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AGENTS.md Best Practices for AI Coding Assistants: Comprehensive Guide

AGENTS.md has emerged as the de facto open standard for guiding AI coding assistants, now adopted by over 20,000 repositories and formalized in August 2025 through collaboration between OpenAI, Google, Cursor, Factory, and Sourcegraph. This file acts as a "README for machines"—providing structured, technical context that helps AI assistants write better code from the start. For Python + AWS + Terraform projects, a well-crafted AGENTS.md dramatically reduces friction, ensuring generated code follows your conventions, uses the right tools, and adheres to security requirements.

What is AGENTS.md and why it matters

AGENTS.md is a dedicated Markdown file that complements, not replaces, README.md. While README targets human developers with project overviews and quick-start guides, AGENTS.md contains detailed technical instructions specifically for AI coding agents. Think of it as onboarding documentation for an AI team member: ex

Installing the NVIDIA driver, CUDA and cuDNN on Linux (Ubuntu 20.04)

This is a companion piece to my instructions on building TensorFlow from source. In particular, the aim is to install the following pieces of software

NVIDIA graphics card driver (v450.57)
CUDA (v11.0.2)
cuDNN (v8.0.2.39)

on an Ubuntu Linux system, in particular Ubuntu 20.04.

Convert PascalVOC Annotations to YOLO

This script reads PascalVOC xml files, and converts them to YOLO txt files.

Note: This script was written and tested on Ubuntu. YMMV on other OS's.

Disclaimer: This code is a modified version of Joseph Redmon's voc_label.py

Instructions:

Place the convert_voc_to_yolo.py file into your data folder.

Arch Linux tweaks

This is a collection of the tweaks and modification I've made to my Arch Linux installation over the months. These may be applicable to other distros, but please check first before doing anything. I also included Arch Wiki references for all the procedures I mentioned. My recommendation is not to blindly follow this gist but to always check with the Arch Linux wiki first. Things move fast and by the time you're reading this my gist may be out of date. Lastly, the golden rule: never execute a command you don't understand.

Installing the KDE Plasma desktop

My current DE of choice is KDE's Plasma. I find it just about perfect.

There are various ways to install it on Arch. The most popular one is to install plasma and plasma-applications, but I don't like doing that because it comes with too many programs I'll never use. I, instead, install the base plasma group, remove the few extra packages that come with it, then I finish off by installing a few KDE apps that don't come with th

R to python data wrangling snippets

The dplyr package in R makes data wrangling significantly easier. The beauty of dplyr is that, by design, the options available are limited. Specifically, a set of key verbs form the core of the package. Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe. Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R. The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package).

dplyr is organised around six key verbs:

Build tensorflow on OSX with NVIDIA CUDA support (GPU acceleration)

These instructions are based on Mistobaan's gist but expanded and updated to work with the latest tensorflow OSX CUDA PR.

	#!/bin/bash

	### steps ####
	# verify the system has a cuda-capable gpu
	# download and install the nvidia cuda toolkit and cudnn
	# setup environmental variables
	# verify the installation
	###

	### to verify your gpu is cuda enable check

	#
	# Configuration files for Manjaro i3 on Lenovo Yoga 2 pro
	# to scale properly on HiDPI (3200x1800)
	#
	#####################################################
	~/.profile #
	#####################################################

	# UI element scaling, icons
	export GDK_SCALE=2

	## https://timogrossenbacher.ch/2016/12/beautiful-thematic-maps-with-ggplot2-only/
	library(raster)
	library(sp)
	library(rgdal)
	library(viridisLite)
	library(rasterVis)

	## Read age data
	data <- read.csv("input/avg_age_15.csv", stringsAsFactors = F)
	## Read shapefile

	# If you come from bash you might have to change your $PATH.
	export PATH=$HOME/bin:/usr/local/bin:/home/jpenuchot/node_modules/.bin:$HOME/.cargo/bin:$PATH

	# Path to your oh-my-zsh installation.
	export ZSH=$HOME/.oh-my-zsh

	# Set name of the theme to load. Optionally, if you set this to "random"
	# it'll load a random theme each time that oh-my-zsh is loaded.
	# See https://github.com/robbyrussell/oh-my-zsh/wiki/Themes
	ZSH_THEME="robbyrussell"