Math and Compute
Quantitative foundations — math, computer science substance, and programming languages.
| Library | What it covers | Notes |
|---|---|---|
| Math | Linear algebra, probability, calculus, ODE/PDE, optimization, info theory, RL, Lie groups, stochastic calc, Markov + HMM, Riemannian, copulas, functional analysis, variational inference. T3 distribution zoo + optimization taxonomy + kernel zoo + ODE/PDE solvers + numerical methods. | ~32 |
| Compute | Distributed systems, databases, OS, networking, ML/AI, security, observability, architecture. T2 (CUDA/Triton, prompt-eng, model serving, CRDTs, eBPF, FPGA, formal verification, lock-free + RDMA, differential privacy). T3 (ml framework, llm landscape, DB engines, observability, cloud providers, auth providers). | ~36 |
| Languages | 51 deep per-language notes + 6 cross-cutting comparisons (memory/types/concurrency/metaprog/build/learn-next) + 86 family catalogs in Tier3 covering ~310 languages | ~143 |
Cross-cutting
- Math ↔ Compute: ML algorithms = math made executable; cryptography is number theory; ML inference is numerical linear algebra
- Compute ↔ Languages: compilers, runtimes, GC, type systems
- All three ↔ Engineering: control theory, signal processing, scientific computing
- All three ↔ Sciences: DFT (chemistry), climate models, bioinformatics
Adjacent
- Engineering-and-Robotics — applied math + compute (control, SLAM, perception)
- Finance — quant finance is applied math
- Walkthroughs — drone autopilot, ag tractor, semi litho all exercise compute deeply