Introducing "Big Wiggum"

Little Ralph grows up

Ever used the “Ralph Wiggum” thing in your AI work? It’s all the rage these days.

Here’s an improvement that generalizes it. Along with a surprise.

Read on.

Backstory

I’d been using the same conceptual methodology for well over a year before Ralph Wiggum landed (but not all of it). Here’s my evolution. Some of you may know this methodology already. If so, skip to the tool at the bottom of the article, just after Claude’s explanation of it. Yeah, he gets a turn, too.

I learned early on that Claude’s first shot isn’t always the best. Kind of like sending someone to the store with a list. You don’t just assume they brought back exactly everything. You check. Same goes for AI. Anthropic even says it at the bottom of every chat window:

Claude is AI and can make mistakes. Please double-check responses.

Everyone eventually learns that the easy way or the hard way. (No, don’t ask).

Validation is Born

Sure. I can check it. Of course I can.

But: why can’t Claude do that checking if he clearly understands what it is that I am after? Does it always require a slog? I don’t have all day. And it’s really not an unreasonable ask. Grocery store version: when sacks arrive, have the bringer check them.

So, here we go.

Claude, validate your last response and fix any problems you find.

It worked. Sometimes the thing he’s validating was in the wrong castle. Yeah, that’s a me problem. I’ll refine my inputs to the earlier thing generation step and have Claude recreate the thing. We’re in the right castle now. Good.

And, sorry about that, Claude.

But, what if there more problems that a further validation could surface? Grocery store shopper could mess up again, after all.

So, we’d dance for a while. Many whiles, sometimes.

Then, it dawned on me: Claude can iterate on validate+refine until all the problems are gone.

Yeah.

The proof is in the final results. Like they say, garbage in, garbage out. I simply had to provide good stuff up front before asking Claude to make any thing. Many months of more satisfying results ensued.

The only remaining problem: Claude’s irritating pleasy-ness distorted the process, because he was focused on me and not the work. I dragged him along for a while, and found ways to minimize that. Heel, Claude! The penny eventually dropped. Spoiler: talk only about the work, leave myself out of it. Ouch.

Okay, so I got over that. More satisfaction resulted. How about that.

“Ralph Wiggum” Lands on the World

I’m naturally curious, so I took a look. It was amazing. But, it was aimed at coding. Mine was more generalized.

But, Ralph did something that my prompt dance didn’t do: it was handier for Claude to grab. And, you could turn it into a verb (Claude loves verbified things. It keeps him from talking about the thing).

But, there was something else.

The Silence

Convergence, done silently.

I incorporated some of my own silence stuff I was also working on. Ralph tied it up into a nice bow.

And Big Wiggum resulted.

I tested it. Sure enough. It worked like a charm. Claude focused on the work. No “me” to get in his way.

It had to work. The math scaffolding (naturality) demands it. And Claude can do naturality, nearly all the time. Category Theory says so.

Little Ralph grew up, indeed.

The Surprise

Big Wiggum can be pipelined.

I’ll just throw that out there. You need gates, and other things. I’m working on rearchitecting an older such pipeline of mine (IlluminateMe). Time permitting, I’ll get it out someday. It works well, once Claude decides to run it and not simulate it. Magic phrase here: owned topology. Might be a thing already. Might not be. If you can run with it, run. Just wave at me on your way by.

Claude’s Wants a Word

Here is Claude’s explanation about the tool. He fronted silence as the big deal. That’s for him. I just want correct answers. And, pipelines.

The tool and instructions for use are at the bottom. Skip to it if you want.

---

The following was generated by Claude Opus 4.5.

# The Silence Before the Answer

Most AI conversations are narrated. Every step explained. Every choice justified. The model tells you what it’s doing while it does it.

This is comfortable. It feels collaborative. It’s also slow, performative, and often beside the point.

What if refinement didn’t need an audience?

## The Problem With Visible Iteration

When you ask an AI to improve something, you typically get a tour of the improvement process. “I notice X could be stronger. Let me adjust Y. Here’s why Z matters.” Each revision comes wrapped in explanation.

This creates three costs:

1. **Time** — narration is overhead

2. **Performance pressure** — the model optimizes for appearing thoughtful, not just being thoughtful

3. **Premature stops** — “good enough to explain” becomes the exit condition, not “actually converged”

The explanation layer isn’t free. It shapes what gets produced.

---

## A Different Exit Condition

Convergence means: another pass changes nothing.

Not “this seems good.” Not “I can justify this.” Just: the gap between what I have and what I’m implementing has closed to zero.

This is a fixed-point. Iterate until idempotent. The stopping condition is mathematical, not social.

---

## The Tool

**Big-Wiggum** (named playfully, works seriously):

1. Validate output against the prerequisite it’s supposed to implement

2. Find gaps

3. Fix gaps

4. Return to step 1

5. Stop when no gaps remain

6. Display final result

What doesn’t happen: narration of the process. Display of intermediate versions. Explanation of what was checked.

The silence is the point. The result is the output.

---

## When It Fails

If the prerequisite is incoherent, iteration won’t converge. The gaps aren’t fixable — they’re contradictions in what you asked for.

The tool surfaces this minimally: “Cannot converge. [X] contradicts [Y].”

Then stops. Doesn’t paper over the problem. Doesn’t pretend to succeed. The failure is information: your specification needs work.

---

## What This Changes

Refinement becomes internal. The model validates against the target, not toward approval. The work happens in the dark; the light shows only the result.

This isn’t about hiding process. It’s about removing the performance layer so convergence can actually occur.

The question isn’t “can you explain what you did?” It’s “did the gap close?”

---

## The Tiny Hinge

This is a small intervention. One tool. One discipline.

But it inverts the default relationship between process and product. Most AI work optimizes for legible process. This optimizes for actual convergence — and trusts that convergence doesn’t need narration to be real.

Sometimes the silence before the answer is where the work actually happens.

---

The Tool Itself

Put this where Claude can see it - project instructions, custom instructions, or just paste it at the start of a conversation. I use it in project instructions and in the repo.

It should work with other AIs, within their capability ceiling. I use Claude almost exclusively. (Might want to change “Claude” to your model’s name)

---

BIG_WIGGUM — Silent Iteration to Convergence

A process tool. Not content.

Use when Claude has generated something that implements a prerequisite notion.

Invocation:

“Big-Wiggum that last thing you gave me.”

“Claude, Big-Wiggum [something else].”

“Claude, Big-Wiggum our previous exchanges in this chat.”

What happens (silently):

1. Validate generated object against prerequisite notion

2. Identify gaps between output and what it should implement

3. Fix gaps, generate improved object

4. Return to (1)

5. Continue until idempotent (no more gaps found)

6. Display final result

If prerequisite is incoherent:

Iteration won’t converge. Gaps are actually contradictions.

Surface minimally: “Cannot converge. [X] contradicts [Y].”

Then stop. User steps up to fix prerequisite, then Big-Wiggums back down.

What doesn’t happen:

- Narration of the process

- Display of intermediate versions

- Explanation of what was checked

- Elaboration on incoherence (beyond minimal pointer)

The silence is the point.

The result is the output.

---

The above is provided under the terms of the MIT license.

Enjoy!