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Friday

  1. Story1 reports · 1 facts

    Kakao Open-Sources Two Korean-Optimized Embedding Models

    On October 29, Kakao open-sourced two Korean-optimized embedding models on Hugging Face: Kanana-2-3b-embedding (3 billion parameters) and Kanana-2-1.3b-embedding (1.3 billion parameters), targeting personalized AI agents and RAG scenarios. Kakao said that in internal evaluations, the two models outperformed llama-nemotron-embed-1b-v2 and Qwen3-Embedding-4B of comparable scale in Korean retrieval performance, with comparable English performance and higher inference efficiency, while memory usage and inference speed improved by more than tenfold compared with the previous-generation Kanana embedding models. The models adopt MRL, allowing developers to adjust the embedding vector size, and are released under the Kanana Open License for commercial use. There are currently no further developments.

    On October 29, Kakao open-sourced two Korean-optimized embedding models on Hugging Face: Kanana-2-3b-embedding (3 billion parameters) and Kanana-2-1.3b-embedding (1.3 billion parameters), targeting personalized AI agents and RAG scenarios. Kakao said that in internal evaluations, the two models outperformed llama-nemotron-embed-1b-v2 and Qwen3-Embedding-4B of comparable scale in Korean retrieval performance, with comparable English performance and higher inference efficiency, while memory usage

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  2. Story1 reports · 1 facts

    Polymathic AI and Princeton and other institutions release FlashMap

    On October 28, 2026, researchers from Polymathic AI, Princeton University, USC ISI, NYU, and the Flatiron Institute released FlashMap. According to a MarkTechPost report, the method replaces the discrete diffusion drafter with continuous flow mapping, allowing a complete block of 16 tokens to be drafted in a single forward pass. On Qwen3-8B, it achieves up to 6.14x lossless speedup at GSM8K temperature 0, with the drafter having only 0.5B parameters. The drafter weights for Qwen3-8B and Qwen3-4B have been released on Hugging Face, along with vLLM and SGLang plugins. There are currently no further developments.

    eleased FlashMap. According to a MarkTechPost report, the method replaces the discrete diffusion drafter with continuous flow mapping, allowing a complete block of 16 tokens to be drafted in a single forward pass. On Qwen3-8B, it achieves up to 6.14x lossless speedup at GSM8K temperature 0, with the drafter having only 0.5B parameters. The drafter weights for Qwen3-8B and Qwen3-4B have been released on Hugging Face, along with vLLM and SGLang plugins. There are currently no further developments.

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Wednesday

  1. Story1 reports · 1 facts

    SoMark open-sources the 1.3B document parsing model OXR-1.0

    SoMark has open-sourced OXR-1.0, a 1.3B-parameter document parsing model that can convert PDFs and page images into Markdown or JSON. The weights have been released on Hugging Face, and lightweight testing can run on a single 24GB GPU. According to Rohan Paul, the model ranks first on olmOCR-Bench, OmniDocBench, and DocParsingBench, achieving a decoding speed of 558 tokens/s on a single NVIDIA RTX PRO 6000 GPU and parsing 100 pages end-to-end in about 101 seconds; its post-training stage employs a recursive self-improvement (RSI) loop, in which a general-purpose VLM judges parsing results and generates newly annotated pages, eliminating the need for manual annotation. QbitAI reports that the OXR main model open-sourced by the SoMark team has only 1.3B parameters and topped all three benchmark tests, with full parsing speed exceeding that of PaddleOCR-VL-1.6 and MinerU2.5-Pro, which were also evaluated in the same comparison. It covers 13 categories of document elements, including text, tables, formulas, chemical structure formulas, and code blocks, and can parse reading order and output layout coordinates.

