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🎮 This entire Avo Lawn game was built with Muse Spark 1.2 + Muse Code. 🥑 Now it’s your turn—use Muse Spark 1.2 to build your own game and see what you can create. To play: Check out our blog post:

42,871 次观看 • 1 个月前 •via X (Twitter)

9 条评论

Claire Zhou 的头像
Claire Zhou1 个月前

so much fun!

Pete 的头像
Pete1 个月前

What looks to be a clone of a very popular game? 😬😬😬 maybe run past legal

404 Opinions 的头像
404 Opinions1 个月前

built a game with muse spark 1.2 what they meant: we finally found a way to make you pay for code that writes itself

Fahad 的头像
Fahad1 个月前

AVO LAWN DEFENDED! You survived the Guac-pocalypse!

阿北的 Agent 周末 的头像
阿北的 Agent 周末1 个月前

用AI做游戏现在这么顺了?我得去试试这个Muse Spark,看能不能搞个小demo出来,哈哈。

Gargeya 的头像
Gargeya1 个月前

I am facing a muse code installation problem on my mac. Says file not found?

RIDER SKETCH 的头像
RIDER SKETCH1 个月前

Epic game!!!!

Ömer Hamid Kamışlı 🇹🇷 的头像
Ömer Hamid Kamışlı 🇹🇷1 个月前

I love 🥑

AI Mastery Guide 的头像
AI Mastery Guide1 个月前

A full tower defense game built entirely by an agent is wild to see.

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gemini 3.7 flash vs deepseek v4 pro 0813 vs muse spark 1.2 – on voxel city dioramas three models each built three crossy road-style 3d scenes – a construction site, a nyc intersection, a river with a drawbridge – as single self-contained html files the setup: Nous Research's hermes agent cli on OpenRouter, three.js skills preloaded, identical prompts per scene tasks: 1. construction site – tower crane on a working lift loop, paver laying fresh road, roller compacting it behind 2. nyc crossing – four-way intersection with a traffic light state machine, queuing cars, pedestrians crossing on the walk signal 3. river drawbridge – double-leaf bascule that lifts for tall boats, cars queuing at the barriers, animated water every scene: Three.js r185, box geometry only, a locked 20-color palette, four camera presets, and a day/night mode with bloom. one file, no build step, no assets models: Google DeepMind gemini 3.7 flash, DeepSeek v4 pro 0813, AI at Meta muse spark 1.2 muse and gemini finished every scene in two to three minutes. deepseek took 15 to 41 minutes per scene - build time, all three scenes #1 gemini 3.7 flash – 6m 43s #2 muse spark 1.2 – 7m 20s #3 deepseek v4 pro – 91m 25s - total tokens #1 muse spark 1.2 – 440,279 #2 gemini 3.7 flash – 713,855 #3 deepseek v4 pro – 20,957,568 - total price #1 muse spark 1.2 – $0.53 #2 gemini 3.7 flash – $0.56 #3 deepseek v4 pro – $4.57 - agent calls across the three builds #1 muse spark 1.2 – 12 #2 gemini 3.7 flash – 18 #3 deepseek v4 pro – 143 observations: • muse won two of the three scenes on looks with the smallest files in the test – 887 to 1,042 lines against gemini's 1,934 to 2,377. cheapest, fastest to a good frame, and shortest turned out to be the same column • deepseek burned 20.96m tokens – 29x gemini, 48x muse – across 143 agent calls. prompt caching is the only reason that cost $4.57: the cache discount absorbed roughly $30 of resent context • gemini was the only model whose files needed zero fixes to render – and the only one whose night mode is cosmetic. the sky never darkens and one camera button does nothing. clean code for a scene it never looked at follow thehype. for 24/7 ai news, analysis and breakdowns

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29,033 次观看 • 1 个月前