Release3d ago
Apple Intelligence is coming to China: iOS 27.3 developer beta starts Nov 4, public release in December, with Alibaba Qwen inside Siri, Writing Tools and Visual Intelligence
In a press release on its China newsroom, Apple said the first Apple Intelligence features will open for testing in the latest iOS developer beta starting November 4 and reach users in China with the iOS 27.3 release in December. The features run on Apple-built generative models and include Live Translation, Writing Tools, Visual Intelligence, Clean Up, Genmoji, notification and Mail summaries, and Type to Siri; Apple also partnered with Alibaba to bring Qwen into Siri, Writing Tools and Visual Intelligence. The first wave covers iPhone 15 Pro and newer only; Siri AI and the other next-generation features announced at WWDC 2026, plus Image Playground, are not included, and Mac, iPad and other platforms wait on further approvals.
Open Source13d ago
Ornith-1.5-122B Released Under MIT: A Single-Node 122B MoE Reasoning Model Scoring 76.9 on SWE-bench Pro
The Ornith AI team officially released Ornith-1.5-122B under the permissive MIT license, a 122-billion parameter mixture-of-experts (MoE) reasoning model optimized to execute on a single GPU node (such as a single DGX). The model is immediately available across vLLM (0.19.1+), SGLang (0.5.9+), Hugging Face, and Ollama (ornith-1.5:122b) with native FP8, GGUF, and MLX quantization. Across independent agentic evaluations in the OpenHands harness, Ornith-1.5-122B registered 84.5 on SWE-bench Verified, 76.9 on SWE-bench Pro (surpassing Qwen3.8-Flash-Next's 43.9 by 33 points), 81.2 on TerminalBench 2.1, and 74.6 on MCP-Atlas.
Release68d ago
Alibaba Qwen open-weights Qwen3.8-Flash: 125B/6B multimodal MoE scoring 62.5 on SWE-bench Pro
Alibaba Qwen released open-weight Qwen3.8-Flash, a multimodal Mixture-of-Experts model billed as an early preview of the Qwen4 architecture. The production-scale checkpoint is 125B parameters plus 51B N-gram embeddings, with only 6B activated per token. The new stack combines Gated DeltaNet plus Qwen Sparse Attention, Gated Residual branches, N-gram embeddings that can live in host memory, and a refined Muon optimizer. Qwen said it trained at about one-ninth the cost of Qwen3.7-Plus while beating it across the board, especially on coding and office work: 58.7 on DeepSWE 1.1, 62.5 on SWE-bench Pro, 73.9 on CoWorkBench, 84.5 on AndroidWorld, and 95.7 on MathVision with CI. Native context is 262K tokens, extendable to 1M with YaRN; at 1M context the QSA kernel is up to 7.6x faster in prefill and 4.9x faster in decode. QwenCloud lists roughly $0.15 input / $0.47 output per million tokens, and day-0 runtimes arrived from NVIDIA NeMo, SGLang, Unsloth, OpenRouter, and OpenCode Go.
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