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Wednesday

  1. FactRelated story

    Recursive releases open-weight local coding model Flint

    On October 21, 2026, Recursive Superintelligence released Flint, an open-weight local coding model. According to related reports, Flint is a 40B mixture-of-experts model that activates approximately 2.5B parameters per token, is licensed under Apache 2.0, and its weights have been made available on Hugging Face; it was built from scratch in under 4 months by the automated research system Eureka Machine. On October 22, Hugging Face co-founder Thomas Wolf commented that Recursive researchers quantized the lm_head—which is usually recommended to be kept at high precision—to 4 bits, and the result differed from bf16 by only 0.7 percentage points. On the same day, OpenRouter launched Flint, saying it is aimed at coding agents, supports a 262K context, reasoning, and tool calling, and is free to use during the launch period; Unsloth released a Dynamic GGUF quantized version of Flint, saying it can run locally on devices with 24GB of memory. The model now offers multiple usage paths through open weights, hosting platforms, and quantized versions.

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    View story:Recursive releases open-weight local coding model Flint

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Friday

  1. Story14 reports · 1 facts

    Mistral releases Large 4 weights on Hugging Face

    On October 29, 2026, Mistral released the open weights of Mistral Large 4 (Le Chonk), a 1-trillion-parameter native multimodal model that continues reinforcement learning on the basis of the preview version, with improved coding and defensive cybersecurity capabilities. According to data published by Mistral AI, the model scores 19.7% on FrontierCode 2.0 and 48% on the Artificial Analysis Coding Agent Index, roughly on par with GLM-5.3, but uses an average of 47% fewer tokens per task; on Terminal-Bench 4.0 it improves by 13 percentage points to 41%. The model supports three reasoning modes, uses a modified MIT Mistral Ecosystem Open-Weight license, and is priced at $0.70 per million input tokens, $0.07 for cached input, and $2.10 for output via API. On the same day, Unsloth released a 1-bit GGUF quantized version of the model, stating that it can run locally on a Mac Studio, retains 91% top-1 accuracy after quantization, and provides a download link and usage guide.

    On October 29, 2026, Mistral released the open weights of Mistral Large 4 (Le Chonk), a 1-trillion-parameter native multimodal model that continues reinforcement learning on the basis of the preview version, with improved coding and defensive cybersecurity capabilities. According to data published by Mistral AI, the model scores 19.7% on FrontierCode 2.0 and 48% on the Artificial Analysis Coding Agent Index, roughly on par with GLM-5.3, but uses an average of 47% fewer tokens per task; on Termin

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  2. Story1 reports · 1 facts

    Mistral calls Le Chonk the world's best open-weight model

    On October 29, 2026, Mistral released the official version of Le Chonk, a trillion-parameter open-weight model, calling it the world's best open-weight model for automated coding and cybersecurity, available for free. Pierre Stock, Mistral's Vice President of Science, said the model was trained on increasingly difficult cybersecurity and coding problems, enabling it to match or surpass models from Chinese labs such as DeepSeek and Z.ai. Stock also emphasized that the West needs its own advanced open-weight models, because closed-source models can be shut down remotely, whereas open weights cannot have access cut off once released. Currently, this event has only been reported by Wired AI, with no further developments yet.

    On October 29, 2026, Mistral released the official version of Le Chonk, a trillion-parameter open-weight model, calling it the world's best open-weight model for automated coding and cybersecurity, available for free. Pierre Stock, Mistral's Vice President of Science, said the model was trained on increasingly difficult cybersecurity and coding problems, enabling it to match or surpass models from Chinese labs such as DeepSeek and Z.ai. Stock also emphasized that the West needs its own advanced

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Thursday

  1. FactRelated story

    Mistral calls Le Chonk the world's best open-weight model

    On October 29, 2026, Mistral released the official version of Le Chonk, a trillion-parameter open-weight model, calling it the world's best open-weight model for automated coding and cybersecurity, available for free. Pierre Stock, Mistral's Vice President of Science, said the model was trained on increasingly difficult cybersecurity and coding problems, enabling it to match or surpass models from Chinese labs such as DeepSeek and Z.ai. Stock also emphasized that the West needs its own advanced open-weight models, because closed-source models can be shut down remotely, whereas open weights cannot have access cut off once released. Currently, this event has only been reported by Wired AI, with no further developments yet.

