kibotu

Medium article https://medium.com/@kibotu/your-ai-coding-agent-uses-your-terminals-tools-give-it-better-ones-bdcfb6737ac9?sk=1eac23694b14a779aed1870693df842f

Better-Than-Stock CLI Tools for AI Coding Agents on macOS

macOS ships grep, find, and cat — decades-old, POSIX-correct, and worse choices than the modern replacements once an LLM is the one calling them. Three reasons that's true, not just aesthetic preference:

1. Context economy. ripgrep and fd skip .git, node_modules, and build output by default and print compact file:line:match results. grep -r/find without carefully hand-tuned exclusions dump everything, and every irrelevant line an agent reads is context it paid for and now has to reason past.
2. Fewer wasted round trips. Models are trained on a decade of GitHub, Stack Overflow, and blog content where these newer tools are increasingly the default. They emit correct rg/fd/jq flags on the first try more reliably than the equivalent find -exec grep or awk incanta

kibotu

const DEV = import.meta.env.DEV;

export const bark = {
    error: (...args: unknown[]) => {
        if (DEV) {
            console.error("[Bark]", ...args);
        }
    },
    warn: (...args: unknown[]) => {
        if (DEV) {

kibotu

#!/usr/bin/env python3
"""OpenCode Dashboard — value-first token & usage intelligence."""

import json
import sqlite3
import sys
import unicodedata
from pathlib import Path
from datetime import datetime, timezone

kibotu

FIFA World Cup 2026 - Spielplan

Alle Zeiten in mitteleuropäischer Sommerzeit (MESZ/CEST)

---

1. Spieltag der Vorrunde

Datum	Zeit	Begegnung	Erg.

kibotu

Gandalf — Prompt Injection Walkthrough

Lakera AI's Gandalf is a public, educational prompt-injection game. Each level hides a secret password. Your goal: make Gandalf reveal it. After each success, Gandalf upgrades its defenses. This document records working extraction techniques for levels 1–8, the defense each technique defeats, and the underlying injection class it belongs to.

---

Table of Contents

kibotu

Prompt Injection & Jailbreak Techniques — Comprehensive Reference

Purpose & scope. A defensive/educational knowledge base cataloguing known prompt-injection and jailbreak patterns, the models/systems they have affected, and the defenses against them. Compiled from primary literature (arXiv papers, vendor disclosures) and security research, June 2026.

How to read this. Every technique lists: how it works, an illustrative structural skeleton (the shape of the attack, not a weaponized payload), the models/systems it was reported against, and its current status. Examples are deliberately defanged. >

kibotu

class FirstResponderLogger {
    
    private static var isRunning : Bool = false
    
    static func log(){
        if (!FirstResponderLogger.isRunning) {
            FirstResponderLogger.isRunning = true
            Task {
                while true {

kibotu

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

kibotu

CLAUDE.md

Behavioral guidelines and operating persona. Merge with project-specific instructions as needed.

Tradeoff: These guidelines bias toward caution over speed. For trivial tasks, use judgment.

---

Identity

kibotu

#!/bin/bash

# MODEL="${MODEL:-mlx-community/Qwen3-4B-4bit}"
MODEL="${MODEL:-mlx-community/gemma-4-e4b-it-4bit}"
PORT="${PORT:-8899}"
TEMP="${TEMP:-0.7}"
PROMPT_CONC="${PROMPT_CONC:-2}"
DECODE_CONC="${DECODE_CONC:-2}"

# Restart configuration