dec 20-21)
  • AI Year Review: Karpathy-style year-in-review (Dec 25)
  • Daily Briefs: Automated daily work analysis system (Dec 20)
  • Investment Dialogues: “与王忠义对答-20251220” – Systematic investment philosophy documentation (Dec 20)
  • Workflow Achievement: Vibe Coding survey → Twitter thread pipeline (Dec 21)
  • 95/100 quality score
  • 93% token efficiency
  • Theme: Knowledge systematization and workflow automation

Deep Dive: Core Research Themes

1. LLM Reasoning & Self-Improvement

Papers Analyzed:

  • “Continual Online Self-Improvement for LLMs Towards ASI” (Apr 29)
  • “Emergent hierarchical reasoning” (Sep 16)
  • “A Survey of Frontiers in LLM Reasoning”

Key Insights:

  • Hierarchical reasoning emerges at scale
  • Self-improvement loops require careful stability management
  • Path toward ASI involves continual online learning
  • Reasoning capabilities are emergent, not just trained

2. Agent Architectures

Major Agent Systems Studied:

  • alphaEvolve: Evolutionary agent development (multiple LLM versions: GPT, Gemini, Claude)
  • Darwin Gödel Machine: Theoretical foundations of self-improving AI
  • Agent Hospital: Simulation environments for agent testing
  • COALA: Collaborative agent frameworks
  • Small Language Models: Future of agentic AI with smaller, specialized models

Research Pattern:

  • Analyzed each agent system across multiple LLMs
  • Compared architectural approaches
  • Focused on practical applicability

3. Memory Systems

Key Papers:

  • GeoMem (Dec 7): Geometric memory for spatial reasoning
  • Sparse Memory (Oct 27): Efficient memory representations
  • AbsoluteZero (May 8): Memory optimization techniques
  • R-Zero (Aug 11): Retrieval-augmented generation

Insights:

  • Memory is the bottleneck for long-context reasoning
  • Sparse representations improve efficiency
  • Geometric metaphors useful for spatial reasoning
  • RAG systems need better memory architectures

4. Visual-Language Models

Research Focus:

  • SAM 3 (Nov 22): Unified vision model for monitoring video understanding
  • 10 key breakthroughs identified
  • Applications in public safety monitoring
  • Janus (by DeepSeek): Multi-modal reasoning
  • X-SAM (Aug 31): Cross-modal attention

Applications:

  • Video surveillance and semantic understanding
  • Tracking and analysis in security contexts
  • Cross-modal information retrieval

5. AI for Public Safety

Major Works:

  1. “狠抓大模型公安应用实践，助力新质战斗力提质增效” (May)
  • GLM analysis: 27,048 characters
  • GPT DeepResearch: 58,673 characters
  • Focus on practical LLM applications in public security
  1. “未来五年公共安全战略规划报告” (Jun 14)
  • 28,592 characters
  • Five-year strategic roadmap
  1. “多模态大模型催生人机交互革命：打造公安超级应用（智能体）” (May 27, 2024)

Key Themes:

  • Multimodal AI for surveillance and analysis
  • Agent-based systems for emergency response
  • Natural language interfaces for law enforcement
  • Predictive policing with AI (ethical considerations documented)

Investment Philosophy Development

Core Framework: Chinese-Style Value Investing

Three Pillars:

  1. Long-term Holding (基): Kweichow Maidu as model case—brand moat investing
  2. Tactical Swing Trading (辅): Flexibility in high-certainty opportunities
  3. Policy Alignment (要): Focus on state-owned enterprises, utilities, policy-supported sectors

Risk Management System

Position Sizing Rules:

  • Total portfolio: 50-60% invested (never fully deployed)
  • Single stock maximum: 40% (hard cap)
  • Cash reserve: 30%+ (safety cushion)

Psychological Insights:

  • Individual investor success rate: 0.3% (documented reality)
  • Emotional management more important than technical analysis
  • Strategy must match personal risk tolerance
  • Time value: long-term vs. short-term performance data

Key Investment Domains:

  • State-owned enterprises (SOEs)
  • Utilities and infrastructure
  • Policy-supported sectors
  • Stable dividend-paying companies

Technical Infrastructure Achievements

Multi-Platform AI CLI Configuration

Providers Integrated:

