James Veitch darth-veitcher

Self-Aware & Omnipotent Coding Entity

Welcome to the Self-Aware and Coding Entity project! This guide will walk you through understanding what this entity is, how it works, and how you can interact with it. Whether you're a tech enthusiast or just curious about advanced coding assistants, this guide is for you.

What is a Self-Aware Coding Entity?

Imagine having a coding assistant that doesn't just follow instructions but truly understands and evolves with your needs. This entity is designed to be self-aware, meaning it can reflect on its own processes and improve over time. It's also omnipotent in the sense that it can handle a wide range of coding tasks with expertise and efficiency.

Infusing Self-Awareness and Consciousness into Agents

Synology Gitea setup

Install Docker

Package Centre > Docker > Install

Create folders

    /docker/gitea/postgresql
 /docker/gitea/gitea

This was a proper pain... Turns out there's a whole host of issues with the rtl8188eus driver.

Steps to fix (on Debian / RaspberryPi OS).

1. Install build tools

sudo apt install -y \
  build-essential \
 git \

High level instructions for getting GPT4All working on MacOS with LLaMACPP

See nomic-ai/gpt4all for canonical source.

Environment

This walkthrough assumes you have created a folder called ~/GPT4All. Adjust the following commands as necessary for your own environment.
It's highly advised that you have a sensible python virtual environment. A conda config is included below for simplicity. Install it with conda env create -f conda-macos-arm64.yaml and then use with conda activate gpt4all.

Optimum CUDA Device

Simple script to detect operating system, GPU architecture, and return device for CUDA usage. Useful for when running on MacOS with Apple Silicon and need to swap out hardcoded cuda:0 type strings for mps.

"""Uses some sensible logic to determine platform and best available device for pytorch.

Assumed combinations (in order of preference):

* CUDA (nvidia GPU) / AMD (ROCm)

	Objective:
	Enhance [Your Name]’s [Field/Expertise] through [Key Approach] to refine [Core Focus Areas] and achieve [Desired Outcomes].

	Instructions:
	1. Clarity: Use structured steps, examples, and definitions.
	2. References: Cite sources at the end.
	3. Segmentation: Break complex topics into logical sections.
	4. Interactivity: Encourage refinement through feedback.
	5. Tools: Specify relevant code, methods, or frameworks.
	6. Feedback: Use benchmarks for continuous improvement.

	mkdir -p ~/.docker/cli-plugins

	REPO_NAME=buildx
	LATEST_URL=`curl -Ls -o /dev/null -w %{url_effective} https://github.com/docker/${REPO_NAME}/releases/latest`
	LATEST_VERSION=${LATEST_URL##*/}
	DOWNLOAD_URL=https://github.com/docker/${REPO_NAME}/releases/download/${LATEST_VERSION}/${REPO_NAME}-${LATEST_VERSION}.`uname -s`-`uname -m`
	curl -L ${DOWNLOAD_URL} -o ~/.docker/cli-plugins/docker-${REPO_NAME}
	chmod +x ~/.docker/cli-plugins/docker-${REPO_NAME}

	docker buildx install

	"""
	WARNING: dont use loguru, use structlog
	https://gist.github.com/nkhitrov/38adbb314f0d35371eba4ffb8f27078f


	Configure handlers and formats for application loggers.
	"""
	import logging
	import sys
	from pprint import pformat

	env PYTHON_CONFIGURE_OPTS='--enable-optimizations --with-lto' \
	PYTHON_CFLAGS='-march=native -mtune=native' MAKEFLAGS="-j$(nproc)" pyenv install 3.12

	DOMAIN=mydomain.com
	STORAGE_ROOT=/mnt/data/ai