Articles
Hidden Technical Debt of AI Systems: Agent Runtime
siddontang on X: “The Database Is No Longer Storage - It Is Becoming the Runtime for AI” / X
Brain study suggests how students learn from mistakes | USC Rossier School of Education
Books
Systems Programming
Computer Architecture A Quantitative Approach by john L. Hennessy Stanford University David A. Patterson University of California, Berkeley
Grokking Concurrency by Kirill Bobrov
Full Stack Development
Designing Data-Intensive Applications, 2nd Edition (Martin Kleppmann, Chris Riccomini)
Microservices Patterns, Second Edition by Chris Richardson
Real-World Cryptography by David Wong
Kubernetes in Action 1st Edition Kubernetes in Action 2nd Edition
Docker in Practice 2nd Edition Docker in Action 2nd Edition
Container Security
Podman in Action
AI-ML-DL-LLM
The Elements of Statistical Learning - by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
Statistical foundations of ML: linear models, regularization (ridge/lasso), trees, boosting, random forests, SVMs, kernel methods, unsupervised learning. Heavy on the why, with bias-variance and model assessment throughout.
Understanding Machine Learning: From Theory to Algorithms - by Shai Shalev-Shwartz and Shai Ben-David
Learning theory: PAC learning, VC dimension, generalization bounds, and why learning is possible at all. Also covers the math behind SVMs, boosting, and stochastic gradient descent.
Foundations of Machine Learning - by Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar
Similar theory territory with a more rigorous, learning-theory style: Rademacher complexity, generalization bounds, and the guarantees behind SVMs, boosting, and online learning.
The Hundred-Page Machine Learning Book (Andriy Burkov)
A compact overview of the whole field: core algorithms, feature engineering, evaluation, basic neural nets. Good for a map, not depth.
Practical MLOps (Textbook)
Getting models into production: CI/CD for ML, monitoring, versioning, deployment, and cloud tooling.
Mathematics for Machine Learning - by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
The prerequisites: linear algebra, analytic geometry, matrix decompositions, vector calculus, probability, optimization. Then applies them to regression, PCA, GMMs, and SVMs.
Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python
Practical implementation: classic ML in scikit-learn, then neural nets, CNNs, RNNs, transformers, GANs, and GNNs in PyTorch.
Deep Learning from Scratch: Building with Python from First Principles
How neural nets actually work, by building them in plain Python/NumPy: backprop, layers, optimizers, CNNs, RNNs
Deep Learning by Goodfellow, Bengio, and Courville, Hardcover Book on Artificial Intelligence
The deep learning reference: the math background, modern practices (regularization, optimization), CNNs, RNNs, and research topics like autoencoders and generative models. Predates transformers.
AI Engineering: Building Applications with Foundation Models (Chip Huyen)
Building applications on foundation models: prompting, RAG, fine-tuning, evaluation, inference optimization, and production architecture.
Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications (Chip Huyen)
End-to-end ML system design: data pipelines, feature engineering, training, deployment, monitoring, and handling data drift. Strong for system design interviews.
Data Science From Scratch: First Principles with Python, Second Edition
Core algorithms (linear regression, k-NN, naive Bayes, decision trees, basic neural nets) implemented from first principles in Python, plus the stats and linear algebra they need.
The Statquest Illustrated Guide To Statistics: With Hands-on examples in Python and R!!!
Intuitive statistics: distributions, hypothesis testing, p-values, confidence intervals, and regression, with visual explanations.
AI Agents in Action (Micheal Lanham)
Building LLM-based agents: tool use, planning, memory, multi-agent systems, and agent frameworks.
Core
Academic books, for research leaning career.
Matrices and Calculus
Engineering Chemistry
ODE and Vector Calculus
Applied Physics
Discrete Mathematics
Formal Languages and Automata Theory