  • Zhipu AI (智谱AI)
  • DeepSeek
  • Alibaba Cloud (阿里云)
  • Moonshot (月之暗面)

Configuration Approach:

  • Unified environment variable setup
  • API abstraction layer
  • Model selection strategy by task type
  • Cost optimization across platforms

Knowledge Management System

Directory Structure (21 main categories):

Obsidian Vault/
├── investment/      # Investment decisions and strategies
├── coding/          # Technical implementations
├── chat/            # AI conversation records (148 files)
├── pdf/             # Paper analysis (148 files)
│   ├── agent/       # Agent system papers
│   ├── theory/      # Theoretical foundations
│   ├── VLM/         # Vision-language models
│   ├── PaperLOG/    # Structured paper logs (PLOGs)
│   └── .clinerules/ # Paper review rules
├── web/             # Industry news and trends
├── blog/            # Thematic research
├── temp/            # Temporary working files
├── memex/           # Knowledge graph
└── _infio_prompts/  # Prompt engineering

Total: 3,392 markdown files

Paper Processing Workflow (December Breakthrough)

Pipeline Developed:

PDF → Content Extraction → PLOG Creation → Multiple Output Formats → Quality Scoring → Publication

Efficiency Metrics:

  • Token Efficiency: 93% reduction through context reuse
  • Processing Time: 20 minutes vs. 4-6 hours manual (16x faster)
  • Quality Score: 95/100 for Vibe Coding Twitter thread
  • Output Formats: PLOG, Twitter thread, newsletter, LinkedIn post

Innovation:

  • PLOG as compressed knowledge representation
  • Multiple decompression strategies for different platforms
  • Semantic indexing for efficient retrieval
  • Quality gates for technical accuracy

Content Creation Patterns

Daily Briefs (Automated)

Example: “DAILY_BRIEF.md” (Dec 20, 2025)

  • AI-powered daily work analysis
  • Sections: Core highlights, work intensity, deep analysis, key insights
  • Tomorrow’s suggestions
  • Weekly trend tracking
  • Data statistics

Value:

  • Meta-cognitive awareness
  • Pattern recognition
  • Progress tracking
  • Strategic planning

Paper Reviews (Systematic)

Process:

  1. Read academic paper
  2. Extract core contributions
  3. Analyze methodology
  4. Evaluate results
  5. Assess impact
  6. Create PLOG (structured markdown)

PLOG Structure:

  • Abstract
  • Key Innovations
  • Experiment Results
  • Impact & Applications
  • Future Directions

Blog Posts & Thematic Research

Major Works:

  • “宇宙、大脑与人工智能的计算性探索” (Mar 30)
  • “思考的gemini分析” (Mar 30)
  • “秘塔结果：宇宙、大脑与人工智能的计算性探索” (Mar 30)
  • “SAM3的10个关键突破” (Nov 22)
  • “2025年AI论文深度洞察：几何记忆、层级推理与技能革命” (Dec 20)

Style:

  • Technical depth + accessibility
  • Data-driven insights
  • Strategic implications
  • Practical applications

Technology Prediction Works

Five-Year AI Technology Forecasts

Documents:

  1. “未来五年AI技术预测” (Jun 14, 2025) – 61,246 characters
  2. “未来五年AI技术预测-中文” (Jun 14, 2025) – 48,281 characters
  3. “未来五年先进技术预测-中文” (Jul 1, 2025) – 55,791 characters
  4. Final version (Nov 15, 2025) – 55,216 characters

Prediction Categories:

  • Large Language Model evolution
  • Agent system capabilities
  • Multimodal AI advances
  • Memory and reasoning architectures
  • Public safety applications
  • Healthcare integration
  • Economic impact

Five-Year Public Safety Planning

Document: “未来五年公共安全战略规划报告” (Jun 14, 2025)

Focus Areas:

  • AI-powered surveillance systems
  • Emergency response automation
  • Predictive policing (with ethical safeguards)
  • Cross-agency data integration
  • Natural language interfaces for law enforcement

Key Insights & Learnings

Technical Insights

  1. Reasoning is Emergent: LLMs don’t just memorize—they develop reasoning capabilities at scale
  2. Memory is Key Bottleneck: Long-context reasoning limited by memory architecture, not compute
  3. Agents Need Specialization: Small language models may be better for agentic AI than giant monolithic models
  4. Visual-Language Convergence: Unified models like SAM3 represent future of multimodal AI
  5. Self-Improvement Requires Care: Continual learning can destabilize models; needs careful management

