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Installing tmux-256color for macOS

macOS 10.15.5
tmux 3.1b

macOS has ncurses version 5.7 which does not ship the terminfo description for tmux. There're two ways that can help you to solve this problem.

The Fast Blazing Solution

Instead of tmux-256color, use screen-256color which comes with system. Place this command into ~/.tmux.conf or ~/.config/tmux/tmux.conf(for version 3.1 and later):

After reading Why I'm Frequently Absent from Open Source by James Long and listening the corresponding The Changelog episode, I dwelt on the idea and believe that open source maintainers...

... should never be ashamed if they don't have time for a project.
... should be honest with themselves and open with their users so that everybody can be on the same page
... are people and they have at one time or another responsibilities or hardships that they need to attend to which reasonably take them away from a project
... may also reasonbly decide that they don't like the direction of a project or that they would like to explore other things and may leave a project permanently.

Along this line of thinking I've created a set of descriptions for different levels at which a project might be maintained. A maintainer can use these to announce to their users the current ability that they have to dedicate to a pr

You probably don't know how to do Prompt Engineering

(This post could also be titled "Features missing from most LLM front-ends that should exist")

Apologies for the snarky title, but there has been a huge amount of discussion around so called "Prompt Engineering" these past few months on all kinds of platforms. Much of it is coming from individuals who are peddling around an awful lot of "Prompting" and very little "Engineering".

Most of these discussions are little more than users finding that writing more creative and complicated prompts can help them solve a task that a more simple prompt was unable to help with. I claim this is not Prompt Engineering. This is not to say that crafting good prompts is not a difficult task, but it does not involve doing any kind of sophisticated modifications to general "template" of a prompt.

Others, who I think do deserve to call themselves "Prompt Engineers" (and an awful lot more than that), have been writing about and utilizing the rich new eco-system

Docker Swarm in LXC Containers

Part of collection: Hyper-converged Homelab with Proxmox

After struggling for some days, and since I really needed this to work (ignoring the it can't be done vibe everywhere), I managed to get Docker to work reliable in privileged Debian 12 LXC Containers on Proxmox 8

(Unfortunately, I couldn't get anything to work in unprivileged LXC Containers)

There are NO modifications required on the Proxmox host or the /etc/pve/lxc/xxx.conf file; everything is done on the Docker Swarm host. So the only obvious candidate who could break this setup, are future Docker Engine updates!

Getting confused about the piles of development tools that people use for Javascript? Here's a quick index of what is used for what.

Keep in mind that you shouldn't add tools to your workflow for the sake of it. While you'll see many production systems using a wide range of tools, these tools are typically used because they solved a concrete problem for the developers working on it. You should not add tools to your project unless you have a concrete problem that they can solve; none of the tools here are required.

Start with nothing, and add tools as needed. This will keep you from getting lost in an incomprehensible pile of tooling.

Build/task runners

Typical examples: Gulp, Grunt

	// Filename: HttpServer.cs
	// Author: Benjamin N. Summerton <define-private-public>
	// License: Unlicense (http://unlicense.org/)

	using System;
	using System.IO;
	using System.Text;
	using System.Net;
	using System.Threading.Tasks;

	#load the dataset
	from datasets import load_dataset

	#the ag_news dataset has train-test split so use the train-test split method
	dataset = load_dataset("ag_news", split="train").train_test_split(
	test_size=0.2, shuffle=True, seed=41)

	splits = ["train", "test"]

	#pre-process the dataset

	import tiktoken
	import langdetect
	T = tiktoken.get_encoding("o200k_base")

	length_dict = {}

	for i in range(T.n_vocab):
	try:
	length_dict[i] = len(T.decode([i]))
	except:

	{
	"apc.stylesheet": {
	".editor-group-watermark > .letterpress": "background-image: url(https://raw.githubusercontent.com/Aikoyori/ProgrammingVTuberLogos/main/VSCode/VSCode.png) !important; width: unset !important; height: 250px; aspect-ratio: 16/9 !important; filter: drop-shadow(5px 5px 5px #222); margin-bottom: 2rem;"
	}
	}