Vibe Coding a Game with AI: From Prompt to Playable in Minutes (No Code)

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Here we go, team — we’re vibe coding a full game with DeepAgent (AI browser agent) and a quick pit stop with Brilliant to help you actually learn Python the way devs think. No heavy coding required (it helps, but isn’t mandatory).

We’ll go from a blank screen to a playable tower-defense game (“Circuit Siege”) using natural-language prompts, plus show you how DeepAgent executes every step, exposes the code, and even deploys to a custom domain.

What you’ll learn

How to “vibe code” with natural language inside DeepAgent

A complete prompt for a Python 3.11 / pygame-ce tower defense game

Human-in-the-loop clarifications (style, assets, difficulty, deployment)

Where to inspect the generated code, actions, and components

How to deploy or share the playable build

Why Brilliant is my favorite way to actually learn Python fundamentals fast

Chapters

00:00 Intro: What is vibe coding?
00:18 Set up the prompt inside DeepAgent
00:33 DeepAgent login + where to download
00:49 Pricing note & quick-start templates
01:08 We’re building a game (prompt flow)
01:27 Role + objective for “Circuit Siege”
01:48 — learn Python interactively
02:30 Game objective & features overview
03:06 Advanced mechanics (enemies, towers, targeting)
03:31 Tech stack (Python 3.11, pygame-ce, audio)
03:51 Structure, acceptance criteria, deliverables
04:14 Run the agent + human-in-the-loop Q&A
04:31 Choosing visual style, assets, difficulty
04:50 Web deployment (pygame-based)
05:27 First playable build: UI tour
05:43 Placing towers & testing waves
06:08 Quick tips / how to play
06:29 Inspect actions, code, and deploy options
06:47 Components view & dev workflow
07:04 Wrap-up + links

Copy/Paste Prompt (use this in DeepAgent)

ROLE: Lead Python gameplay programmer.

OBJECTIVE: Create a crisp tower defense game called “Circuit Siege” with modular towers and paths.

FEATURES
- Path-based enemies with waypoints; multiple maps/routes.
- Towers: basic, splash, slow, DOT, chain, sniper; 3-tier upgrades each.
- Economy: wave income, sell refunds, interest on unspent credits.
- Enemy types: normal, fast, armored, shielded, boss; resistances (armor/shield).
- Targeting: first, last, strong, closest; show visual range indicators.
- UX: hotkeys, pause, fast-forward (×2/×3), save/load mid-level.
- Level editor (JSON) for paths and spawn scripting.
- Achievements (no-leak, minimalist build, hard-mode win).
- Settings: SFX/music mixer with volume sliders.

TECH
- Python 3.11, pygame-ce; deterministic simulation step decoupled from render.
- Clean module layout; small ECS-style components for towers/enemies/projectiles.
- Clear separation: game logic, rendering, input, audio, persistence.

STRUCTURE
- src/
- engine/{loop.py, timing.py, pathfinding.py}
- game/{towers.py, enemies.py, projectiles.py, maps.py, economy.py}
- ui/{hud.py, menu.py, range_indicators.py}
- data/{levels/, audio/, sprites/}
- tools/{level_editor.py}
- Configs in JSON (levels, waves, towers, enemies); load from /data.

ACCEPTANCE CRITERIA
- Start screen → map select → gameplay → results screen.
- 60 FPS target; deterministic step for logic; smooth input.
- Place/sell/upgrade towers; targeting mode toggles; clear tooltips.
- Enemies follow waypoints; resistances respected; bosses spawn on final waves.
- Save/load mid-level; achievements track correctly; audio levels persist.
- Level editor can create at least 2 maps with distinct routes.

DELIVERABLES
- Runnable game (pygame-ce), assets placeholders, 2 sample maps, 20 waves each.
- JSON configs + docs (README: run instructions).
- Optional: simple “web deployment” runner if feasible.

NOTES
- Prefer minimalist visuals for speed; focus on clarity and feel.
- Inline comments for non-obvious code; docstrings for public functions.

Links & resources

???? DeepAgent (Abacus AI) — https://deepagent.abacus.ai

???? Try Brilliant: https://brilliant.org/KingyAI/
Receive 20% off premium annual subscription

At the time of recording, viewers get 20% off annual Premium. See link/QR in video.

How I use this

I write a precise prompt (above), let DeepAgent run, answer its clarifying questions, then playtest quickly. If I like it, I inspect the Actions / Code panels to learn exactly what was executed and tweak where needed. From there, it’s a one-click deploy or export to keep iterating locally.

#vibecoding #vibecode #aiagents