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Company: "yupp_ai"
not much happened today
qwen-image-2512 ax-k1 k-exaone sk-telecom lg upstage naver alibaba unsloth replicate mixture-of-experts model-release quantization open-source-models image-generation model-integration model-benchmarking compute-costs dataset-curation eliebakouch clementdelangue dorialexander rising_sayak _akhaliq ostrisai ivanfioravanti yupp_ai
South Korea's Ministry of Science launched a coordinated program with 5 companies to develop sovereign foundation models from scratch, featuring large-scale MoE architectures like SK Telecom A.X-K1 (519B total / 33B active) and LG K-EXAONE (236B MoE / 23B active), with a total first-round budget of ~$140M. This initiative contrasts with EU approaches by focusing funding on fewer stakeholders and explicitly budgeting for data. Meanwhile, Alibaba's Qwen-Image-2512 emerges as a leading open-source image generation model, rapidly integrated into various toolchains including AI-Toolkit and local inference paths with quantization support, and hosted on platforms like Replicate. The model has undergone extensive blind testing with over 10,000 rounds on AI Arena, highlighting its ecosystem adoption.
Kimi K2 Thinking: 1T-A32B params, SOTA HLE, BrowseComp, TauBench && Soumith leaves Pytorch
kimi-k2-thinking gemini moonshot-ai google apple vllm_project arena baseten yupp_ai mixture-of-experts quantization int4 context-window agentic-ai benchmarking model-deployment inference-acceleration api performance-optimization eliebakouch nrehiew_ andrew_n_carr ofirpress artificialanlys sundarpichai akhaliq
Moonshot AI launched Kimi K2 Thinking, a 1 trillion parameter mixture-of-experts (MoE) model with 32 billion active experts, a 256K context window, and native INT4 quantization-aware training. It achieves state-of-the-art results on benchmarks like HLE (44.9%), BrowseComp (60.2%), and agentic tool use with 200-300 sequential tool calls. The model is deployed with vLLM support and OpenAI-compatible APIs, available on platforms like Arena, Baseten, and Yupp. Early user reports note some API instability under launch load. Meanwhile, Google announced the TPU v7 (Ironwood) with a 10× peak performance improvement over TPU v5p, aimed at training and agentic inference for models like Gemini. Apple added support for M5 Neural Accelerators in llama.cpp for inference acceleration.
nano-banana is Gemini‑2.5‑Flash‑Image, beating Flux Kontext by 170 Elo with SOTA Consistency, Editing, and Multi-Image Fusion
gemini-2.5-flash-image-preview hermes-4 nemotron-nano-9b-v2 internvl3.5 gpt-oss qwen3 deepseek-v3.1 google-deepmind nous-research nvidia openai ollama huggingface openrouter image-editing natural-language-processing multi-image-composition character-consistency reasoning hybrid-models context-windows model-steerability pretraining finetuning alignment vision vision-language api model-integration sundarpichai _philschmid lmarena_ai omarsar0 skirano yupp_ai xanderatallah officiallogank mervenoyann
Google DeepMind revealed Gemini-2.5-Flash-Image-Preview, a state-of-the-art image editing model excelling in character consistency, natural-language edits, and multi-image composition, dominating the Image Edit Arena with a ~170-180 Elo lead and over 2.5M votes. It is integrated into multiple platforms including Google AI Studio and third-party services. Nous Research released Hermes 4, an open-weight hybrid reasoning model focused on steerability and STEM benchmarks. NVIDIA launched Nemotron Nano 9B V2, a hybrid Mamba-Transformer with 128k context, top-performing under 10B parameters, and released a 6.6T-token pretraining subset. InternVL3.5 introduced 32 vision-language models based on OpenAI's gpt-oss and Qwen3 backbones. Ollama v0.11.7 added DeepSeek v3.1 support with hybrid thinking and Turbo mode preview.