My 2.5 year old laptop can write Space Invaders in JavaScript now (GLM-4.5 Air) | AI Paper Digest

TL;DR Highlight

GLM-4.5 Air running on a local laptop with no internet connection can generate playable game code.

Who Should Read

Developers evaluating local LLM adoption, or engineers looking to deploy code generation AI internally without API costs.

Core Mechanics

GLM-4.5 Air is a lightweight model from Zhipu AI that can run local inference on a regular consumer laptop (2.5 years old)
Generates complete, playable JavaScript game code (Space Invaders level) in a single pass on CPU/integrated graphics — no GPU needed
The 'Air' series is designed as an offline code assistant that works without cloud API dependency

Evidence

Runs real-time inference on a 2.5-year-old regular laptop (no GPU) — specific tokens/sec not provided in source
Generated Space Invaders code runs immediately playable in a browser as demonstrated
No benchmark scores provided in the original source for quantitative comparison

How to Apply

Try it immediately with `ollama pull glm4.5-air` and send code generation prompts — no API key needed.
For organizations with security policies restricting external API use, deploy GLM-4.5 Air as a local code assistant to eliminate cloud dependency.
Use as a prototyping tool — generate initial drafts locally with GLM-4.5 Air instead of paying GPT-4 API costs, then refine if needed.

Code Example

snippet

# Example of running GLM-4.5 Air with ollama and generating Space Invaders
# 1. Installation
# ollama pull glm4.5-air

# 2. Code generation prompt
prompt = """
Write a complete, playable Space Invaders game in a single HTML file using vanilla JavaScript.
Requirements:
- Player ship moves left/right with arrow keys, shoots with spacebar
- 3 rows of alien enemies that move side-to-side and descend
- Collision detection for bullets vs aliens and aliens vs player
- Score counter and game over screen
Output only the HTML file, no explanation.
"""

import ollama
response = ollama.chat(
    model='glm4.5-air',
    messages=[{'role': 'user', 'content': prompt}]
)
print(response['message']['content'])

Terminology

local LLMRunning an AI model directly on your own computer instead of sending requests to a server like ChatGPT. Works offline and incurs no API costs.

GLM-4.5 AirA lightweight language model from China's Zhipu AI. 'Air' indicates a compressed version designed to run on low-spec devices.

inferenceThe process of a trained AI model generating outputs. Distinct from training — inference is using the model, not teaching it.

Related Papers

Related Resources

https://github.com/THUDM/GLM-4