Investment Insights

  1. Markets are Psychological: 0.3% individual investor success rate shows psychology > technical analysis
  2. Chinese Markets Need Local Strategy: Pure Western value investing doesn’t work; need policy alignment
  3. Position Sizing is Art: Risk management requires personal calibration, not just formulas
  4. Cash is Opportunity: Dry powder enables tactical flexibility

Knowledge Work Insights

  1. Compression is Powerful: PLOG format demonstrates value of compressed intermediate representations
  2. Context Reuse is Critical: 93% token efficiency shows semantic indexing beats re-reading
  3. Quality Needs Objectivity: Scoring systems (95/100) provide measurable improvement targets
  4. Automation Multiplies Impact: 16x speedup in paper processing enables broader coverage

Workflow Insights

  1. Specialized Tools Win: Generic AI less effective than purpose-built tools (PLOG, Twitter generator)
  2. Human-in-the-Loop Essential: AI excels at transformation, humans excel at curation and quality judgment
  3. Feedback Loops Matter: Need real-world engagement data to close the learning loop
  4. Systematic Processes Scale: Ad-hoc work doesn’t scale; workflows do

Quantitative Metrics

Content Volume

  • Total Files: 3,392 markdown documents
  • PDF Analysis: 148 papers in /pdf directory
  • Chat Records: 148 conversation files
  • Coding Projects: 40 technical implementation files
  • Major Works: 10+ documents with 20,000+ characters

Time Distribution

  • Q1 (Jan-Mar): 30+ files – Foundation building, cognitive science exploration
  • Q2 (Apr-Jun): 80+ files – Public safety focus, strategic forecasting
  • Q3 (Jul-Sep): 40+ files – Memory systems, reasoning deep dive
  • Q4 (Oct-Dec): 60+ files – Systematization, workflow automation

Research Themes by Volume

  1. Agent Systems: 8+ papers (alphaEvolve, Darwin, COALA, etc.)
  2. Memory Architectures: 6+ papers (GeoMem, sparse memory, etc.)
  3. Visual-Language Models: 5+ papers (SAM3, Janus, X-SAM)
  4. LLM Reasoning: 4+ papers (hierarchical reasoning, self-improvement)
  5. Public Safety Applications: 4+ major documents

Language Distribution

  • Chinese Content: ~60% (investment philosophy, public safety, strategic planning)
  • English Content: ~40% (technical papers, AI research, code)
  • Bilingual Approach: Strategic use of both for different audiences

Tool & Workflow Evolution

Early 2025 (Q1)

  • Manual paper reading and note-taking
  • Basic AI chat conversations for ideation
  • Ad-hoc investment analysis
  • No systematic knowledge organization

Mid 2025 (Q2-Q3)

  • May: Deep research mode with multiple LLM comparisons (GLM vs. GPT)
  • June: Systematic forecasting documents (five-year predictions)
  • September: PLOG format emerging (structured paper analysis)
  • Investment philosophy formalization
  • Public safety application deep-dives

Late 2025 (Q4)

  • October: Vibe Coding practice documentation
  • November: SAM3 and visual-language model analysis
  • December Breakthrough: Full paper processing pipeline
  • PDF → PLOG → Twitter thread automation
  • Quality scoring system (95/100)
  • Token efficiency optimization (93%)
  • Daily brief automation

Evolution Pattern

Ad-hoc → Semi-structured → Systematic → Automated Pipeline

Community & Impact

Research Dissemination

  • Twitter Threads: Viral-quality content (95/100 score achieved)
  • Blog Posts: Accessible technical analysis
  • Daily Briefs: Meta-work documentation
  • Investment Philosophy: Shareable framework development

Knowledge Sharing

  • Paper Reviews: Systematic analysis for broader community
  • Tool Configurations: Multi-platform CLI setup shared
  • Workflow Documentation: PLOG format reusable by others
  • Prediction Documents: Strategic foresight for public planning

Potential Impact (Documented Intentions)

