One snail. One blue button. An entirely unnecessary engineering department.
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady.
What I Built
Meet Mr. Pending, Chief Engineer of The Department of Unnecessary Engineering.
His mission? Press one blue button.
Most people would simply press it. Mr. Pending is a snail with professional standards. Naturally, he requires blueprints, machinery, testing, repairs and an unreasonable amount of paperwork.
Behind the joke is a real experiment: a small open-weight AI model designs ridiculous machines, and a simulator tests whether they actually work.
If a machine fails, the AI receives feedback and tries to repair it.
You press one button. The Department handles the rest.
Why a snail instead of a sloth?
The Slow and Steady theme immediately made me think of a sloth. But a snail felt even funnier: slow, determined, and completely serious about doing a very simple job in the most complicated way imaginable.
The sloth didn't appreciate being passed over.
So it occasionally interrupts the Department's proceedings.
Fortunately, Mr. Pending has a response:
"This is why we employ snails."
Don't worry. The sloth is only a visual joke. It doesn't interfere with the actual simulation.
Demo
🐌 Visit The Department of Unnecessary Engineering
Press the blue button and watch the machinery come alive!
The public site is a replay, not a hosted AI service: it plays builds recorded on my laptop and re-checks every result in the browser. Gemma runs locally through Ollama, designing new machines and proposing repairs after the simulator tests them. The site lets anyone explore the genuine results.
Code
Moonchichiii/mrpending on GitHub
Open source, with the simulator, evaluation results and local setup instructions included.
How I Built It
The local installation runs on Gemma 4 E2B through Ollama, behind a small Mastra-based agent that owns one loop: design, simulate, repair.
The stack includes:
- React 19, TypeScript and Vite+ for the interactive experience.
- SVG and CSS animations for Mr. Pending and the machinery.
- Zod for structured AI output validation.
- A deterministic TypeScript simulator to test every machine.
- Render for the public static replay.
- ElevenLabs for 20 prerecorded lines in Mr. Pending's Alistair voice.
The process is simple:
Gemma designs → Simulator tests → Gemma repairs.
Gemma places mechanical parts — ramps, seesaws, conveyors, fans, dominoes and catapults — on a grid. The simulator then determines what actually happens.
Gemma cannot simply declare success.
What actually happened?
I tested three model-and-prompt configurations, with 15 headline builds each.
| Configuration | Successful button presses |
|---|---|
| Gemma 4 E2B, prompt v1 | 0/15 |
| Gemma 4 E2B, prompt v2 | 2/15 |
| Gemma 4 E4B, prompt v2 | 4/15 |
Every successful press came after an AI-proposed repair.
E4B achieved more successes in this small test, but used significantly more memory. I chose E2B with prompt v2 for the release because it was more practical on the test laptop.
Most machines failed. That wasn't a bug in the experiment. It was the experiment!
And whenever a machine succeeded, the simulator—not the AI's confidence—verified it.
One thing I learned the hard way
I spent too much time perfecting infrastructure, tests and agent workflows before properly testing the actual experience.
The code worked. The first interface didn't.
Instead of a funny snail and a blue button, I'd built something closer to an engineering administration portal.
The solution was to return to the original idea: one character, one button and one animated blueprint.
A useful reminder that passing tests and making something enjoyable are two different achievements.
Why Does Open Innovation Matter?
I wanted to explore what a small AI model could accomplish on an ordinary computer.
Running Gemma locally allowed me to experiment with prompts, compare model sizes, inspect failures and reproduce results without relying on a paid cloud inference API.
More importantly, the open approach allowed me to keep the model's role narrow and measurable.
Gemma proposes the engineering. The simulator independently checks it.
The project demonstrates that small models don't need unlimited autonomy to do something interesting.
Sometimes a clearly defined task, useful feedback and another attempt are enough.
My Agent Session
I used Claude Code for implementation and Codex for independent reviews, alongside GitHub issues, CI checks and hands-on browser testing.
The biggest lesson? AI coding agents can produce plenty of technically correct work while still missing the intended user experience.
Human creative direction and actual product testing remain essential.
Even small things can do great things. 🐌💙
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