Learning Paths
Structured routes to mastery.
Follow a path or build your own curriculum using the resource vault.
Built for progress
Each path blends theory, practice, and projects.
AI Foundations
Core math and machine learning fundamentals for new builders.
- MIT OpenCourseWare - Calculus · Foundational
- Khan Academy - Linear Algebra · Foundational
- 3Blue1Brown - Essence of Linear Algebra · Foundational
Local AI Builder
Launch local-first AI apps with automation and device control.
- 3Blue1Brown - Essence of Linear Algebra · Foundational
- MIT OCW - Differential Equations · Intermediate
Intelligence Augmentation (IA) Core
Hybrid path covering math foundations, ML systems, and IA workflows.
- Khan Academy - Linear Algebra · Foundational
- MIT OpenCourseWare - Calculus · Foundational
- Stanford CS229 - Machine Learning · Intermediate
- Fast.ai Practical Deep Learning · Intermediate
- Stanford CS224n - NLP · Intermediate
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- Arm Mbed · Intermediate
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- Awesome C# · All levels
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- Arm Mbed · Intermediate
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- Arm Mbed · Intermediate
Consciousness Systems Path
A hybrid track bridging neural signals, physiology, and physical constraints with experimental design.
- Signal foundations: EEG bands + preprocessing
- Physiology coupling: HRV, respiration, autonomics
- Neural computation: spiking + neuromorphic models
- Interventions: tDCS/tACS protocols with safety
- Research design: baselines, tasks, analysis
IA Process (Hybrid: Teaching + Technical)
- Clarify goal, constraints, and success metrics.
- Build a math + ML foundation (linear algebra, calculus, stats).
- Model the task with ML, NLP, CV, and RL components.
- Design human-in-the-loop feedback and guardrails.
- Ship an IA workflow, then iterate with data-driven improvements.
loop:
observe()
reason()
recommend()
act_with_human()
evaluate()
refine()