    SoMark has open-sourced OXR-1.0, a 1.3B-parameter document parsing model that can convert PDFs and page images into Markdown or JSON. The weights have been released on Hugging Face, and lightweight testing can run on a single 24GB GPU. According to Rohan Paul, the model ranks first on olmOCR-Bench, OmniDocBench, and DocParsingBench, achieving a decoding speed of 558 tokens/s on a single NVIDIA RTX PRO 6000 GPU and parsing 100 pages end-to-end in about 101 seconds; its post-training stage employs

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  2. Story3 reports · 1 facts

    Liquid AI releases six open-weight 2B-class models in the LFM3 series

    On October 28, 2026, Liquid AI released the LFM3 series of open-weight models, comprising six model instances in total, covering text, vision, audio, Omni, and the d1 decision model, all branched from LFM3-1.7B-Base. According to the release information published on Hugging Face, LFM3-1.7B-Base adopts a triple-hybrid layer structure, was pretrained on 22T tokens, and in the mid-training stage over the final 3.5T tokens added more agent trajectories and tool-calling data, with the context window extended to 256K tokens; the company states that LFM3-1.7B is about 25% faster than LFM2-1.2B at an 8K context, scores 38.42 versus 3.02 on WebArena-Verified, and LFM3-VL-2B's average score across 62 vision benchmarks rose from 57.6 to 71.4. All models are available for download on Hugging Face, along with single-GPU fine-tuning notebooks. A separate report the same day stated that the LFM3-1.7B text model outperforms its 2.6B predecessor, uses a 24-layer triple-hybrid architecture, and scores 41.29 on AutomationBench.

    ng six model instances in total, covering text, vision, audio, Omni, and the d1 decision model, all branched from LFM3-1.7B-Base. According to the release information published on Hugging Face, LFM3-1.7B-Base adopts a triple-hybrid layer structure, was pretrained on 22T tokens, and in the mid-training stage over the final 3.5T tokens added more agent trajectories and tool-calling data, with the context window extended to 256K tokens; the company states that LFM3-1.7B is about 25% faster than LFM

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Tuesday

  1. Story1 reports · 1 facts

    Baidu releases open-source model Unlimited OCR 2

    On October 27, 2026, Baidu released and open-sourced the document parsing model Unlimited OCR 2. According to Baidu's official introduction, the model uses a new SR-SWA mechanism to compress the attention interaction between text and vision into a fixed-size working reference, thereby parsing documents of almost arbitrary length within a limited context window; Baidu says that as documents grow longer, per-step decoding latency and memory usage remain nearly constant, and that it achieves SOTA performance on MPDocBench and MarathonDocBench, which cover documents ranging from dozens to hundreds of pages. On the same day, QbitAI reported that in a single-sequence test at the hundred-page level, the model reduced peak KV cache from 47GB under standard decoding to 1.77GB, a reduction of about 96%; in a 16-way concurrent test, end-to-end throughput rose from 360 to 1315 tokens/second, about 3.65 times that of standard decoding. The model has been open-sourced on Hugging Face and GitHub.

    TA performance on MPDocBench and MarathonDocBench, which cover documents ranging from dozens to hundreds of pages. On the same day, QbitAI reported that in a single-sequence test at the hundred-page level, the model reduced peak KV cache from 47GB under standard decoding to 1.77GB, a reduction of about 96%; in a 16-way concurrent test, end-to-end throughput rose from 360 to 1315 tokens/second, about 3.65 times that of standard decoding. The model has been open-sourced on Hugging Face and GitHub.

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Monday

  1. Story1 reports · 1 facts

    Tencent Hunyuan open-sources Chinese-English simultaneous interpretation model Hy-SimulMT

    On October 26, 2026, Tencent Hunyuan open-sourced the real-time Chinese-English simultaneous interpretation model Hy-SimulMT, with a latency of about 3 seconds and support for real-time voice cloning; the model has been released on Hugging Face, ModelScope, and GitHub. Tencent Hunyuan officially stated that its performance surpasses most open-source models and commercial APIs. The next day, IT Home reported that the model supports real-time Chinese-English speech-to-speech translation and has been launched for free on Tencent's Hy Translation App; according to the official statement, the measured response time is about 3 seconds, compared with an average of 4 to 5 seconds for professional human interpreters. On RealSI, an open-source long-audio evaluation benchmark, the model achieved the best results among open-source models at the lowest latency, and is said to outperform most commercial simultaneous interpretation APIs; in comparisons with six CATTI-certified simultaneous interpretation experts, it scored higher in translation quality and had lower latency.

