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  1. Story1 reports · 1 facts

    Thomas Wolf estimates that the shared training compute on Hugging Face Hub is nearly $1 billion

    Thomas Wolf estimates that the value of shared training compute on Hugging Face Hub has approached nearly $1 billion. He noted that about 30-plus labs, including Meta, NVIDIA, Alibaba, DeepSeek, Moonshot, and Mistral, share training compute on the platform, and it can be downloaded for free; by the end of 2023, that figure was still less than $40 million. Wolf said the number is a lower bound: about half comes from official compute data, the rest is reasonable inference, and it does not yet include roughly 900,000 community fine-tuned models. There are currently no subsequent developments.

    s Wolf estimates that the value of shared training compute on Hugging Face Hub has approached nearly $1 billion. He noted that about 30-plus labs, including Meta, NVIDIA, Alibaba, DeepSeek, Moonshot, and Mistral, share training compute on the platform, and it can be downloaded for free; by the end of 2023, that figure was still less than $40 million. Wolf said the number is a lower bound: about half comes from official compute data, the rest is reasonable inference, and it does not yet include r

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  2. Story1 reports · 1 facts

    Arena.ai says user satisfaction is the biggest gap for AI labs

    Arena.ai puts forward a judgment: whether users enjoy collaborating with agents is the biggest gap between AI labs. This judgment is based on Arena's tracking of four Agent Arena signals for the best agents from six labs—Anthropic, OpenAI, Google DeepMind, Kimi, xAI, and DeepSeek—from late June to early October. Among them, on the Praise vs Complaint metric, OpenAI and Anthropic were +35 points above the leaderboard average, while Moonshot, xAI, and DeepSeek ranged between +5 and +13; on Confirmed Success, the average lab's best model rose from -4 to +14 points within three months, with Anthropic leading at +20 with Claude Opus 5.5 (Max); the Bash Recovery gap narrowed from 34 to 16 points, and Steerability from 19 to 11 points. There are currently no further developments.

    st gap between AI labs. This judgment is based on Arena's tracking of four Agent Arena signals for the best agents from six labs—Anthropic, OpenAI, Google DeepMind, Kimi, xAI, and DeepSeek—from late June to early October. Among them, on the Praise vs Complaint metric, OpenAI and Anthropic were +35 points above the leaderboard average, while Moonshot, xAI, and DeepSeek ranged between +5 and +13; on Confirmed Success, the average lab's best model rose from -4 to +14 points within three months, wit

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  3. Story1 reports · 1 facts

    Aleph Alpha releases the Kolibri-1 open-weight model

    On October 16, 2026, Aleph Alpha released the open-weight model Kolibri-1. According to heise online, the model has 78B total parameters and uses a MoE architecture with 384 experts and 6 routed experts, activating only 3.46B parameters during inference, with a context of 256K tokens 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 1,137 tokens, whereas the compared DeepSeek-V4.1-Flash requires 1,910 tokens. Pretraining used 20 trillion tokens, with data up to June 18, 2026. The report's author tested it and said its answers were correct and its German reasoning process evident, and said the Merlin-Arthur protocol makes the model hallucinate less in benchmarks, but its actual performance still needs to be tested in real-world use. There are currently no subsequent developments.

    on German and English, and its tokenizer segments German words more token-efficiently; for example, the German Bundestag glossary requires only 1,137 tokens, whereas the compared DeepSeek-V4.1-Flash requires 1,910 tokens. Pretraining used 20 trillion tokens, with data up to June 18, 2026. The report's author tested it and said its answers were correct and its German reasoning process evident, and said the Merlin-Arthur protocol makes the model hallucinate less in benchmarks, but its actual perf

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  1. Story1 reports · 1 facts

    Teacher shares practice of a 9-agent collective lesson-preparation pipeline

    On October 17, 2026, a high school English teacher, under the identity "Ku Sir," posted on the Tencent Cloud Developer Community, sharing how he and a 3-person lesson-preparation group transformed collective lesson preparation into a four-stage pipeline of 9 AI agents plus 3 human roles. The article states that the lesson-preparation loop that originally spanned two weeks was run through in a single morning in a test in early September; four sets of prompts respectively handled three-dimensional attribution of student learning conditions, four-part discussion opinions each no more than 100 characters, an 80-minute debate hosting script, and three-dimensional scoring and archiving, and the consensus execution rate changed from frequently left unfinished to 100%. The author also recorded that a 6-point drop in the second quality-check score was actually due to a change in the scoring rubric, teachers' resistance to format constraints, and a member on a business trip being covered by the deputy preparer filling three gaps, and stated that the SOP is general across disciplines and that the prompts can run on Doubao, Kimi, ChatGPT, and DeepSeek. At present, this practice is still presented only as the author's self-account, with no subsequent independent progress.

    hor also recorded that a 6-point drop in the second quality-check score was actually due to a change in the scoring rubric, teachers' resistance to format constraints, and a member on a business trip being covered by the deputy preparer filling three gaps, and stated that the SOP is general across disciplines and that the prompts can run on Doubao, Kimi, ChatGPT, and DeepSeek. At present, this practice is still presented only as the author's self-account, with no subsequent independent progress.

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