Andrews Cordolino Sobral andrewssobral

List of terminal-based developer tools that deliver value in 30 seconds

miniogre: from source code to reproducible envionments, in seconds
- pip install miniogre
- Go to project folder
- Run miniogre run
freeze: generate images of code and terminal output
- brew install charmbracelet/tap/freeze
- freeze --execute "ls -ltra"
uv: An extremely fast Python package installer and resolver, written in Rust

Setting Up MCP Servers on Windows

A step-by-step guide to setting up Model Context Protocol (MCP) servers for Claude Desktop on Windows.

Prerequisites

Install Node.js (v18.x or later)
- Download from: https://nodejs.org/
- Verify installation by opening PowerShell and running:
```
node --version
```

npm --version

High-Performance Matrix Multiplication

This is a short post that explains how to write a high-performance matrix multiplication program on modern processors. In this tutorial I will use a single core of the Skylake-client CPU with AVX2, but the principles in this post also apply to other processors with different instruction sets (such as AVX512).

Intro

Matrix multiplication is a mathematical operation that defines the product of

	######################### Preamble ###########################################
	SHELL := bash
	.ONESHELL:
	.SHELLFLAGS := -eu -o pipefail -c
	.DELETE_ON_ERROR:
	.SECONDEXPANSION:
	MAKEFLAGS += --warn-undefined-variables
	MAKEFLAGS += --no-builtin-rules
	MAKEFLAGS += -j$(shell nproc)

	while true; do
	osascript -e '
	tell application "System Events"
	if exists process "Claude" then
	tell process "Claude"
	if exists button "Allow for This Chat" of group 2 of group 1 of group 1 of group 1 of UI element 2 of group 1 of group 1 of group 1 of group 1 of window "Claude" then
	click button "Allow for This Chat" of group 2 of group 1 of group 1 of group 1 of UI element 2 of group 1 of group 1 of group 1 of group 1 of window "Claude"
	log "clicked allow button"
	end if
	end tell

	cmake_minimum_required(VERSION 3.27)

	project(_ext LANGUAGES CXX)

	# ----------------------------- Setup -----------------------------
	set(CMAKE_CXX_STANDARD 17)
	set(CMAKE_CXX_STANDARD_REQUIRED ON)
	set(CMAKE_POSITION_INDEPENDENT_CODE ON)

	option(BUILD_SHARED_LIBS "Build as a shared library" ON)

	# -----------------------------------------------------------------------------
	# AI-powered Git Commit Function
	# Copy paste this gist into your ~/.bashrc or ~/.zshrc to gain the `gcm` command. It:
	# 1) gets the current staged changed diff
	# 2) sends them to an LLM to write the git commit message
	# 3) allows you to easily accept, edit, regenerate, cancel
	# But - just read and edit the code however you like
	# the `llm` CLI util is awesome, can get it here: https://llm.datasette.io/en/stable/

	gcm() {

	# Requires:
	# pip install pyobjc-framework-Metal
	import numpy as np
	import Metal

	# Get the default GPU device
	device = Metal.MTLCreateSystemDefaultDevice()

	# Make a command queue to encode command buffers to
	command_queue = device.newCommandQueue()

	# This is a modified version of TRL's `SFTTrainer` example (https://github.com/huggingface/trl/blob/main/examples/scripts/sft_trainer.py),
	# adapted to run with DeepSpeed ZeRO-3 and Mistral-7B-V1.0. The settings below were run on 1 node of 8 x A100 (80GB) GPUs.
	#
	# Usage:
	# - Install the latest transformers & accelerate versions: `pip install -U transformers accelerate`
	# - Install deepspeed: `pip install deepspeed==0.9.5`
	# - Install TRL from main: pip install git+https://github.com/huggingface/trl.git
	# - Clone the repo: git clone github.com/huggingface/trl.git
	# - Copy this Gist into trl/examples/scripts
	# - Run from root of trl repo with: accelerate launch --config_file=examples/accelerate_configs/deepspeed_zero3.yaml --gradient_accumulation_steps 8 examples/scripts/sft_trainer.py