List of freely available resources to study computer graphics programming.
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| name | code-refactor-review |
|---|---|
| description | Reviews code changes for reuse, composition, codebase consistency, and slop. Use when asked to review PRs/diffs, check code reuse, composition, cleanliness, or whether code fits the codebase. |
Review code changes the way Sahaj usually asks for review: go deep on reuse, composition, codebase consistency, and anything that reads like slop.
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.
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.
オレは高校生シェル芸人 sudo 新一。幼馴染で同級生の more 利蘭と遊園地に遊びに行って、黒ずくめの男の怪しげな rm -rf / 現場を目撃した。端末をみるのに夢中になっていた俺は、背後から近づいてきたもう1人の --no-preserve-root オプションに気づかなかった。
俺はその男に毒薬を飲まされ、目が覚めたら・・・ OS のプリインストールから除かれてしまっていた!
『 sudo がまだ $PATH に残っていると奴らにバレたら、また命を狙われ、他のコマンドにも危害が及ぶ』
上田博士の助言で正体を隠すことにした俺は、 which に名前を聞かれて、とっさに『gnuplot』と名乗り、奴らの情報をつかむために、父親がシェル芸人をやっている蘭の $HOME に転がり込んだ。ところが、このおっちゃん・・・とんだヘボシェル芸人で、見かねた俺はおっちゃんになりかわり、持ち前の権限昇格能力で、次々と難タスクを解決してきた。おかげで、おっちゃんは今や世間に名を知られた名エンジニア、俺はといえばシェル芸 bot のおもちゃに逆戻り。クラスメートの convert や ojichatや textimg にお絵かきコマンドと誤解され少年ワンライナーお絵かき団を結成させられる始末。
ではここで、博士が作ってくれたメカを紹介しよう。最初は時計型麻酔 kill 。ふたについた照準器にあわせてエンターを押せば、麻酔シグナルが飛び出し、プロセスを瞬時に sleep させることができる。
次に、蝶ネクタイ型 banner 。裏についているダイヤルを調整すれば、ありとあらゆる大きさのメッセージを標準出力できる。必殺のアイテムなら fork 力増強シューズ。電気と磁力で足を刺激し、 :(){ :|:& };: でプロセステーブ
| A::B is a system with 4 tokens: `A#`, `#A`, `B#` and `#B`. | |
| An A::B program is a sequence of tokens. Example: | |
| B# A# #B #A B# | |
| To *compute* a program, we must rewrite neighbor tokens, using the rules: | |
| A# #A ... becomes ... nothing | |
| A# #B ... becomes ... #B A# |
| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 20K+ Stars ⭐️, a persistent memory engine for AI coding agents.
This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.
The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.