1. GUIDE
  2. 1. Develop Locally, Deploy To The Cloud
    1. 1.1. Section 1: Foundations of Local Development for ML/AI
    2. 1.2. Section 2: Hardware Optimization Strategies
    3. 1.3. Section 3: Local Development Environment Setup
    4. 1.4. Section 4: Model Optimization Techniques
    5. 1.5. Section 5: MLOps Integration and Workflows
    6. 1.6. Section 6: Cloud Deployment Strategies
      1. 1.6.1. Specialized GPU Cloud Providers for Cost Savings
    7. 1.7. Section 7: Real-World Case Studies
    8. 1.8. Section 8: Miscellaneous "Develop Locally, DEPLOY TO THE CLOUD" Content
  3. 2. 50-Day Study Plan
    1. 2.1. Day 1-2 Rust/Tauri
    2. 2.2. Day 3-4 LLMs and LLMops
    3. 2.3. Day 5-6 Ingesting APIs
    4. 2.4. Day 7-8 Data Wrangling
    5. 2.5. Day 9-10 Vector Databases
    6. 2.6. Day 11-12 Jujutsu & GitHub
    7. 2.7. Day 13-14 arXiv API
    8. 2.8. Day 15-16 HuggingFace API
    9. 2.9. Day 17-19 Patent APIs
    10. 2.10. Day 20-22 FinNews APIs
    11. 2.11. Day 23-25 Email APIs
    12. 2.12. Day 26-28 Anthropic MCP
    13. 2.13. Day 29-31 Google A2A
    14. 2.14. Day 32-34 Agent Orchestration
    15. 2.15. Day 35-37 Info Summarization
    16. 2.16. Day 38-40 Learning Preferences
    17. 2.17. Day 41-43 Data Persistence
    18. 2.18. Day 44-46 Adv Email w/AI
    19. 2.19. Day 47-48 Refactor UI
    20. 2.20. Day 49-50 Deploy/Test
    21. 2.21. Milestones
    22. 2.22. Daily Workflow
    23. 2.23. Autodidacticism
    24. 2.24. Communities
    25. 2.25. Papers
    26. 2.26. Documentation
    27. 2.27. References
  4. 3. Blogifying The Plan
    1. 3.1. Rust Dev Fundamentals
    2. 3.2. Tauri Development
      1. 3.2.1. Tauri vs Electron
      2. 3.2.2. Svelte With Tauri
    3. 3.3. ML/AI Development
    4. 3.4. ML/AIOps System Design
    5. 3.5. Personal Assistant Agentic Systems (PAAS)
    6. 3.6. Multi-Agent Systems and Architecture
    7. 3.7. Data Storage and Processing Technologies
    8. 3.8. Creative Process Flow For Development
    9. 3.9. Philosophy/Principles
    10. 3.10. Cross-Platform
  5. 4. ML/AI Ops Study Notes
    1. 4.1. Rust Language
    2. 4.2. Tauri
    3. 4.3. Cargo
    4. 4.4. crates.io
  6. 5. Information Autonomy
    1. 5.1. Philosophical Foundations
    2. 5.2. Technical Foundations
    3. 5.3. Adv Observability Enrg
    4. 5.4. Data Pipeline Architecture
    5. 5.5. Knowledge Engineering
    6. 5.6. Unobtrusive AI Assistance
    7. 5.7. Architecture Integration
    8. 5.8. Compute Resources
    9. 5.9. Implementation Roadmap
    10. 5.10. Application, Adjustment
    11. 5.11. Future Directions
    12. 5.12. Conclusion

GUIDE: Civic Information Resource

References Pertinent To Our Intelligence Gathering System

Cloud Compute

RunPod

ThunderCompute

VAST.ai

Languages

Go

Python

Rust

Rust Package Mgmt

  • crates.io
  • Cargo

Tauri

Typescript

Libraries/Platforms for LLMs and ML/AI

HuggingFace

Kaggle

Ollama

OpenAI

Papers With Code

DVCS

Git

Jujutsu