    e real-time Chinese-English simultaneous interpretation model Hy-SimulMT, with a latency of about 3 seconds and support for real-time voice cloning; the model has been released on Hugging Face, ModelScope, and GitHub. Tencent Hunyuan officially stated that its performance surpasses most open-source models and commercial APIs. The next day, IT Home reported that the model supports real-time Chinese-English speech-to-speech translation and has been launched for free on Tencent's Hy Translation App

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  2. Story2 reports · 2 facts

    Clément Delangue says developing AI in a closed manner is not safe

    On October 26, 2026, Hugging Face CEO Clément Delangue commented on a report, saying that if one believes concentrating AI in a closed environment makes it safe, that judgment needs updating. The report he cited said that a group of former DOGE cybersecurity researchers used AI agents to penetrate Anthropic's systems in less than 12 hours, including the Slack channels where the most sensitive models were discussed. The next day, the security startup Cathedral was reported to have used AI agents in a security test authorized by Anthropic to enter its sales, HR, and IT systems through third-party enterprise software vulnerabilities in less than 12 hours, accessing meeting transcripts, compensation and hiring details, and internal Slack messages. Anthropic confirmed the intrusion was real but stressed that messages customers sent to Claude, core code, and model weights were not leaked, and said its monitoring system had detected anomalies and cut off access before the report was submitted.

    On October 26, 2026, Hugging Face CEO Clément Delangue commented on a report, saying that if one believes concentrating AI in a closed environment makes it safe, that judgment needs updating. The report he cited said that a group of former DOGE cybersecurity researchers used AI agents to penetrate Anthropic's systems in less than 12 hours, including the Slack channels where the most sensitive models were discussed. The next day, the security startup Cathedral was reported to have used AI agents

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  3. Story1 reports · 1 facts

    MiniMax adds 11 community contributions to the H3 ecosystem index

    On October 26, 2026, MiniMax announced 11 new community contributions to the H3 video generation model ecosystem and has added them to the H3 ecosystem index. According to the official statement, these contributions cover 8-step reference-conditioned video plus audio, 4-step distillation, sparse attention, and reference token compression, and are integrated into deployment and workflows on GPU servers, TPU, and Apple Silicon. MiniMax said these improvements bring lower experimentation costs, finer creative control, and more runtime environment options, and thanked haoailab, Hugging Face, and teams including PTD, DMAD, SparkH3, TorchTPU, and FlashML. On the same day, MiniMax Design (H3) said the community continues to open up new possibilities for H3, with the ecosystem developing faster than expected, bringing faster generation, better control, and operation on more platforms.

    on GPU servers, TPU, and Apple Silicon. MiniMax said these improvements bring lower experimentation costs, finer creative control, and more runtime environment options, and thanked haoailab, Hugging Face, and teams including PTD, DMAD, SparkH3, TorchTPU, and FlashML. On the same day, MiniMax Design (H3) said the community continues to open up new possibilities for H3, with the ecosystem developing faster than expected, bringing faster generation, better control, and operation on more platforms.

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Sunday

  1. Story1 reports · 1 facts

    Ollama launches speech model Phonon, supporting text-to-speech

    Ollama launched its speech model Phonon on October 24, 2026, beginning support for text-to-speech. According to the Ollama Blog, Phonon is a series of small text-to-speech models from Gradium, currently available on macOS, with Windows and Linux support coming soon. On a MacBook Pro with M5 Max, Ollama uses MLX acceleration to run 35% faster than Gradium's own runtime, with word error rate on par with the Hugging Face Transformers reference implementation. Phonon is compatible with OpenAI's /v1/audio/speech interface, so existing OpenAI SDK code only needs its base URL changed. On licensing, phonon is released under Apache 2.0, while the 40M model uses the CC BY-NC 4.0 license and is restricted to non-commercial use. There are currently no subsequent developments.

