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Agents Conventions

This document harmonizes Makefile-derived developer conventions with augmentative best-practices inspired by Pragmatic AI Labs and the fast.ai developer style guide. It is written to be non-breaking and augmentive: preserve existing Makefile behavior while recommending optional improvements for reproducibility, discoverability, CI parity, and notebook hygiene.

Purpose

Provide a concise, actionable reference for:

• How agents (developers, automation) should run tasks via Makefile.
• How to keep local development and CI behavior consistent.
• Notebook and coding style guidance informed by Pragmatic AI Labs on YT and fast.ai.

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A guide to George Polya's "How to Solve It"

Introduction

George Polya (1887-1985) was a Hungarian mathematician and educator who revolutionized our understanding of problem-solving and teaching. While he made significant contributions to mathematics, his most enduring legacy is his insights into how people learn and solve problems. His 1945 book "How to Solve It" has influenced educators across numerous fields, extending far beyond mathematics.

Polya's Teaching Philosophy

Polya's approach centered on three key principles. First, he championed active learning, believing students learn best by discovering solutions themselves rather than being told answers. Second, he emphasized heuristic thinking - the art of guided discovery and learning from experience, viewing problem-solving as a skill that can be developed through practice. Third, he developed sophisticated questionin

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Pokémon Battle Advisor try here https://shuyib-pokemonbattleadvisor.web.val.run

Overview

The Pokémon Battle Advisor is a web application designed to help Pokémon trainers strategize their battles by providing detailed and realistic battle recommendations. Users can specify their Pokémon team or randomize a team, and then receive tailored battle strategies based on their team and the opponent's Pokémon. The application leverages the CEREBRAS API to generate these recommendations, ensuring they adhere to official Pokémon game mechanics.

Features

• Specify Your Pokémon Team: Users can enter the names of up to three Pokémon to include in their team.
• Randomize Pokémon Team: Users can opt to randomize their Pokémon team, fetching three random Pokémon from the PokéAPI.
• Challenger Pokémon Input: Users can enter the name of the opponent's Pokémon to receive a battle recommendation.
• Battle Recommendations: The app provides detailed battle strategies, including recommended Pokémon, strate

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#!/bin/bash

# install ffmpeg if not already installed
if ! command -v ffmpeg &> /dev/null
then
    echo "ffmpeg could not be found"
    echo "Installing ffmpeg..."
    sudo apt-get install ffmpeg
fi

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# Thank you @Earthly https://www.youtube.com/watch?v=w2UeLF7EEwk
# Can be adapted to pipenv, and poetry
# Other languages coming soon especially R and Julia

# .ONESHELL tells make to run each recipe line in a single shell
.ONESHELL:

# .DEFAULT_GOAL tells make which target to run when no target is specified
.DEFAULT_GOAL := all

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SHELL := /usr/bin/env bash

#######
# Help
#######

.DEFAULT_GOAL := help
.PHONY: help

help:

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#  ---------------------------------------------------------------------------
#
#  Description:  This file holds all my BASH configurations and aliases
#
#  Sections:
#  1.   Environment Configuration
#  2.   Make Terminal Better (remapping defaults and adding functionality)
#  3.   File and Folder Management
#  4.   Searching
#  5.   Process Management

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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:

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