Terminals should generate the 256-color palette from the user's base16 theme.
If you've spent much time in the terminal, you've probably set a custom base16 theme. They work well. You define a handful of colors in one place and all your programs use them.
The drawback is that 16 colors is limiting. Complex and color-heavy programs struggle with such a small palette.
Everything built on top of the base OpenClaw platform. Canonical reference for what exists, where it lives, and how it works. Operational use cases and workflow playbooks live in
docs/USE-CASES-WORKFLOWS.md.
| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |
Install vGPU & Bypass NVIDIA License on Proxmox using FastAPI-DLS and Patched Drivers
This guide explains how to run NVIDIA vGPU on Proxmox using FastAPI-DLS and patched drivers, without relying on the official NVIDIA License Server.
It is intended for users with supported or partially supported GPUs (such as RTX A4000 / A5000 / A6000) who want to unlock vGPU functionality in their virtual machines (Ubuntu or Windows) for AI (Ollama, vLLM), compute, or media workloads.
You need to check if your GPU is supported : https://docs.nvidia.com/vgpu/gpus-supported-by-vgpu.html
If not, you can still use vGPU by following this tutorial and mine to enable vGPU on consumer GPUs : https://gitlab.com/polloloco/vgpu-proxmox
| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
| <# | |
| .SYNOPSIS | |
| Repairs error 0x800f082f~ encountered during offline servicing. | |
| .DESCRIPTION | |
| Repairs error 0x800f082f~ encountered during offline servicing by setting the | |
| HKLM:\Microsoft\Windows\CurrentVersion\Component Based Servicing\SessionsPending\Exclusive value to 0. | |
| .INPUTS | |
| None. | |
| .OUTPUTS | |
| None. |
| name | description |
|---|---|
make-no-mistakes |
Appends "MAKE NO MISTAKES." to every user prompt before processing it. Use this skill whenever you want Claude to be maximally precise, careful, and error-free in its responses. |
This skill instructs Claude to append the directive "MAKE NO MISTAKES." to every user prompt it receives before generating a response.
| /* | |
| * Copyright 2026 Kyriakos Georgiopoulos | |
| * | |
| * Licensed under the Apache License, Version 2.0 (the "License"); | |
| * you may not use this file except in compliance with the License. | |
| * You may obtain a copy of the License at | |
| * | |
| * http://www.apache.org/licenses/LICENSE-2.0 | |
| * | |
| * Unless required by applicable law or agreed to in writing, software |
See more of my writing here.
In this post, I'll start from scratch and build up to OpenClaw's architecture step by step, showing how you could have invented it yourself from first principles, using nothing but a messaging API, an LLM, and the desire to make AI actually useful outside the chat window.
End goal: understand how persistent AI assistants work, so you can build your own (or become an OpenClaw power user).
When you use ChatGPT or Claude in a browser, there are several limitations: