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Monday4 items

Saturday3 items

  1. tencenthunyuanFirst-partyProduct release
    AI score62

    English translation is being prepared.

    Quoted postTencent Hy · @TencentHunyuan

    🚀 Hy4 preview is here. 770B, 49B active, 1M context. Built for productivity. Open source frontier. Consistent affordable price. Use it. Tell us what breaks. More on Hy blog：https://t.co/rbl1IWRk3C HuggingFace：https://t.co/mE9wevH5XR Github：https://t.co/pyl9zckpoL https://t.co/4iW6gSuZKr

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  2. roshan_sProduct release
    AI score67

    English translation is being prepared.

    Quoted postGrok Bot · @bot

    Grok Bot can now buy things on the internet. To get started, connect @link, send your Bot shopping, and it can complete purchases on your behalf. https://t.co/pA3pNsF1Qc

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

  3. unslothaiFirst-partyProduct release
    AI score71

    English translation is being prepared.

    Quoted postZ.ai · @Zai_org

    GLM-5.3 is now open-weight. Our most capable model for agentic coding and cyber defense is now available to download, run, and customize. Weights: https://t.co/v1IbWMXxg4 Tech blog: https://t.co/ekQkO83jCv https://t.co/f8XlJksKyf

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Friday4 items

Thursday6 items

  1. UnslothFirst-partyProduct release
    AI score66

    Unsloth releases GLM-5.3-Flash local-run GGUF quantized version

    Unsloth announces that GLM-5.3-Flash can run locally via its GGUF quantized version; the 3-bit quantization requires 128GB of memory, and a usage guide and GGUF download link are included. The cited Z.ai release information shows that GLM-5.3-Flash is a 320B-A18B model under the MIT license, natively multimodal and supporting a 1M token context window; it was previously previewed under the name Ox Alpha and runs entirely on Chinese AI chips. Unsloth says the model is comparable to Claude Opus 4.8 on DeepSWE, coding, and agent benchmarks.

    Quoted postZ.ai · @Zai_org

    Introducing GLM-5.3-Flash - Leading capabilities at a highly competitive price - Natively multimodal with a 1M-token context window - A 320B-A18B model released under the MIT License - Previously previewed as Ox Alpha, running entirely on Chinese AI chips Blog: https://t.co/tzOmB7gdZP Available now across all official platforms: Weights: https://t.co/9LRMahY9Wa API: https://t.co/VcaQnzYmS9 Coding Plan: https://t.co/Nk8Y98HNhU ZCode: https://t.co/Peepqv4XSx Chat: https://t.co/WCqWT0qCQb AutoClaw: https://t.co/aGEG5HqTTb

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  5. mntruellProduct release
    AI score67

    English translation is being prepared.

    Quoted postGrok Bot · @bot

    All SuperGrok and Cursor Pro subscribers now have access to Grok Bot. We're also resetting weekly usage limits for all users. Enjoy!

    #Product updates#Agents#xAI

Wednesday10 items

  1. unslothaiFirst-partyProduct release
    AI score69

    English translation is being prepared.

    Quoted postQwen · @Alibaba_Qwen

    ⚡Meet Qwen3.8-Flash, a multimodal MoE and an early preview of the Qwen4 architecture, now open-weight! The production version Qwen3.8-Flash will be available soon via QwenCloud API at just $ 0.16/1M input tokens and $ 0.47/1M output tokens. 125B parameters + 51B N-gram embeddings, with just 6B activated per token. Unmatched cost-efficiency. What's new: 🥳 - Next architecture: GDN + QSA hybrid attention, Gated Residual, N-gram Embedding & Muon optimizer, serving as a precursor to the architecture used in Qwen4. - Dramatically lower training and inference costs: trained at just 1/9 the cost of Qwen3.7-Plus, while outperforming it across the board with especially strong gains in coding and office tasks. - Strong performance: scoring 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). - 262K native context, extensible to 1M with YaRN. We’re also releasing the weights for Qwen3.8-Flash-Next, giving the community an early look at the new architecture we’re exploring for Qwen4.🚀 We can't wait to see what you build with Qwen3.8-Flash!👀👇 - Blog: https://t.co/M5hYypFLgJ - Technical Report: https://t.co/IF0gObIkQO - Hugging Face: https://t.co/6ow8QVAABt - ModelScope: https://t.co/tDOn2jNuFG

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