AYi(@AYi_AInotes):Xiaohongshu (RedNote)—often called China's Facebook, with 350 million users and over a decade of lifestyle notes—has quietly open-sourced a large language model. I've always believed that in the end, the LLM race won't be won by whoever is smarter, but by whoever holds data that others can't copy. Math and code? Anyone can scrape those. But "how to plan a wedding," "how to organize a trip," "how to open a shop without stepping on every landmine"—that kind of knowledge? Only Xiaohongshu has it. What's interesting is its technical choice. A sibling model in the same series just scored a perfect 42/42 at IMO 2026—a feat achieved by only 7 human contestants worldwide. Yet this open-sourced version didn't chase static benchmark scores. Instead, it aims directly at long-horizon life agents: a 512K context window, 16B active parameters, and a method called TEMPO that specifically tackles the RL training challenge of tasks spanning tens of hours. While everyone else is racing to top the exams, Xiaohongshu went ahead and raised an AI that actually knows how to live.

2026.08.14 18:06

Xiaohongshu (RedNote)—often called China's Facebook, with 350 million users and over a decade of lifestyle notes—has quietly open-sourced a large language model. I've always believed that in the end, the LLM race won't be won by whoever is smarter, but by whoever holds data that others can't copy. Math and code? Anyone can scrape those. But "how to plan a wedding," "how to organize a trip," "how to open a shop without stepping on every landmine"—that kind of knowledge? Only Xiaohongshu has it. What's interesting is its technical choice. A sibling model in the same series just scored a perfect 42/42 at IMO 2026—a feat achieved by only 7 human contestants worldwide. Yet this open-sourced version didn't chase static benchmark scores. Instead, it aims directly at long-horizon life agents: a 512K context window, 16B active parameters, and a method called TEMPO that specifically tackles the RL training challenge of tasks spanning tens of hours. While everyone else is racing to top the exams, Xiaohongshu went ahead and raised an AI that actually knows how to live.

dots studio@dotsstudioai

2026.08.14 02:00

Introducing dots3-note preview — a small but mighty step toward long-horizon agency in real life. 🔹 280B MoE with 16B active parameters, a 512K context window, and multimodal understanding across text, vision, and audio 🔹 Introduces TEMPO, a new RL approach for long-horizon agent training through self-critiquing and test-time-scaled value estimation 🔹 Built to reason, explore unfamiliar environments, update memory over time, and combine multimodal perception with coding and tool use to solve complex tasks 🔹 Open weights on Hugging Face, alongside two open benchmarks for real-life agents: VibeSearchBench and VibeLifeBench Competitive with much larger models across reasoning, agentic, and multimodal evaluations. 🔗 Tech blog: 🔗 Model weights: 🔗 Github:

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