Archives
Browse articles organized by date
2026
September 20264
Build-Time Slimming
Two slimming routes: images and dictionaries
Page Switching and First Paint
Prefetch and view transitions for page switching, plus CLS fixes and content-visibility long-article rendering
Static Site Caching and Search
Service Worker bucket caching and the Pagefind index
Docker Compose Hadoop Cluster
Stand up a three-node cluster with Compose
August 20267
Encoding and Graphic Codes
Three motives of encoding, four densities of graphics
Life and Science Tools
Formulas, rhythms, balancing, and significance tests
Game Numerical Design
RPG formulas and growth curves, taming the idle exponential, elimination board structures, and pricing letter probabilities
Cognition, Parties, and Escape Rooms
Perception tests, social play, and level design
Text Adventures and State Machines
Six hundred-line text engines, dissecting how state machines carry narrative
Hot100 Algorithm Notes
LeetCode Hot100 algorithm notes
SQL Algorithm Notes
Algorithm notes for SQL50 on LeetCode
July 20267
Blog Underlying Stack Update Log
Upgrading the blog stack from Astro 5 to Astro 7
Eino Notes — Agentic Advanced
Eino Notes — Agentic Advanced Features
Eino Study Notes
Eino Study Notes — Getting Started
Eino Study Notes — Appendix 1
Eino Study Notes — Appendix 1: The Flow Module — ReAct Agent and MultiAgent
Eino Study Notes — Appendix 2
Eino Study Notes — Appendix 2: the Components module
Eino Study Notes — Appendix 3
Eino Study Notes — Appendix 3: Lambda Writing, Debugging Tools, and Visualization
Cloudflare Tunnel Setup
Map local services to your own domain with Cloudflared
June 20262
May 20262
March 20262
February 20262
2025
September 20259
Deep Learning Notes - 3: Classification Models and Introduction to Deep Neural Networks
Deep Learning Notes - 3, covering classification models (discriminant functions, logistic regression, softmax) and deep neural networks (activation functions, transfer learning, mixture density networks). Corresponding to Chapters 5-6 of "Deep Learning: Fundamentals and Concepts".
Deep Learning Notes - 4: Gradient Descent, Backpropagation, and Regularization
Deep Learning Notes - 4, covering gradient descent, backpropagation, and regularization. Corresponding to Chapters 7-9 of "Deep Learning: Foundations and Concepts".
Deep Learning Notes - 5: Convolutional Networks and Probabilistic Graphical Models
Deep Learning Notes - 5, covering Convolutional Neural Networks (convolution, pooling, object detection, image segmentation, style transfer) and Probabilistic Graphical Models (Bayesian networks, conditional independence, d-separation). Corresponds to Chapters 10-11 of "Deep Learning: Fundamentals and Concepts".
Deep Learning Notes - 7: Sampling Methods and Latent Variable Models
Deep Learning Notes - 7, covering sampling methods (Monte Carlo, MCMC, Gibbs sampling), discrete latent variables (K-means, GMM, EM algorithm), and continuous latent variables (PCA, probabilistic PCA). Corresponding to Chapters 14-16 of "Deep Learning: Fundamentals and Concepts".
Deep Learning Notes - 8: GANs, Normalizing Flows, Autoencoders, and Diffusion Models
Deep Learning Notes - 8, covering four major generative models: GANs, normalizing flows, autoencoders and variational autoencoders (VAE), and diffusion models. Corresponds to Chapters 17-20 of "Deep Learning: Foundations and Concepts".
Deep Learning Notes-1: Polynomial Fitting, Probability Theory, and Information Theory Fundamentals
Deep Learning Notes-1, covering overfitting and underfitting, regularization, probability theory fundamentals (Bayes' theorem, Gaussian distribution, maximum likelihood estimation), and information theory (entropy and KL divergence). Corresponds to Chapters 1-2 of "Deep Learning: Foundations and Concepts".
Deep Learning Notes-2: Probability Distributions, Linear Regression, and the Bias-Variance Tradeoff
Deep Learning Notes-2, covering the Bernoulli distribution, multinomial distribution, multivariate Gaussian distribution, the probabilistic derivation of linear regression, decision theory, and the bias-variance tradeoff. Corresponds to Chapters 3-4 of "Deep Learning: Foundations and Concepts".
Deep Learning Notes-6: Transformer and Graph Neural Networks
Deep Learning Notes-6, covering the Transformer attention mechanism (self-attention, multi-head attention, positional encoding), language models (GPT/BERT), and graph neural networks (message passing, graph convolution, graph attention). Corresponds to Chapters 12-13 of "Deep Learning: Foundations and Concepts".
Deep Learning Notes: Glossary
A quick-reference glossary for the deep learning study notes series, explaining all the technical terms in plain language. Based on Bishop's "Deep Learning: Foundations and Concepts," covering core concepts from probability theory, neural networks, optimization, CNNs, Transformers, and generative models.
2005
June 20055
CSS
CSS notes following HTML
Go Web Frameworks and RPC Notes
A study guide to the mainstream Go frameworks for beginners and interview preparation, covering the core concepts, usage, and comparisons of Gin, Eino, GoZero, gRPC, and Protobuf
HTML
HTML notes
JavaScript
Notes on JavaScript following HTML and CSS
Silly Arch Bridge
Recording markdown syntax with a friend