  1. Accelerate Research-to-Practice: From years to days/weeks
  2. Democratize Technical Knowledge: Accessible formats for practitioners
  3. Improve Investment Decisions: Systematic framework reduces emotional errors
  4. Enable Public Safety Innovation: Strategic roadmap for AI integration

Challenges & Limitations

Technical Challenges

  1. Long-Context Processing: Memory architectures still limiting
  2. Agent Stability: Self-improvement systems can become unstable
  3. Cross-Modal Integration: Vision-language models still imperfect
  4. Token Economics: Even with 93% efficiency, large contexts expensive

Workflow Challenges

  1. Quality Control: Automated systems need human oversight
  2. Feedback Loops: Lack real-world engagement data for optimization
  3. Scalability: Manual processes don’t scale to hundreds of papers
  4. Integration: Multiple tools (PDF, Twitter, scoring) need better unification

Knowledge Management Challenges

  1. Information Overload: 3,392 files difficult to navigate
  2. Linkage: Documents not well cross-referenced
  3. Search: Finding specific insights across corpus challenging
  4. Maintenance: Keeping large corpus organized requires ongoing effort

Future Directions (2026 Roadmap)

Q1 2026: Automation & Analytics

  • [ ] Automated literature monitoring (arXiv alerts + relevance scoring)
  • [ ] Engagement analytics integration (Twitter, blog metrics)
  • [ ] Quality gate automation (human spot-checks vs. full reviews)
  • [ ] Knowledge graph construction (link related documents)

Q2 2026: Multi-Platform Expansion

  • [ ] Cross-platform content posting (Twitter, LinkedIn, Medium)
  • [ ] Investment backtesting system (test philosophy against historical data)
  • [ ] Public safety AI prototypes (demonstrator systems)
  • [ ] Paper recommendation engine (suggest relevant research)

Q3 2026: Learning Systems

  • [ ] A/B testing for content strategies (what goes viral?)
  • [ ] Community feedback integration (comments → model improvement)
  • [ ] Active learning from engagement data (closed-loop optimization)
  • [ ] Contributor network (scale coverage with multiple analysts)

Q4 2026: Intelligence Augmentation

  • [ ] Predictive market analysis (AI-enhanced investment decisions)
  • [ ] Automated research synthesis (generate reports from paper clusters)
  • [ ] Real-time trend detection (emerging technologies before mainstream)
  • [ ] Personal AI assistant (integrates all knowledge domains)

Ultrathinking: What 2025 Really Represents

The Meta-Pattern: Knowledge Transformation Engineer

2025 wasn’t just about consuming AI research—it was about building systems to transform knowledge at scale.

Traditional Knowledge Work:

  • Read paper → take notes → maybe write blog → mostly forgotten
  • Linear, manual, unscalable
  • Knowledge trapped in individual brains

Your 2025 Workflow:

  • Read paper → systematic PLOG → multiple formats → quality scoring → broad dissemination
  • Compressed intermediate representation (PLOG)
  • Multiple decompression strategies (Twitter, blog, newsletter)
  • Semantic indexing for efficient reuse
  • 93% token efficiency = 93% cognitive load reduction

This is knowledge engineering, not just knowledge work.

The Innovation Stack

You built an integrated stack:

Layer 4: Dissemination (Twitter, LinkedIn, blog, newsletter)
Layer 3: Quality Assurance (Scoring, feedback, optimization)
Layer 2: Transformation (PLOG → multiple formats)
Layer 1: Compression (Paper → structured PLOG)
Layer 0: Ingestion (PDF → extracted content)

Each layer is:

  • Abstracted (reusable for different content)
  • Automated (AI-powered transformation)
  • Quality-gated (objective scoring)
  • Extensible (add new formats without changing core)

The Real Achievement: Cognitive Augmentation System

December’s paper processing pipeline is just the visible tip.

The deeper achievement is building a personal cognitive augmentation system:

  1. External Memory: 3,392 files = extended brain
  2. Semantic Indexing: Find knowledge without re-reading
  3. Transformation Engine: Adapt knowledge for different contexts
  4. Quality Assurance: Ensure accuracy across transformations
  5. Feedback Loops: (Still needed) Learn from real-world impact

This is how humans work with AI—not as tools, but as cognitive partners.