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    View story:Mistral calls Le Chonk the world's best open-weight model

Wednesday

  1. Story1 reports · 1 facts

    Google releases Gemini 4 Argon and demonstrates complex task capabilities

    On October 27, 2026, Google released its latest model, Gemini 4 Argon, and demonstrated its ability to handle complex real-world tasks. Google says the model can reason, code, and coordinate tools, and the company gave 8 examples: first, writing 5,000 lines of mathematical code from scratch with zero source material, rendering a 3D sunlit library; second, processing more than 80,000 raw files from the NASA Mars mission in Antigravity and generating executive briefing slides. There is currently no information on further developments.

    On October 27, 2026, Google released its latest model, Gemini 4 Argon, and demonstrated its ability to handle complex real-world tasks. Google says the model can reason, code, and coordinate tools, and the company gave 8 examples: first, writing 5,000 lines of mathematical code from scratch with zero source material, rendering a 3D sunlit library; second, processing more than 80,000 raw files from the NASA Mars mission in Antigravity and generating executive briefing slides. There is currently n

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Friday

  1. Story1 reports · 1 facts

    Google DeepMind releases Gemini 4 Argon

    On October 23, 2026, Google DeepMind released the frontier model Gemini 4 Argon. Google's official blog stated that the model performs outstandingly on complex tasks such as coding, mathematics, and 3D world building, and surpasses other frontier models on benchmarks across multiple domains; the company also showcased five community creative projects, including a 3D Earth that maps wildfires and seismic waves in real time, a turbofan engine simulation with live data, a 3D horse-drawn carriage generated from a single image, and a multiplayer capture-the-flag browser game, and said the model had already been made broadly available last week. On the same day, another report stated that Argon can learn physical tasks by watching videos and transfer them to Apptronik's Apollo humanoid robot, with Apollo hitting seven consecutive shots in internal tests; the report also said Argon scored 87% on the embodied reasoning benchmark ERQA. On October 24, the Gemini for Android app pushed a redesign of its sidebar and settings interface via a server-side update, and reports said the redesign had already arrived on iOS, macOS, and Web alongside Gemini 4 Argon last week.

    On October 23, 2026, Google DeepMind released the frontier model Gemini 4 Argon. Google's official blog stated that the model performs outstandingly on complex tasks such as coding, mathematics, and 3D world building, and surpasses other frontier models on benchmarks across multiple domains; the company also showcased five community creative projects, including a 3D Earth that maps wildfires and seismic waves in real time, a turbofan engine simulation with live data, a 3D horse-drawn carriage ge

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Sunday

  1. Story1 reports · 1 facts

    Memory Tensor open-sources and releases the MemT 1.0 memory-native model series

    On October 20, 2026, Memory Tensor open-sourced and released the MemT 1.0 series of memory-native models. The series includes two models: Nano has 36B parameters with 3B activated, based on Qwen3.6-35B-A3B; Flash has 288B parameters with 17B activated, based on DeepSeek-V4-Flash. The models add a memory branch parallel to attention in selected Transformer modules, encoding experience as updatable matrix states that participate in subsequent generation. On 500 SWE-bench Verified tasks, Nano achieved an average success rate of 69.73% at key checkpoints, versus 66.07% for the no-experience baseline; under a 128K long history, end-to-end inference speed is approximately 1.86 times that of the full-context approach, and the persistent state footprint per trajectory is one sixty-sixth of the full-context KV Cache. There are currently no subsequent development facts.

    On October 20, 2026, Memory Tensor open-sourced and released the MemT 1.0 series of memory-native models. The series includes two models: Nano has 36B parameters with 3B activated, based on Qwen3.6-35B-A3B; Flash has 288B parameters with 17B activated, based on DeepSeek-V4-Flash. The models add a memory branch parallel to attention in selected Transformer modules, encoding experience as updatable matrix states that participate in subsequent generation. On 500 SWE-bench Verified tasks, Nano achie

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