Intelligence Gathering
1.
Information Autonomy
1.1.
Philosophical Foundations
1.2.
Technical Foundations
1.3.
Adv Observability Enrg
1.4.
Data Pipeline Architecture
1.5.
Knowledge Engineering
1.6.
Unobtrusive AI Assistance
1.7.
Architecture Integration
1.8.
Compute Resources
1.9.
Implementation Roadmap
1.10.
Application, Adjustment
1.11.
Future Directions
1.12.
Conclusion
2.
50-Day Study Plan
2.1.
Day 1-2 Rust/Tauri
2.2.
Day 3-4 LLMs and LLMops
2.3.
Day 5-6 Ingesting APIs
2.4.
Day 7-8 Data Wrangling
2.5.
Day 9-10 Vector Databases
2.6.
Day 11-12 Jujutsu & GitHub
2.7.
Day 13-14 arXiv API
2.8.
Day 15-16 HuggingFace API
2.9.
Day 17-19 Patent APIs
2.10.
Day 20-22 FinNews APIs
2.11.
Day 23-25 Email APIs
2.12.
Day 26-28 Anthropic MCP
2.13.
Day 29-31 Google A2A
2.14.
Day 32-34 Agent Orchestration
2.15.
Day 35-37 Info Summarization
2.16.
Day 38-40 Learning Preferences
2.17.
Day 41-43 Data Persistence
2.18.
Day 44-46 Adv Email w/AI
2.19.
Day 47-48 Refactor UI
2.20.
Day 49-50 Deploy/Test
2.21.
Milestones
2.22.
Daily Workflow
2.23.
Autodidacticism
2.24.
Communities
2.25.
Papers
2.26.
Documentation
2.27.
References
2.28.
Big Compute
3.
Blogifying The Plan
3.1.
Rust Dev Fundamentals
3.2.
Tauri Development
3.2.1.
Tauri vs Electron
3.2.2.
Svelte With Tauri
3.3.
ML/AI Development
3.4.
ML/AIOps System Design
3.5.
Personal Assistant Agentic Systems (PAAS)
3.6.
Multi-Agent Systems and Architecture
3.7.
Data Storage and Processing Technologies
3.8.
Creative Process Flow For Development
3.9.
Philosophy/Principles
3.10.
Cross-Platform
4.
ML/AI Ops Study Notes
4.1.
Rust Language
4.2.
Tauri
4.3.
Cargo
4.4.
crates.io
Light
Rust
Coal
Navy
Ayu
Intelligence Gathering
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