Skip to content

Latest commit

 

History

History
63 lines (51 loc) · 1.79 KB

File metadata and controls

63 lines (51 loc) · 1.79 KB

Specialization Study Tracks

This document defines how deeper specialization areas can be taught as reusable study tracks inside the wider lippytm ecosystem.

Purpose

Turn growing specialization areas into clearer tracks that builders can study, practice, review, and eventually package into reusable systems.

Core Principle

A good specialization track should connect setup, exercises, review loops, sandbox practice, and reusable outputs instead of being just a list of topics.

Study Track Types

1. Programming Tracks

Use for:

  • language practice
  • debugging and testing
  • toolkit usage
  • environment-specific exercises

2. Machine Learning Tracks

Use for:

  • workflow practice
  • evaluation exercises
  • retrieval and embeddings practice
  • experimentation and review loops

3. Blockchain Tracks

Use for:

  • wallet practice
  • contract practice
  • testing and deployment exercises
  • safe experimentation paths

4. Agent and Workflow Tracks

Use for:

  • routing exercises
  • secure workflow practice
  • evaluation loops
  • sandboxed agent patterns

Core Parts of a Track

  • setup path
  • lesson or exercise path
  • review path
  • sandbox path
  • reuse path into packs, kits, or templates

Best Practices

  • keep tracks modular
  • attach setup, review, and security notes
  • connect repeated study patterns into reusable packs
  • use study loops to decide what should become a specialization product or kit
  • archive weak tracks clearly when stronger versions replace them

Connected Repositories

  • lippytm-lippytm.ai-tower-control-ai
  • MyClaw.lippytm.AI-
  • OpenClaw-lippytm.AI-
  • Factory.ai
  • The-Encyclopedia-of-Everything-Applied-ChatAIBots

Guiding Principle

Specialization becomes more teachable when it is organized into repeatable study tracks with setup, practice, review, and reuse paths.