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Monday

  1. CTech

    Shashua's new company doubleAI releases Maxwell code execution model

    Amnon Shashua's new company doubleAI has released its first product, Maxwell, which it says belongs to a new category called Code Execution Models (CEM), optimizing software execution efficiency between code and chips without modifying source code. doubleAI says Maxwell continuously analyzes running programs, hardware, and data to optimize underlying machine instructions, while using formal mathematical verification to ensure each optimization does not change the program's original behavior, with support for CPUs and GPUs. The company says Maxwell speeds up data pipelines by more than 38x and AI inference kernels by more than 4.5x on real workloads; these figures have not been independently verified. Maxwell is now available for early access through the official website, and doubleAI plans to charge customers based on the compute costs saved.

    Amnon Shashua's new company doubleAI has released its first product, Maxwell, which it says belongs to a new category called Code Execution Models (CEM), optimizing software execution efficiency between code and chips without modifying source code. doubleAI says Maxwell continuously analyzes running programs, hardware, and data to optimize underlying machine instructions, while using formal mathematical verification to ensure each optimization does not change the program's original behavior, wit

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    #tech-media#Model releases#Reasoning#English text is being prepared.

Sunday

Saturday

  6. TechCrunch AI

    Elorian Releases Its First Model Glimpse, Using a Visual Draft Space for Image Reasoning

    Elorian has released its first model, Glimpse, opening the API as a research preview, with a waitlist for the chat interface. During reasoning, the model can crop image regions, extract video frames, or run code to measure and compare what it sees, which Elorian calls a visual draft space. Glimpse is priced at $0.60 per million input tokens and $3 per million output tokens, and according to Elorian's evaluations it surpasses GPT-6 Astra on scientific chart reasoning and short video description. The company says the model was trained on several hundred GPUs, a scale notably smaller than the frontier systems it benchmarks against, and it initially targets enterprises with large amounts of video, such as robotics, research laboratories, and media and creative firms.

    Elorian has released its first model, Glimpse, opening the API as a research preview, with a waitlist for the chat interface. During reasoning, the model can crop image regions, extract video frames, or run code to measure and compare what it sees, which Elorian calls a visual draft space. Glimpse is priced at $0.60 per million input tokens and $3 per million output tokens, and according to Elorian's evaluations it surpasses GPT-6 Astra on scientific chart reasoning and short video description.

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    #tech-media#Model releases#Multimodal#Reasoning

Friday

  3. Unite.AISelected

    Reflection open-sources 501B-parameter MoE model Beam under Apache 2.0

    On October 16, Reflection released its first open-weight model Beam on Hugging Face under the Apache 2.0 license, and Baseten provided day-0 support the same day through Model APIs and Dedicated Inference. Beam is a 501B total-parameter MoE autoregressive Transformer with 23B activated per token, a context window of up to 1 million tokens, pretraining on 23.8 trillion tokens, and weights released in BF16, FP8, and NVFP4 precisions. The evaluation results given by Reflection are GPQA Diamond 90.5%, SWE-bench Verified 80.9%, AIME 2026 97.8%, Terminal-Bench 2.1 80.1%, and MCP Atlas 78.7%, and it also open-sourced the terminal agent tool Mirror CLI.

    On October 16, Reflection released its first open-weight model Beam on Hugging Face under the Apache 2.0 license, and Baseten provided day-0 support the same day through Model APIs and Dedicated Inference. Beam is a 501B total-parameter MoE autoregressive Transformer with 23B activated per token, a context window of up to 1 million tokens, pretraining on 23.8 trillion tokens, and weights released in BF16, FP8, and NVFP4 precisions. The evaluation results given by Reflection are GPQA Diamond 90.5

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    #hot-signal#Model releases#Open-source ecosystem#Agents
  4. heise online

    Aleph Alpha Releases Kolibri-1: A 78B-Parameter Open-Weight Model for German-Language Reasoning

    Aleph Alpha has released the open-weight model Kolibri-1, with 78B total parameters. It uses a MoE architecture with 384 experts and 6 routed experts, activating only 3.46B parameters during inference, with a 256K Token context that can be extrapolated to about 1 million Tokens. The model focuses on German and English, and its tokenizer segments German words more token-efficiently; for example, the German Bundestag glossary requires only 1137 Tokens, whereas the compared DeepSeek-V4.1-Flash requires 1910 Tokens. Pretraining used 20 trillion Tokens, with data current as of June 18, 2026. The author's hands-on test found its answers correct and its German-language reasoning process evident, and stated that the Merlin-Arthur protocol makes the model hallucinate less in benchmarks, but its actual performance still needs to be verified in real-world use.

    Aleph Alpha has released the open-weight model Kolibri-1, with 78B total parameters. It uses a MoE architecture with 384 experts and 6 routed experts, activating only 3.46B parameters during inference, with a 256K Token context that can be extrapolated to about 1 million Tokens. The model focuses on German and English, and its tokenizer segments German words more token-efficiently; for example, the German Bundestag glossary requires only 1137 Tokens, whereas the compared DeepSeek-V4.1-Flash requ

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    #hot-signal#Model releases#Reasoning#English text is being prepared.

Thursday