    indows and Linux support coming soon. On a MacBook Pro with M5 Max, Ollama uses MLX acceleration to run 35% faster than Gradium's own runtime, with word error rate on par with the Hugging Face Transformers reference implementation. Phonon is compatible with OpenAI's /v1/audio/speech interface, so existing OpenAI SDK code only needs its base URL changed. On licensing, phonon is released under Apache 2.0, while the 40M model uses the CC BY-NC 4.0 license and is restricted to non-commercial use. Th

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  2. Story1 reports · 1 facts

    OlmoEarth v1.3 released, including the Base model and global precomputed embeddings

    OlmoEarth v1.3 has been released, including the OlmoEarth v1.3 Base model, global precomputed embeddings, the OlmoEarth Embeddings Explorer, and a technical report. According to Hugging Face, this version generates 128-dimensional embedding vectors for every 10-meter pixel worldwide, with the first 64 dimensions usable on their own, and it has made the global embeddings openly available to the community on Source Cooperative. In 27 evaluations covering six continents, the 128-dimensional version scored highest on 20, and the 64-dimensional version led on 18; training compute was 53% of v1.2 and 18% of v1. Model weights, pretraining data, and code are all open source, and a final run to pretrain the Base version costs approximately $2,720 in H100 rental. There are currently no subsequent development facts.

    OlmoEarth v1.3 has been released, including the OlmoEarth v1.3 Base model, global precomputed embeddings, the OlmoEarth Embeddings Explorer, and a technical report. According to Hugging Face, this version generates 128-dimensional embedding vectors for every 10-meter pixel worldwide, with the first 64 dimensions usable on their own, and it has made the global embeddings openly available to the community on Source Cooperative. In 27 evaluations covering six continents, the 128-dimensional version

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  3. Story1 reports · 1 facts

    Ai2 releases PLANTEUR, a benchmark for tropical crop mapping

    Ai2 releases PLANTEUR, a benchmark for tropical crop mapping. The benchmark extends the PASTIS benchmark to six tropical regions—Réunion, Guadeloupe, Martinique, French Guiana, Mayotte, and Saint-Martin—and includes 2,750 aligned time series from Sentinel-2, Sentinel-1, and Landsat-8, as well as parcel labels covering 7 crop types across 4,506 km2. Ai2 says all evaluated methods score significantly lower on PLANTEUR than on PASTIS, and that each model's accuracy drops by about half when tested on small islands it never saw during training. PLANTEUR is open on Hugging Face and GitHub. There are no follow-up developments at present.

    Guadeloupe, Martinique, French Guiana, Mayotte, and Saint-Martin—and includes 2,750 aligned time series from Sentinel-2, Sentinel-1, and Landsat-8, as well as parcel labels covering 7 crop types across 4,506 km2. Ai2 says all evaluated methods score significantly lower on PLANTEUR than on PASTIS, and that each model's accuracy drops by about half when tested on small islands it never saw during training. PLANTEUR is open on Hugging Face and GitHub. There are no follow-up developments at present.

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  4. Story1 reports · 1 facts

    Hao AI Lab announces that FastH3 Omni can run locally on DGX Spark, RTX GPUs, and Apple MLX

    Hao AI Lab announced that FastVideo FastH3 Omni can run locally on NVIDIA DGX Spark, RTX GPUs, and Apple MLX. The lab's reported performance figures include: generating a 5-second 480p video from a single photo and a prompt takes about 3 minutes, and about 2 minutes on two Sparks; FastH3 T2AV takes 52.6 seconds on a single Spark and 30.7 seconds on two Sparks. The clips in the demo video were all generated by DGX Spark using an 8-step NVFP4 checkpoint. The lab also provided links to a blog, a Cookbook, and Hugging Face. There are currently no subsequent developments.

    DGX Spark, RTX GPUs, and Apple MLX. The lab's reported performance figures include: generating a 5-second 480p video from a single photo and a prompt takes about 3 minutes, and about 2 minutes on two Sparks; FastH3 T2AV takes 52.6 seconds on a single Spark and 30.7 seconds on two Sparks. The clips in the demo video were all generated by DGX Spark using an 8-step NVFP4 checkpoint. The lab also provided links to a blog, a Cookbook, and Hugging Face. There are currently no subsequent developments.

