PERSONAL LEARNING DOCS
Everything I Learn,
Documented.
Not a course. Not a tutorial site. Just me building things, breaking things, and writing down what I actually understand.
Retrieval Augmented Generation, embeddings, cross-encoders, LangChain pipelines.
Backpropagation, CNNs, RNNs, Transformers, attention mechanisms, training tricks.
Linear models, trees, ensembles, SVM, clustering, dimensionality reduction.
Arrays, trees, graphs, dynamic programming, system design fundamentals.
Distributions, Bayes theorem, hypothesis testing, CLT, confidence intervals.
Components, hooks, App Router, Server Actions, data fetching, performance.
Limits, derivatives, chain rule, gradients, partial derivatives, optimization.
Vectors, matrix transformations, eigenvalues, SVD, PCA — the 3Blue1Brown way.
Routing, Pydantic, JWT auth, SQLModel, async, background tasks, testing, and a full TaskFlow project.
Physical layer to HTTP — OSI model, TCP/UDP, DNS, HTTP/HTTPS, sockets, WebSockets, and network security.
DFA, NFA, CFG, Turing Machines, decidability, P vs NP — the mathematical foundations of CS.
Lexer, parser, semantic analysis, IR generation, optimization, and code generation — how source becomes machine code.
Hardware to software — CPU, RAM, motherboard, BIOS/UEFI, kernel, processes, memory, file systems, syscalls.
Transistors to CPU — logic gates, ALU, datapath, pipelining, cache, and ISA (x86-64 & ARM).