Build Interactive 3D Worlds With GPT-6 & Blender
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A Huawei-Queen's study finds open coding models fine-tuned under OpenHands degrade sharply under other scaffolds - SWE-Lego-Qwen3-32B drops from 52.6% to 8.4% Pass@1 on OpenCode. The fix: train planning as a model capability, not a scaffold artifact.
FermiSense fine-tuned Qwen 3.5 9B with 2,500 GRPO steps on a single GPU for $500 and beat GPT-5.6 Sol (93%) and Opus 4.8 (91%) on automotive catalog review, reaching 97% accuracy at 68x lower cost per listing.
Gemma 4 ships byte-for-byte open weights from Google DeepMind. How developers deploy it locally, fine-tune it, and ship agents on top of it.
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