Additional Topics¶
Free, high-signal resources for deepening your engineering knowledge. Each link is chosen for practical value and clarity.
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System Design
Scalability, reliability, and maintainability. The art of building production systems.
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Distributed Systems
Consensus, consistency models, and the fallacies of distributed computing.
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Networking
OSI model, TCP/UDP, DNS, HTTP/3, and how the internet actually works.
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Security
OWASP Top 10, authentication, authorization, and cryptographic fundamentals.
Essential Reading¶
Foundational articles every engineer should revisit regularly.
| Resource | Type | Why It Matters |
|---|---|---|
| The Joel Test | Article | Pragmatic scorecard for engineering teams |
| Continuous Integration | Article | Foundational CI practices and pitfalls |
| 12-Factor App | Article | Principles for portable, maintainable services |
| The Forest and the Desert | Article | System design trade-offs and simplicity |
| Google SRE Workbook | Book | Battle-tested reliability patterns |
| AWS Builders Library | Articles | Deep dives on distributed systems |
AI/ML Learning Resources¶
YouTube channels for building intuition around machine learning and AI.
Foundations¶
| Channel | Focus |
|---|---|
| 3Blue1Brown | Visual intuition for math, linear algebra, neural networks |
| StatQuest | Clear explanations of statistics and ML fundamentals |
| Andrej Karpathy | Deep walkthroughs of neural networks and LLMs |
Applied ML¶
| Channel | Focus |
|---|---|
| DeepLearningAI | Structured learning paths for deep learning |
| Hugging Face | Open-source LLMs, transformers, modern NLP |
| sentdex | Practical ML and Python projects |
| Jeremy Howard | Practical deep learning with strong intuition |
Research and Papers¶
| Channel | Focus |
|---|---|
| Yannic Kilcher | Deep dives into ML research papers |
| Two Minute Papers | Accessible summaries of cutting-edge AI research |
| Arxiv Insights | Beginner-friendly explanations of AI papers |
| Machine Learning Street Talk | Technical discussions on AI research |
Advanced Topics¶
| Channel | Focus |
|---|---|
| Umar Jamil | Implementation-focused transformer explanations |
| Steve Brunton | Dynamical systems, control theory, scientific ML |
| Michael Bronstein | Geometric deep learning, graph neural networks |
Academic Resources¶
University-grade courses available for free.
| Channel | Focus |
|---|---|
| Stanford Online | CS229 (ML), Andrew Ng's courses |
| MIT OpenCourseWare | Rigorous ML, AI, and applied math |
| Caltech | Advanced optimization and theory |
General Engineering¶
Broader engineering talks and conversations.
| Channel | Focus |
|---|---|
| Anthropic Engineering | Applied AI safety and tooling |
| GOTO Conferences | Broad, practical engineering talks |
| Lex Fridman | Long-form conversations with AI researchers |
| Kaggle | Applied ML, competitions, real-world workflows |
How to Use This List¶
Quality Over Quantity
Don't try to watch everything. Pick one resource per topic and go deep before moving to the next.
Active Learning
Pause videos to try implementations yourself. Take notes. Explain concepts out loud.
Spaced Repetition
Revisit articles and videos after a few weeks. Understanding deepens with repeated exposure.