karpathy

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

mmozeiko

This downloads standalone MSVC compiler, linker & other tools, also headers/libraries from Windows SDK into portable folder, without installing Visual Studio. Has bare minimum components - no UWP/Store/WindowsRT stuff, just files & tools for native desktop app development.

Run py.exe portable-msvc.py and it will download output into msvc folder. By default it will download latest available MSVC & Windows SDK from newest Visual Studio.

You can list available versions with py.exe portable-msvc.py --show-versions and then pass versions you want with --msvc-version and --sdk-version arguments.

To use cl.exe/link.exe first run setup_TARGET.bat - after that PATH/INCLUDE/LIB env variables will be updated to use all the tools as usual. You can also use clang-cl.exe with these includes & libraries.

To use clang-cl.exe without running setup.bat, pass extra /winsysroot msvc argument (msvc is folder name where output is stored).

pratyakshm

Install Windows 11 on unsupported devices

Guide to install Windows 11 on any PC (does not involve ISO modifications) This guide will take you through a clean and simple way to install Windows 11 on your device by bypassing all requirements without doing any ISO modifications. Note: Guide shows fresh installation only.

Requirements:

1. ISO file (Link 1) (Link 2) (22000.65)
2. Rufus Microsoft Store GitHub Website
3. USB drive [8GB or more]

bavanws

Reducing Code-Comment Verbosity with Claude Code

Claude Code over-comments. It narrates loops, restates signatures, leaves change-log notes addressed to a PR reviewer who'll never see them, and points at spec sections that have already moved. Two layers fix it and compound: a pre-hoc layer in CLAUDE.md that steers the model as it writes, and a post-hoc cleanup skill that reclaims the residue — plus everything verbose you inherited from past work and other contributors. Use both; neither is sufficient alone.

The whole thing is one principle applied twice. Code shows how. A comment earns its place only by carrying why — a non-obvious constraint, a deliberate deviation, a gotcha, a workaround, the reason a tempting simpler version is wrong. Everything else is restatement, and restatement rots: it drifts out of sync, adds diff noise, and trains readers to skim past comments entirely.

Layer 1 — Prevent it (CLAUDE.md)

The block I use:

going-digital

;; Native implementations of sin, log and exp functions.
;; sintau: 41 bytes code, 34 bytes shared code, 24 bytes data
;; exp2: 25 bytes code, 34 bytes shared code, 20 bytes data
;; log2: 37 bytes code, 34 bytes shared code, 24 bytes data
;; Total 137 bytes code, 68 bytes data

;; Wasm-opt -Oz tries to optimise out $half by converting to f32.consts, but that actually takes up more space, not less.

;; Polynomial coefficients calculated by accompanying python script.
;; call $evalpoly parameters will need to be manually changed for different length polynomials.

dewmal

From Algorithms to Agentic AI with Python

A Step-by-Step Tutorial Using Groq, Vector Databases, RAG, Tools, Pinecone, and Pydantic AI

This tutorial explains a practical progression from traditional deterministic code to an agentic AI system. It follows one consistent example: a customer-support assistant that classifies issues, retrieves user/support knowledge, calls tools, and eventually makes multi-step decisions through an agent framework.

The goal is not to jump directly into an agent framework. Instead, each stage introduces one new capability so that the reason for RAG, tool calling, vector search, and orchestration becomes clear.

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geohot

Compilers for Machine Learning

A hands-on one semester course where students build their own compiler from scratch, starting from elementwise programs and ending with training SOTA LLMs on GPUs. This course aggressively builds on the previous week, and is an exercise in slop management. If you let any slop in early, it will compound and you will not finish the class.

Course description: This course covers the design and implementation of a modern machine learning compiler, and examines the interaction between IR design, hardware capabilities, and the structure of machine learning programs. Topics covered include term rewriting, code generation, movement operators, kernel fusion, memory hierarchies, GPU architecture, automatic differentiation, and flash attention. It is a project course, providing experience with performance-oriented programming, managing a codebase that grows all semester, and working in 1 or 2 person teams, culminating in a compiler capable of training modern LLMs.

Prerequisites: This c