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Thursday

Wednesday

  1. Ant Ling: Developer BlogSelected

    Ant Ling Open-Sources Native Multimodal Model Ling-3.0-flash-VL

    Ant Ling has released and open-sourced Ling-3.0-flash-VL, the first native multimodal large model in the Ling series. Built as an extension of the Ling-3.0-flash MoE architecture, it has 124B total parameters and 5.5B activated per inference, natively supports image, text, and video inputs, and has a 256K Token context window. The company says native multimodal joint training improved the model's score on the Artificial Analysis Intelligence Index v4.1.1 by 4 points over the text-only version, and it introduces a visual feedback loop of observe, act, verify, and correct for front-end replication, GUI Agent, and medical report interpretation. The model is now available for free trial on Ling Studio, and the BF16 and FP8 versions have been open-sourced on Hugging Face and ModelScope.

    English text is being prepared.

    Ant Ling has released and open-sourced Ling-3.0-flash-VL, the first native multimodal large model in the Ling series. Built as an extension of the Ling-3.0-flash MoE architecture, it has 124B total parameters and 5.5B activated per inference, natively supports image, text, and video inputs, and has a 256K Token context window. The company says native multimodal joint training improved the model's score on the Artificial Analysis Intelligence Index v4.1.1 by 4 points over the text-only version, a

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    #engineering#Model releases#Multimodal#Open-source ecosystem

Thursday

Wednesday

  1. Google DeepMind

    Google DeepMind releases Gemini 3.8 Flash and 3.8 Flash Cyber

    Google DeepMind has released Gemini 3.8 Flash and Gemini 3.8 Flash Cyber, aimed at trusted defenders, calling them its most capable reasoning and coding models to date, with speed and cost comparable to 3.7 Flash. 3.8 Flash surpasses most larger frontier models on DeepSWE v1.1, scores 54.9% on HLE-Verified, and performs more reasoning steps and iteratively calls tools on complex tasks. 3.8 Flash Cyber reaches frontier-level performance on CyberGym, with an internal vulnerability-discovery benchmark success rate exceeding 70% across 20 programming languages, and a CWE-Bench patch pass@1 of 47.2%.

    Google DeepMind has released Gemini 3.8 Flash and Gemini 3.8 Flash Cyber, aimed at trusted defenders, calling them its most capable reasoning and coding models to date, with speed and cost comparable to 3.7 Flash. 3.8 Flash surpasses most larger frontier models on DeepSWE v1.1, scores 54.9% on HLE-Verified, and performs more reasoning steps and iteratively calls tools on complex tasks. 3.8 Flash Cyber reaches frontier-level performance on CyberGym, with an internal vulnerability-discovery benchm

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    #lab#Model releases#Agents#English text is being prepared.

Tuesday

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Tuesday

  1. Ant Ling: Developer BlogSelected

    Ant Ling Open-Sources Ling-3.0-tiny: 7.9B Total Parameters, 1.3B Activated Parameters

    Ant Ling has officially open-sourced Ling-3.0-tiny, a native hybrid reasoning model with 7.9B total parameters that activates only 1.3B parameters during inference, available in BF16, FP8, and INT4 versions. The company says it scores 25 on the Artificial Analysis Intelligence Index and 16 on the Agentic Index, with output speed exceeding 160 tokens/s. The model can run FP8 inference on a single NVIDIA DGX Spark with weights of about 7.85GB, and has been adapted for local execution on Apple Silicon MacBook and Mac mini.

    English text is being prepared.

    Ant Ling has officially open-sourced Ling-3.0-tiny, a native hybrid reasoning model with 7.9B total parameters that activates only 1.3B parameters during inference, available in BF16, FP8, and INT4 versions. The company says it scores 25 on the Artificial Analysis Intelligence Index and 16 on the Agentic Index, with output speed exceeding 160 tokens/s. The model can run FP8 inference on a single NVIDIA DGX Spark with weights of about 7.85GB, and has been adapted for local execution on Apple Sili

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    #engineering#Model releases#Open-source ecosystem#Reasoning

Monday

  2. Fireworks AISelected

    Meta's Muse Glimmer 30B Launches on Fireworks, Targeting Persistent Agents

    Fireworks has launched Meta's Muse Glimmer 30B dense model under the Apache 2.0 license, supporting 128K+ token context and native image understanding, targeting persistent agents that require multi-turn tool calling. The model scores 75.5 on MCP Atlas, 74.6 on DeepSearch QA, and 51.2 on SWE-Bench Pro, all higher than the corresponding results of Gemma 4 31B and Qwen 3.6 27B; these benchmark figures are reported by Meta. Fireworks also provides recommended settings of temperature 1.0, top_p 0.95, and top_k 64, and supports adjusting reasoning intensity in the system prompt.

    Fireworks has launched Meta's Muse Glimmer 30B dense model under the Apache 2.0 license, supporting 128K+ token context and native image understanding, targeting persistent agents that require multi-turn tool calling. The model scores 75.5 on MCP Atlas, 74.6 on DeepSearch QA, and 51.2 on SWE-Bench Pro, all higher than the corresponding results of Gemma 4 31B and Qwen 3.6 27B; these benchmark figures are reported by Meta. Fireworks also provides recommended settings of temperature 1.0, top p 0.95

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    #editorial#Product updates#English text is being prepared.#Agents

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