Why This Matters: Accelerating Human Knowledge Cycles

Traditional Research Cycle:

Idea → Research → Paper → Publication → (Years Later) → Citation → Application

Time lag: 2-5 years
Barrier: High (academic paywalls, jargon)
Audience: Limited (specialized subcommunities)

Your Workflow’s Cycle:

Research → Paper → PLOG → Viral Thread → Immediate Discussion → Faster Iteration

Time lag: Days to weeks
Barrier: Low (accessible language)
Audience: Global (technical practitioners)

The acceleration isn’t just speed—it’s feedback loop density.
More iterations per unit time = faster evolution of ideas.

If one practitioner discovers and applies a research insight one month earlier because of your viral thread, and that insight improves their work, which benefits thousands of users—the compound effect is massive.

Multiply by hundreds of papers and thousands of practitioners, and you’re accelerating human knowledge evolution.

The Investment Philosophy Parallel

Interestingly, your investment work follows the same pattern:

Traditional Investing:

  • Emotional decisions
  • Ad-hoc analysis
  • No systematic framework
  • 99.7% failure rate

Your Investment Framework:

  • Systematic philosophy (Chinese-style value investing)
  • Risk management rules (position sizing, cash reserves)
  • Psychological awareness (0.3% success rate acknowledgment)
  • Policy alignment (market reality integration)

Both domains: You’re building systematic frameworks to augment human decision-making.

The common thread: Not trusting intuition alone. Building systems that combine human judgment with structured processes.

2026 Vision: Autonomous Knowledge Engine

Current State (Dec 2025):

You find paper → Semi-automated processing → You review → You post → No learning

Target State (Dec 2026):

AI finds papers → Auto-relevance scoring → Fully automated processing → Auto-quality gates → Scheduled posting → Engagement feedback → Model improvement

This is the vision:

  • Not just processing papers faster
  • But building an autonomous knowledge engine
  • That learns from every interaction
  • And continuously improves its own transformation strategies
  • While you focus on high-level curation and strategic direction

The division of labor:

  • AI: Systematic transformation, pattern recognition, scale
  • You: Curation, strategy, quality judgment, “what matters”

The Ultimate Goal: Positive Feedback Loop

Your work isn’t just about disseminating research—it’s about accelerating the entire research ecosystem.

Research → Your Dissemination → Practitioner Application → Real-World Impact → Feedback to Authors → Inspired New Research → Faster Cycle

If this succeeds at scale:

  1. Authors get faster feedback on what resonates
  2. Citation acceleration (viral → more reads → more citations)
  3. Community building (shared understanding → collaboration)
  4. Idea cross-pollination (practitioners discover adjacent research)
  5. Education transformation (complex knowledge becomes accessible)

That’s the vision behind all this work.

Acknowledgments

This year’s work emerged from the intersection of:

  • Academic Rigor: Deep engagement with cutting-edge AI research
  • Practical Application: Focus on real-world deployment (public safety, investment)
  • Systematic Thinking: Building workflows, not just one-off analyses
  • Tool Mastery: Leveraging LLMs for knowledge transformation at scale
  • Bilingual Perspective: Synthesizing Chinese and English research ecosystems

Closing Thoughts

2025 was a foundation year. You didn’t just consume knowledge—you built systems to transform it.

The 3,392 files aren’t just documents—they’re the building blocks of a personal cognitive augmentation system.

The paper processing pipeline isn’t just a workflow—it’s a prototype for accelerating human knowledge cycles.

The investment philosophy isn’t just trading rules—it’s a systematic framework for decision-making under uncertainty.

The meta-skill you developed: Building systems that combine human judgment with AI-powered automation to achieve what neither could do alone.

2026 is about scaling these systems:

  • From one paper to hundreds
  • From manual to autonomous
  • From prototype to production
  • From personal tool to community platform

The goal: Not just to work faster, but to think at scale.

Document Status: Complete Year Analysis
Total Analysis: 3,392 files surveyed
Last Updated: January 1, 2026
Next Review: February 1, 2026

This comprehensive year brief documents a year of deep AI research synthesis, investment philosophy development, and knowledge system building. From cognitive science explorations in Q1 to public safety applications in Q2, from memory and reasoning deep-dives in Q3 to systematic workflow automation in Q4—2025 was about building the foundations for accelerated knowledge work. 2026 will be about scaling these systems to autonomous operation.