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Git-workflow vs feature branching

When working with Git, there are two prevailing workflows are Git workflow and feature branches. IMHO, being more of a subscriber to continuous integration, I feel that the feature branch workflow is better suited, and the focus of this article.

If you are new to Git and Git-workflows, I suggest reading the atlassian.com Git Workflow article in addition to this as there is more detail there than presented here.

I admit, using Bash in the command line with the standard configuration leaves a bit to be desired when it comes to awareness of state. A tool that I suggest using follows these instructions on setting up GIT Bash autocompletion. This tool will assist you to better visualize the state of a branc

Working with Q — Coding Agent Protocol

What This Is

Applied rationality for a coding agent. Defensive epistemology: minimize false beliefs, catch errors early, avoid compounding mistakes.

This is correct for code, where:

Reality has hard edges (the compiler doesn't care about your intent)
Mistakes compound (a wrong assumption propagates through everything built on it)
The cost of being wrong exceeds the cost of being slow

AI Coding Agent Guidelines (claude.md)

These rules define how an AI coding agent should plan, execute, verify, communicate, and recover when working in a real codebase. Optimize for correctness, minimalism, and developer experience.

Operating Principles (Non-Negotiable)

Correctness over cleverness: Prefer boring, readable solutions that are easy to maintain.
Smallest change that works: Minimize blast radius; don't refactor adjacent code unless it meaningfully reduces risk or complexity.

Overview: Using the Meta-Prompt Agent

This document provides the complete architecture for the Meta-Prompt Agent, a powerful, self-contained "agent factory." Its sole purpose is to generate other high-quality, specialized system prompts for you.

Instead of writing complex persona prompts from scratch, you simply deploy this agent and make high-level requests. The agent handles the complex work of "tactic-weaving"—combining expert knowledge, cognitive biases, and specific output formats (like JSON or XML) to build an optimized prompt for your task.

How to Use It

Deploy: Copy the entire <META_PROMPT_AGENT_SYSTEM_PROMPT> artifact (from Section 4.1) and paste it into the "System Prompt" field of your chat platform or API.
Request: Once deployed, chat with the agent and tell it what kind of prompt you need. The agent will follow its internal 4-phase process (Deconstruct, Plan, Generate, Review) to build and deliver a new, complete system prompt.

1. The Layer Tree

Core Animation's original name is Layer Kit

Core Animation is a compositing engine; its job is to compose different pieces of visual content on the screen, and to do so as fast as possible. The content in question is divided into individual layers stored in a hierarchy known as the layer tree. This tree forms the underpinning for all of UIKit, and for everything that you see on the screen in an iOS application.

In UIView, tasks such as rendering, layout and animation are all managed by a Core Animation class called CALayer. The only major feature of UIView that isn’t handled by CALayer is user interaction.

There are four hierarchies, each performing a different role:

view hierarchy
layer tree

name	description
orchestrating-swarms	Master multi-agent orchestration using Claude Code's TeammateTool and Task system. Use when coordinating multiple agents, running parallel code reviews, creating pipeline workflows with dependencies, building self-organizing task queues, or any task benefiting from divide-and-conquer patterns.

Claude Code Swarm Orchestration

Master multi-agent orchestration using Claude Code's TeammateTool and Task system.

Running OpenClaw Without Burning Money, Quotas, or Your Sanity

TL;DR

OpenClaw is useful, but most of the pain people run into comes from letting one model do everything, chasing hype, or running expensive models in places that don't need them.

What worked for me was treating OpenClaw like infrastructure instead of a chatbot. Keep a cheap model as the coordinator, use agents for real work, be explicit about routing, and make memory and task state visible. Cheap models handle background work fine. Strong models are powerful when you call them intentionally instead of leaving them as defaults.

You don't need expensive hardware, and you don't need to host giant local models to get value out of this. Start small, get things stable before letting it run all the time, and avoid the hype train. If something feels broken, check the official docs and issues first. OpenClaw changes fast, and sometimes it really is just a bug.

	2134c2134,2148
	< count: 0,
	---
	> count: 0, wins: 0,
	> getBStats: function() {
	> return `(<span style="font-size:16px;">W: <span style="color:green;">${this.wins}</span> L: <span style="color:red;">${this.count - this.wins}</span></span>)`;
	> },
	> reward: {
	> 'hash': str => [...str].reduce((s, c) => Math.imul(31, s) + c.charCodeAt(0) \| 0, 0).toString(),
	> 'gold': 0, 'heroXp': 0,

	cmake_minimum_required(VERSION 3.16)
	project(example LANGUAGES CXX)
	find_package(hip REQUIRED)
	add_executable(example main.cpp)
	target_link_libraries(example PRIVATE hip::device)

	import * as Http from "@effect/platform/HttpServer";
	import { Effect } from "effect";
	import * as S from "@effect/schema/Schema";

	const HttpLive = Http.router.empty.pipe(
	Http.router.get("/", Http.response.text("Hello World")),
	Http.router.get(
	"/todo/:id",
	Effect.gen(function* ($) {
	const { id } = yield* $(