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Topic: "user-preference"
AI Engineer Code Summit
gemini-3-pro-image gemini-3 gpt-5 claude-3.7-sonnet google-deepmind togethercompute image-generation fine-tuning benchmarking agentic-ai physics model-performance instruction-following model-comparison time-horizon user-preference demishassabis omarsar0 lintool hrishioa teknium artificialanlys minyangtian1 ofirpress metr_evals scaling01
The recent AIE Code Summit showcased key developments including Google DeepMind's Gemini 3 Pro Image model, Nano Banana Pro, which features enhanced text rendering, 4K visuals, and fine-grained editing capabilities. Community feedback highlights its strong performance in design and visualization tasks, with high user preference scores. Benchmarking updates reveal the new CritPt physics frontier benchmark where Gemini 3 Pro outperforms GPT-5, though AI still lags on complex unseen research problems. Agentic task evaluations show varied time horizons and performance gaps between open-weight and closed frontier models, emphasizing ongoing challenges in AI research and deployment. "Instruction following remains jagged for some users," and model fit varies by use case, with Gemini 3 excelling in UI and code tasks but showing regressions in transcription and writing fidelity.