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GPT-6.1-sol

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Headlines · 4

  1. Einsia launches Frontier3DBench for coding agents in 3D worlds

    Einsia released Frontier3DBench, a benchmark with 145 tasks across nine categories that tests 11 model-harness configurations producing editable Blender projects and runnable Unreal Engine worlds. Evaluation covers appearance, structure and behavior through rendered views, scene inspection and runtime tests. Einsia reports that GPT-6.1-sol led overall, while GPT-6-astra led interactive-world tasks and MiniMax-M3.1-Flash ranked second in scene editing.

    r/MachineLearning · 🔥 1
  2. GPT-6.1-sol builds an AI-guided 3D vending machine

    A Juejin author says GPT-6.1-sol helped build a Three.js vending machine that stores 60 simulated drinks, dispenses cans, supports reversible disassembly, and provides guided camera movements. The current version uses Ming-3.0-tts for locally generated Chinese narration, including selectable voices, while an approximately 4.58 MB single-file edition preserves purchasing, disassembly, camera, and audio functions offline. The published recording predates the switch to Ming, so it does not demonstrate the current narration version's complete natural playback.

    掘金 · 人工智能 · 🔥 0
  3. Simon Willison releases llm 0.37

    Simon Willison announced llm 0.37, a new version of his command-line tool and Python library for accessing large language models. The release adds gpt-6.1-sol and gpt-6.1-terra, introduces an `llm openai endpoint add` command for configuring additional models that use OpenAI-compatible endpoints, changes the default model to gpt-6-luna, and expands embedding support to handle binary content with different content types. Willison said the release represents around two months of accumulated features and fixes.

    Simon Willison's Weblog · 🔥 0
  4. WhisperNow releases locally fine-tuned transcription cleanup models

    Developer stephan273 released WhisperNow, an MIT-licensed voice-to-text app for Windows, macOS, and Linux, together with three locally fine-tuned models, a 66,557-row dataset, and training documentation. The LFM2.5 1.2B, LFM2.5 2.6B, and Qwen3.5 4B models were trained to remove fillers, false starts, and self-corrections and to format lists locally. The author reports that they outperformed several cloud models in factual-error tests, although those performance results have not been independently verified.

    r/LocalLLaMA · 🔥 0

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