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Decoding AI: How Large Language Models "Think" Like You Do

Have you ever wondered what's going on "inside the head" of an AI like ChatGPT? We often hear terms like "neural networks" and "algorithms," which can sound a bit like magic. But what if we told you that, in some fundamental ways, Large Language Models (LLMs) operate with processes remarkably similar to your own brain when you're trying to understand something complex, like a piece of code?

Let's dive into a fascinating analogy that maps the human cognitive process of reading code to the architecture of an LLM. Our goal is to demystify AI by showing how its "thinking" can be understood through the lens of our own memory systems.

The Human Brain Model: LTM, STM, and Working Memory

When you read a piece of code, your brain engages three core memory systems:

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The Greenfield Economics Just Shifted

A $50k contract delivered for $297. Not a typo.

The "Ralph Wiggum loop" is an autonomous coding pattern where an AI agent restarts with fresh context in each iteration, reading filesystem state and tests to decide what to fix next. It sounds simple because it is. But the implications are profound.

For greenfield work (new projects, clean slate), the unit economics have inverted. The marginal cost of labor now approaches compute costs, not human salaries. This means outsourcing firms' traditional pricing model is broken for this class of work.

What this means for your Build vs. Buy strategy:

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#!/bin/bash
set -e

usage() {
  echo "Usage:"
  echo "  switch-claude [-y] {pro|zai}"
  echo "  switch-claude status"
}

AUTO_YES=false

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• Delete unused or obsolete files when your changes make them irrelevant (refactors, feature removals, etc.), and revert files only when the change is yours or explicitly requested. If a git operation leaves you unsure about other agents' in-flight work, stop and coordinate instead of deleting.
• Before attempting to delete a file to resolve a local type/lint failure, stop and ask the user. Other agents are often editing adjacent files; deleting their work to silence an error is never acceptable without explicit approval.
• NEVER edit .env or any environment variable files—only the user may change them.
• Coordinate with other agents before removing their in-progress edits—don't revert or delete work you didn't author unless everyone agrees.
• Moving/renaming and restoring files is allowed.
• ABSOLUTELY NEVER run destructive git operations (e.g., git reset --hard, rm, git checkout/git restore to an older commit) unless the user gives an explicit, written instruction in this conversation. Treat t

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╭─── Claude Code v2.0.61 ──────────────────────────────────────────────────────╮
│                                                   │ Tips for getting started │
│                   Welcome back!                   │ Run /init to create a C… │
│                                                   │ ──────────────────────── │
│                      ▐▛███▜▌                      │ Recent activity          │
│                     ▝▜█████▛▘                     │ No recent activity       │
│                       ▘▘ ▝▝                       │                          │
│                                                   │                          │
│          Sonnet 4.5 · API Usage Billing           │                          │

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Generate a production‑grade Rust workspace that implements an autonomous, multi‑agent Test‑Driven Development machine for code katas. The tool must run locally as a CLI, orchestrate three agents (tester, implementor, refactorer), and follow a strict red‑green‑refactor loop for a configurable number of steps. It must store state in git and allow each agent to read the last commit message, the last git diff, and the entire working tree.

Objectives

• Create a Rust workspace with clean boundaries, strong types, and testable modules.
• Implement an orchestrator that cycles over agents: tester → implementor → refactorer → implementor → … for N steps.
• Each agent must run tests and compile checks, and must be able to edit the codebase across multiple files and modules.
• Persist progress via conventional commits. Commit messages must include all context needed by the next agent.
• Consume a kata description from a Markdown file. Agents should align their actions to that document.
• Support pluggable LLMs throu

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#!/usr/bin/env bash
# Requirements: GitHub CLI (gh) configured with access to the repository

set -euo pipefail

# Default branch is 'master' if not provided
BRANCH=${1:-master}

# Detect remote URL for origin or fallback to first remote
REMOTE_URL=$(git remote get-url origin 2>/dev/null || git remote get-url "$(git remote | head -n1)" 2>/dev/null || true)

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#!/usr/bin/env bash
set -euo pipefail

# Local mirror of CI steps (fmt, clippy, build, docs, tests) per crate.
# Usage: ./ci-local.sh [options]
# Options:
#   -a, --all           Run for all crates (ignore git diff detection)
#   -b, --base <ref>    Base git ref to compare against (default: origin/master)
#   -f, --fix           Apply formatting instead of just checking
#   -t, --skip-tests    Skip tests

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{
  "name": "F# (.NET)",
  "image": "mcr.microsoft.com/devcontainers/dotnet:9.0-bookworm",
  "customizations": {
    "vscode": {
      "extensions": [
        "Ionide.Ionide-fsharp",
        "ms-dotnettools.csharp",
        "ms-dotnettools.vscode-dotnet-runtime"
      ],

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#!/bin/bash

# Find all processes matching 'socket_vmnet' and kill them
ps auxwww | grep -i socket_vmnet | grep -v grep | awk '{print $2}' | xargs sudo kill