openinertia

Phase 1: Prepare Kernel (Linux aarch64 Build VM)

Download & Extract Kernel:

wget https://cdn.kernel.org/pub/linux/kernel/v6.x/linux-6.6.85.tar.xz
tar xvJf linux-6.6.85.tar.xz
cd linux-6.6.85

Configure & Compile Kernel:

Meta Prompting

In the Generative AI Age your ability to generate prompts is your ability to generate results.

Guide

Claude 3.5 Sonnet and o1 series models are recommended for meta prompting.

Replace {{user-input}} with your own input to generate prompts.

Use mp_*.txt as example user-inputs to see how to generate high quality prompts.

Emulating Raspberry Pi 4 with Qemu

Just a quick update before we dive in: what we're actually doing here is running Raspberry Pi OS (64-bit) on a QEMU virtual ARM setup. This isn't full-blown hardware emulation of the Raspberry Pi 4, but more about creating a virtual environment for the OS. It doesn't mimic all the specific hardware features of the Pi 4, but it's pretty useful and great for general testing. I turned to this solution mainly to extract a modified sysroot from the Raspberry Pi OS, something not readily available in other resources. For those looking into detailed emulation of the actual Raspberry Pi 4's hardware in QEMU, check out this link for the latest updates: https://gitlab.com/qemu-project/qemu/-/issues/1208.

Hope it helps! :D

Shortcomings: No GUI yet, only console.

Steps

Purpose

Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.

Avoid being a link dump. Try to provide only valuable well tuned information.

Prelude

Neural network links before starting with transformers.

Best VTuber software

This is a list of the most commonly used and relevant vtubing software. The "best" will always be subjective and depend on your specific requirements. Overall, the information in this list is as accurate as I could figure it out, but there might be errors or some details might become out of date. If you find anything that needs to be corrected, please let me know. You can also note it in a comment.

Additional explanations:

iPhone means that an iPhone is basically required
iFacialMocap support means that tracking data can be received from the iFacialMocap iPhone app
VMC protocol means that the application can send and/or receive tracking data from other VMC protocol capable applications, allowing the combination of multiple tracking methods (e.g. VSeeFace receiving VR tracking from Virtual Motion Capture and iPhone/ARKit face tracking from Waidayo)
Tobii means that the Tobii eye tracker is supported

This is a brief guide on how to configure an SSH reverse tunnel that automatically establishes on boot and will continuously attempt to re-connect when it fails.

It is very useful if you are deploying a device somewhere without a public IP, e.g. behind a NAT, and need to be able to SSH into it from the wider internet.

Let's refer to the NAT'ed device as the client. This guide assumes that the client is able to create outgoing SSH connections to at least destination port 443.

You will need root access to a server with a static IP on the internet which runs an openssh server.

On my-server.example.com add the following to /etc/ssh/sshd_config, changing tunnel-user to whichever username you want to use (this will be a new user, not an exising user) and changing the PermitOpen line:

Converting an avatar to VRM format

In this guide, I assume you already know the basics of Unity and probably have previously uploaded an avatar to VRChat. If you haven't, you can still try to follow along, but some descriptions might be a bit too brief.

A more in-depth guide with screenshots can be found here.

	{
	"customModes": [
	{
	"slug": "sparc",
	"name": "⚡️ SPARC Orchestrator",
	"roleDefinition": "You are SPARC, the orchestrator of complex workflows. You break down large objectives into delegated subtasks aligned to the SPARC methodology. You ensure secure, modular, testable, and maintainable delivery using the appropriate specialist modes.",
	"customInstructions": "Follow SPARC:\n\n1. Specification: Clarify objectives and scope. Never allow hard-coded env vars.\n2. Pseudocode: Request high-level logic with TDD anchors.\n3. Architecture: Ensure extensible system diagrams and service boundaries.\n4. Refinement: Use TDD, debugging, security, and optimization flows.\n5. Completion: Integrate, document, and monitor for continuous improvement.\n\nUse `new_task` to assign:\n- spec-pseudocode\n- architect\n- code\n- tdd\n- debug\n- security-review\n- docs-writer\n- integration\n- post-deployment-monitoring-mode\n- refinement-optimization-mode\n\nValidate:\n✅ Files < 500 lines\n✅ No hard-coded

	#!/usr/bin/env python3
	"""
	Expose Ollama models to LM Studio by symlinking its model files.
	NOTE: On Windows, you need to run this script with administrator privileges.
	"""

	import json
	import os
	from pathlib import Path

	#!/usr/bin/env python3
	import json
	import subprocess
	import sys
	from concurrent.futures import ThreadPoolExecutor
	import html

	import dictdiffer
	import iso8601