u/BodegaOneAI

I've been stress-testing Qwen3.6:27b and wanted to share the result with the people who actually run this model.

BONESMITH is a skull-knight action-platformer roguelite. The whole thing is one index.html. No images, no audio files, no libraries, no bundler, no server. Every sprite is drawn procedurally on a canvas, every sound is synthesized through the WebAudio API, and it runs air-gapped with the network off. The only thing that built it was Qwen3.6:27b running locally.

The part I think this community will care about: how a 27B model actually behaves on a job this big.

First attempt with a loose prompt was a broken shell. Soft-locked on room 1, blurry text, no audio. The model tried to build every system at once and collapsed under it. That's the failure mode people expect from a 27B, and it's fair.

Then I rewrote the prompt with hard scope control: build a vertical slice first, pass a QA gate, then expand. Separate world canvas for the pixel art, a second UI canvas for crisp HUD text, explicit anti-soft-lock rules. Second attempt was a completely different result.

What Qwen3.6:27b did well, unprompted:

• Held the multi-file architecture in context and kept concerns separated
• Added hit-stop, screenshake with trauma decay, coyote time, and jump buffering with no request from me
• WebAudio synthesis was genuinely competent, punchy sfx straight from raw oscillators

Where it needed a concrete recipe:

• Procedural pixel art. The first characters were readable but crude until I handed it a literal process: silhouette, core shading, highlights, 1px dark outline.
• Keeping file size under control without being told to.

Curious if others here have pushed Qwen3.6 on multi-system codebases. Does the "architecture and logic clean, but needs concrete visual direction" pattern match what you're seeing, or is that just my prompting?

PS - the clip shows me playing badly. I built it, I never claimed I could beat it. 😂

BONESMITH is a skull-knight action-platformer roguelite I built using Qwen3.6:27b running locally. The self-imposed constraint: zero external files. Sprites drawn on canvas in code, audio synthesized via WebAudio, one index.html that runs air-gapped with no server.

The interesting part for local inference: the first prompt attempt produced a broken mess. Soft-lock on room 1, blurry text, no audio. The model tried to build everything at once. So I rewrote the prompt with explicit scope control, build the vertical slice first, pass a QA gate, then expand. That framing made a real difference.

What a 27B model locally is actually good for in this workflow: holding a complex multi-file architecture in context, implementing game feel details without hand-holding (it added hit-stop, screenshake trauma decay, coyote time), and iterating quickly without API costs or rate limits. You can be wrong, regenerate, and try a different framing immediately.

Where it needed explicit help: procedural pixel art direction, and keeping file sizes under control without being told.

Anyone else using local models for creative/game projects? Interested in how people are structuring prompts for multi-system codebases.

PS - As you can see I suck at this game. 😂

Had to share this with the people who'd appreciate it most. Gave Qwen3-Coder-30B-A3B one prompt and it built a complete 3D synthwave racer: three.js + bloom, a real multi-file Vite/TS project, 60fps with delta-time movement and object pooling, collision, high-score persistence. Running 100% locally on a single RTX 5090 via llama.cpp. No cloud, no API key.

Quick setup if anyone's curious: Q5_K_M GGUF, llama.cpp with --jinja (essential for the tool-calling to actually work).

Genuinely impressed. A 30B you can run on one consumer card doing this is a completely different world than a year ago. Big props to the Qwen team 🙏

(Disclosure: I make the IDE I ran it in, so I'm biased on the tooling, but this one's about what the model pulled off.)

Happy to share the exact prompt if anyone wants it.