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Model: "qwen3-next-80b"
not much happened today
gpt-5-codex vllm-0.10.2 qwen3-next-80b hunyuanimage-2.1 openai microsoft perplexity-ai huggingface amd tencent lmstudio agentic-ai ide context-windows inference distributed-inference reinforcement-learning robotics long-context model-optimization text-to-image multimodality model-licenses gdb teknium1 finbarrtimbers thsottiaux theturingpost pierceboggan amandaksilver aravsrinivas sergiopaniego art_zucker danielhanchen rwojo awnihannun
GPT-5 Codex rollout shows strong agentic coding capabilities with some token bloat issues. IDEs like VS Code Insiders and Cursor 1.6 enhance context windows and model integration. vLLM 0.10.2 supports aarch64 and NVIDIA GB200 with performance improvements. AMD ROCm updates add modern attention, sparse MoE, and distributed inference. TRL introduces Context Parallelism for long-context training. Robotics and RL data pipelines improve with Unsloth and LeRobotDataset v3. Qwen3-Next-80B runs efficiently on Mac M4 Max with MLX. Tencent's HunyuanImage 2.1 is a 17B bilingual text-to-image model with 2048×2048 resolution and restricted open weights.
GPT-5 Codex launch and OpenAI's quiet rise in Agentic Coding
gpt-5-codex qwen3-next-80b openai alibaba together-ai nvidia agentic-ai software-engineering long-context mixture-of-experts model-optimization cuda-acceleration inference-efficiency routing task-adaptive-thinking sama swyx omarsar0 ofirpress
OpenAI released GPT-5-Codex, an agentic coding model optimized for long-running software engineering tasks with dynamic task-adaptive thinking, multi-hour autonomy, and improved code quality. It achieves 51% accuracy on an unreleased large refactor benchmark and integrates deeply with developer tools like Xcode. Meanwhile, Alibaba launched Qwen3-Next-80B, a hybrid MoE model with native long-context support (262k tokens, extensible to 1M+), targeting efficient reasoning and repository-scale code analysis, supported by Together AI and NVIDIA with CUDA-accelerated attention. The trend towards hybrid SSM + MoE architectures is noted, emphasizing efficiency and scaling in China and US training regimes. Community discussions highlight the importance of variable compute and routing for inference efficiency and quality.