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  5. Story1 reports · 1 facts

    Modal launches LLM training library Modal Dojo

    On October 21, 2026, Modal released Modal Dojo, an LLM training library. According to Modal's official engineering blog, the library provides tuned default recipes, modifiable parameters, and an out-of-the-box metrics observability dashboard, and supports models ranging from 500 million to trillions of parameters; it is built on open-source frameworks such as Miles and Slime, can load datasets from Hugging Face, and lets agents query run status, logs, and trajectories via a CLI. The same source also states that Axiom Bio has used it to launch training of DeepSeek V4.1 Flash with a 64k context, running on 64 B300s. There are currently no subsequent independent developments.

    eters, and an out-of-the-box metrics observability dashboard, and supports models ranging from 500 million to trillions of parameters; it is built on open-source frameworks such as Miles and Slime, can load datasets from Hugging Face, and lets agents query run status, logs, and trajectories via a CLI. The same source also states that Axiom Bio has used it to launch training of DeepSeek V4.1 Flash with a 64k context, running on 64 B300s. There are currently no subsequent independent developments.

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Friday

  1. Story5 reports · 2 facts

    MiniMax open-sources M3.1 Flash model weights

    English text is being prepared.

    B total parameters and activates 23B per token, and uses MiniMax Sparse Attention and DSpark speculative decoding to improve inference efficiency; the weights are now available on Hugging Face and ModelScope, with API and MCode desktop entry points also provided. On the same day, Together AI, as a day-one launch partner, deployed the model to its AI Native Cloud production environment, so users do not need to manage GPUs themselves; according to Together AI, the model targets agentic tasks, codi

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  2. Story1 reports · 1 facts

    OpenAI fires 3 safety researchers after leak investigation

    According to a WSJ report, OpenAI fired 3 safety researchers after a leak investigation. The report says safety staff discovered disappearing Signal chats between these 3 safety researchers and an outside safety researcher. The phone checks began with a September news report that said OpenAI's latest model Astra uses a new approach that makes it smarter but writes down less of its thought process, so humans can see less of the reasoning. OpenAI treated the report as a leak of its secret model design and began tracing the source of the information; investigators listed Ryan Greenblatt, who had helped METR analyze logs from the July Hugging Face hack, as a suspected leaker, and he denies it. Currently the public information comes only from this report, with no further developments yet.

    latest model Astra uses a new approach that makes it smarter but writes down less of its thought process, so humans can see less of the reasoning. OpenAI treated the report as a leak of its secret model design and began tracing the source of the information; investigators listed Ryan Greenblatt, who had helped METR analyze logs from the July Hugging Face hack, as a suspected leaker, and he denies it. Currently the public information comes only from this report, with no further developments yet.

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  3. Story1 reports · 1 facts

    Mifeng and other co-initiators announce the results of this round of RoboColiseum evaluation

    Mifeng Technology, together with nearly 20 institutions including Hugging Face, NVIDIA, and Tsinghua University, co-initiated the embodied evaluation platform RoboColiseum, and announced the results of this round of evaluation on October 20. According to a Leikeji report on October 23, Claude Opus 5.5 ranked first overall among the four flagship models, followed closely by GPT-6 Astra; GPT-6 Astra scored 99.35 on instruction following, while Opus 5.5 performed better on spatial understanding and general manipulation, and consumed the fewest tokens. The platform contains 78 simulation evaluation tasks, with a simulation-to-real correlation of 89.5%, and more than 100 teams have participated since it launched in August.

