Ari auser

Rust Engineering Best Practices

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.

Status and terminology

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

Notes on this tweet.

The screenshots were taken on different sessions.
The entire sessions are included on the screenshots.
I lost the original prompts, so I had to reconstruct them, and still managed to reproduce.
The "compressed" version is actually longer! Emojis and abbreviations use more tokens than common words.

A curated list of command-line utilities written in Rust

Note: I have moved this list to a proper repository. I'll leave this gist up, but it won't be updated. To submit an idea, open a PR on the repo.

Note that I have not tried all of these personally, and cannot and do not vouch for all of the tools listed here. In most cases, the descriptions here are copied directly from their code repos. Some may have been abandoned. Investigate before installing/using.

The ones I use regularly include: bat, dust, fd, fend, hyperfine, miniserve, ripgrep, just, cargo-audit and cargo-wipe.

atuin: "Magical shell history"
bandwhich: Terminal bandwidth utilization tool

	#!/usr/bin/env bash
	# esp32-ai: test a custom prompt against an already-trained, already-flashed model.
	# Does NOT retrain and does NOT rewrite the 15MB model partition.
	#
	# Usage:
	# ./run_custom_prompt.sh "Once there was a robot"
	set -euo pipefail

	if [ "$#" -lt 1 ]; then
	echo "Usage: $0 \"Your custom prompt\""

	#!/usr/bin/env bash
	# esp32-ai: clone, train, export, build, and flash — end to end
	# Reproduces github.com/slvDev/esp32-ai from a bare board.
	# Requires: ESP32-S3 N16R8 (16MB flash, 8MB PSRAM).
	#
	# Usage:
	# ./build_and_flash.sh # full run: data prep, train, export, build, flash
	# ./build_and_flash.sh --skip-train # reuse an existing firmware/model/model.bin, just build + flash
	set -euo pipefail

	use async_trait::async_trait;
	use chrono::Local;
	use graph_flow::{
	Context, ExecutionStatus, FlowRunner, GraphBuilder, GraphStorage, InMemoryGraphStorage,
	InMemorySessionStorage, NextAction, Session, SessionStorage, Task, TaskResult,
	};
	use image::{ImageBuffer, Rgb};
	use minifb::{Key, Window, WindowOptions};
	use nokhwa::{
	pixel_format::RgbFormat,

	use async_trait::async_trait;
	use chrono::Local;
	use graph_flow::{
	Context, ExecutionStatus, FlowRunner, GraphBuilder, GraphStorage, InMemoryGraphStorage,
	InMemorySessionStorage, NextAction, Session, SessionStorage, Task, TaskResult,
	};
	use image::{ImageBuffer, Rgb};
	use minifb::{Key, Window, WindowOptions};
	use nokhwa::{
	pixel_format::RgbFormat,

	# 2023-11-27 MIT LICENSE

	Here's the open source version of my ChatGPT game MonkeyIslandAmsterdam.com.

	It's an unofficial image+text-based adventure game edition of Monkey Island in Amsterdam, my home town.

	Please use it however you want. It'd be nice to see more ChatGPT-based games appear from this. If you get inspired by it, please link back to my X https://x.com/levelsio or this Gist so more people can do the same!

	Send me your ChatGPT text adventure game on X, I'd love to try it!

	<!--
	This was a sandboxing challenge where the JS language is presenteded in the form of exotic, made-up language.
	It's almost properly sandboxed but there is one bug that players needed to find.
	The bug I found was to construct HTML comment (<!--) that is understood by JS and which makes it possible to ignore one semicolon
	and then to concat array expression with variable name, like $var$['eval']. To get reference to eval we used DOM clobbering
	and defined <iframe name=$win$>
	-->

	<iframe name=$win$></iframe>
	<x-program>

	"""
	Built on top of this gist by @karpathy:
	https://gist.github.com/karpathy/00103b0037c5aaea32fe1da1af553355

	stable diffusion dreaming over text prompts
	creates hypnotic moving videos by smoothly walking randomly through the sample space

	example way to run this script:
	$ python stable_diffusion_walk.py --prompts "['blueberry spaghetti', 'strawberry spaghetti']" --seeds 243,523 --name berry_good_spaghetti