All series
Groups, rings, fields, and Galois theory — the structural lens on mathematics.
Bailian model platform: prompt engineering, fine-tuning, agents, and evaluation.
The complete guide to building on Alibaba Cloud — compute, networking, storage, databases, AI, security, and infrastructure-as-code.
Production-grade ML on Alibaba Cloud — DSW, DLC, EAS, Designer, QuickStart, end-to-end.
Six-piece field guide to Claude Code — config, modes, slash commands, MCP, hooks, SDK + GitHub.
Infrastructure, networking, and the platforms ML actually runs on.
OS, networking, compilers — the substrate beneath everything.
SQL, NoSQL, and everything in between.
From curves and surfaces to manifolds, connections, and Gauss-Bonnet.
Containers from first principles to production.
Infinite-dimensional vector spaces, bounded operators, spectral theory, and the math behind PDE.
From the kernel trick and RKHS theory to GP, Nystrom approximation, deep kernel learning, and a production toolkit.
Algorithms by pattern, with worked solutions.
The geometry and computation that underlies all of ML.
Practical Linux — shell, processes, networking, and performance.
End-to-end modern LLM stack: architectures, post-training, inference, RAG, evaluation, safety, and production.
Deriving the algorithms — no hand-waving.
Modern NLP — language models, embeddings, transformers, and beyond.
From classical ODE methods to neural ODEs.
Self-hosted AI agent gateway from zero to a real working stack — install, channels, skills, MCP.
From convex analysis to non-convex landscapes — first-order, second-order, constrained, stochastic, and combinatorial optimization with complete proofs.
PINNs, neural operators, and the math behind learned PDE solvers.
Asset allocation, wealth management products, and the beginner's path to a sensible portfolio — learned in public, one concept at a time.
The mathematical foundation every ML practitioner needs.
The thinking, tradeoffs, and growth behind building real systems — architecture, security, UX, reliability, and abstraction.
From scripts to production-grade Python.
From classic CF to GNN, GCSAN, HCGR, and modern multi-objective ranking.
Foundations of RL: MDPs, policy gradients, actor-critic, and offline RL.
Building systems that survive production.
Building infrastructure-as-code agents: planning, validation, and apply loops.
Statistical and deep methods for forecasting at scale.
Domain adaptation, fine-tuning, and representation transfer.