    Mifeng Technology, together with nearly 20 institutions including Hugging Face, NVIDIA, and Tsinghua University, co-initiated the embodied evaluation platform RoboColiseum, and announced the results of this round of evaluation on October 20. According to a Leikeji report on October 23, Claude Opus 5.5 ranked first overall among the four flagship models, followed closely by GPT-6 Astra; GPT-6 Astra scored 99.35 on instruction following, while Opus 5.5 performed better on spatial understanding and

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  4. Story3 reports · 1 facts

    Anthropic donates Agent Skills to the Agentic AI Foundation

    Anthropic announced that it will donate Agent Skills to the Agentic AI Foundation (AAIF). According to its first-party information, MCP had previously also been donated to the same foundation; since December 2025, more than 70 agent platforms have adopted the standard, including ChatGPT, Cursor, GitHub Copilot, and VS Code. Subsequent reports stated that AAIF, under the Linux Foundation, announced on October 22, 2026 that it had accepted four projects: in addition to Anthropic's Agent Skills, these include OpenAI's Open Responses, Hugging Face's OpenEnv, and Alibaba's OpenSandbox; Agent Skills is a universal format that allows different agents to reuse business knowledge and workflows, and Anthropic made it public as an open standard in December 2025. Reports also stated that the donation includes the name, logo, the GitHub organization account agentskills, and the agentskills.io domain; going forward, a technical operations committee composed of core maintainers from multiple organizations will decide the development direction, with Anthropic's David Soria Parra and Mahesh Murag serving as lead maintainers; the number of projects under AAIF has grown from three at its founding to ten.

    AAIF, under the Linux Foundation, announced on October 22, 2026 that it had accepted four projects: in addition to Anthropic's Agent Skills, these include OpenAI's Open Responses, Hugging Face's OpenEnv, and Alibaba's OpenSandbox; Agent Skills is a universal format that allows different agents to reuse business knowledge and workflows, and Anthropic made it public as an open standard in December 2025. Reports also stated that the donation includes the name, logo, the GitHub organization account

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Saturday

  1. Story1 reports · 1 facts

    Tencent Hunyuan launches open-source interactive video world model Hy-Nomas

    Tencent Hunyuan launched the open-source interactive video world model Hy-Nomas on October 24, 2026. According to official release information, the model supports interaction using text prompts and camera trajectories, and has long-horizon generation capability with world memory; it can be adapted to specialized domains such as robotics via lightweight LoRA, and has been optimized for inference on consumer-grade GPUs. The official release also provides website, GitHub, Hugging Face, and demo entry points. There are currently no subsequent development facts.

    d model Hy-Nomas on October 24, 2026. According to official release information, the model supports interaction using text prompts and camera trajectories, and has long-horizon generation capability with world memory; it can be adapted to specialized domains such as robotics via lightweight LoRA, and has been optimized for inference on consumer-grade GPUs. The official release also provides website, GitHub, Hugging Face, and demo entry points. There are currently no subsequent development facts.

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Thursday

  1. Story2 reports · 1 facts

    Google open-sources TorchTPU native PyTorch stack

    Google collaborated with Meta's PyTorch team to launch the open-source project TorchTPU, providing a native PyTorch backend for Google TPU; migration only requires changing model.to("cuda") to model.to("tpu"). The project supports both eager and torch.compile modes, adds a new DeferAndFuse eager mode, covers about 450 Aten operators, and has been verified to run out of the box on 4,917 Hugging Face models, with a Tier 1 pass rate of 98% and Tier 2 numerical consistency relative to GPU of 97%. The official statement says it has been used in the inference and training production workloads of some Google Cloud major customers, and is integrated with vLLM, SGLang, TorchTitan, Ray, and TorchRec. Subsequent reports said that at PyTorch Conference North America 2026, Google open-sourced TorchTPU, whose runtime no longer depends on JAX, and existing workloads can be migrated with only minor code changes; PyTorch noted that TorchTPU's default Skinny Collectives scheme suffers roughly 30–40% end-to-end performance loss under distributed workloads compared with optimized schemes, and achieving peak performance still requires Pallas kernels and vLLM-T

    The project supports both eager and torch.compile modes, adds a new DeferAndFuse eager mode, covers about 450 Aten operators, and has been verified to run out of the box on 4,917 Hugging Face models, with a Tier 1 pass rate of 98% and Tier 2 numerical consistency relative to GPU of 97%. The official statement says it has been used in the inference and training production workloads of some Google Cloud major customers, and is integrated with vLLM, SGLang, TorchTitan, Ray, and TorchRec. Subsequen

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  2. Story7 reports · 4 facts

    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.

    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

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