A complete project standard for writing correct, explicit, allocation-aware, production-grade Rust.

This document is intended to replace the previous rust-best-practices.md Gist in full. It integrates ownership, API design, error handling, testing, linting, performance, debug_assert!() usage, and heap-allocation discipline into one coherent set of rules.

This is a normative engineering guide, not an introductory Rust tutorial.

Each step was A/B tested against the commit just before it (requests/sec at 16 clients, median of 3–5 alternating reps). Every step had its own baseline, so the percentages don't multiply into the totals exactly. A dash means no notable change on that route.

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

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.

记录/修订时间：2026-09-24 本文记录 TN3399 v3 实机配置、编译过程、legacy RGA2 补丁、FFmpeg/libass 替换、Jellyfin wrapper 和最终验证结果。 文中不包含真实主机名、UUID、账号等私有信息。

https://learnjapanese.moe

Yomitan (formerly Yomichan) is a browser extension created for Japanese which enables its users to quickly and efficiently look up words on a webpage.
While it is made for Japanese, it is possible to use it for other languages, including Chinese.

Yomitan is available for both Chromium and Firefox based browsers. You can find the respective versions below.

⚠️ NOTE (20.5.2023): I don't really use this function at all anymore since there are now tools such as Taskfile, which reads the .env file for me sparing me the need to pollute my session with environment variables.

I've been using Fish shell for years which is great and all, but one thing that has got me frustrated is using it with .env files.

When attempting to run source .env in a project, I usually encounter this problem: