hhggfs

I want a RT game while I’m enjoying my evening cozy on my sofa🤭🤭

@YoLaNaTie 擦边才是大家喜闻乐见的艺术🤓

学了十年舞蹈，都说是艺术，我用来擦边。这难道就是许嵩说的雅俗共赏？ #舞蹈服 #白丝 #舞蹈袜 #大长腿

@Xiaoniu6161 傻站着干啥 赶快跑啊🤣🤣🤣🤣

近日，有网友爆料，华南理工大学发生一起恶劣校园偷拍事件。校内一名蒋姓男学生，在上课期间偷拍后排女生裙底，行为持续长达半个多小时，被受害女生当场察觉。

Recent thoughts: The Shift to Long-Horizon Tasks The most likely breakthrough this year will be in long-horizon tasks. We are moving toward a stage where Large Language Models (LLMs) learn to complete extended, complex missions by interacting with Agent environments. This is perhaps where the true value of LLMs lies. Take cybersecurity as an example: imagine a model that continuously hunts for software bugs and vulnerabilities. While it sounds like a search process, it’s actually the model learning the high-level intuition and methodology of a professional hacker. Unlike humans, AI can run 24/7 without fatigue. It could potentially find exploits at a much higher frequwill ency and claim bounties on platforms like HackerOne or BugCrowd. It sounds fun, but fundamentally, it's a revolution that displaces the hacker. If even hackers are being "disrupted," one can only imagine the impact on general programmers. From One-Person to None-Person Companies Building on long-horizon capabilities, Autonomous Agent Systems (AAS) will inevitably become the next frontier. Last year, we were discussing the rise of the "One Person Company" (OPC). I didn't expect us to move so quickly toward the "None Person Company" (NPC). It’s an ironic twist—we might all end up as NPCs in this new ecosystem. Engineering the Impossible: Memory and Learning To realize the vision above, we must solve three technical pillars: Memory, Continual Learning, and Self-Judging. I used to think these would require massive paradigm shifts and years of research. However, the pressure from both the technical and application sides is so intense that we are seeing these capabilities emerge through ingenious engineering "tricks": Memory: Long context windows (1M+) and RAG have significantly bridged the gap. Continual Learning: While true continual learning remains difficult, the release cycles are shrinking. Global models are updated monthly; domestic models are catching up. If we reach weekly updates by next year, it will effectively function as continual learning. Self-Judging: This remains the most elusive, yet models like Opus 4.7 are already demonstrating early self-correction and judgment capabilities. The Self-Evolving Endgame The most difficult—and most promising—path is Self-Evolution. The current wave is incredibly fierce. I suspect that models like Claude may have already achieved a baseline for self-training: writing their own code, cleaning their own data, generating synthetic data, and then training on it. It might "waste" some compute, but it saves the most precious resources: human labor and time. In the LLM era, speed is everything. Rapid iteration is what creates the cognitive gap between leaders and followers. Claude’s rumored 2-million-chip cluster for next year is likely dedicated to exactly this: autonomous model self-training. Technical Summary: 1M Context: Necessary baseline. Memory & Continual Learning: Prerequisites, likely solved first via "tricky" engineering. Harnessing Environments: The breakthrough point. Self-Judging: The tipping point. Full Self-Training: The endgame. Redefining AGI and the Industry If this is the road to AGI, then AGI’s definition should be the sum of all human collective intelligence, not just an individual’s intelligence. It must possess the creative capacity to produce something as profound as the "Theory of Relativity"—meeting the bar set by Hassabis. During this transition, every APP will need to be reconstructed as AI-native. In fact, we might move past the concept of APPs entirely. The most significant challenge will be the reconstruction of the operating system itself. In the future, you won’t see a traditional desktop; you will see an LLM OS, where applications are "generated on demand." This challenges the 80-year-old Von Neumann architecture and represents a total upheaval of the computer science industry. The Irreversible Wave From completing long-horizon tasks to fully autonomous operations, every sector—Security, Finance, Law, E-commerce—will be reshaped. Many friends have reached out lately, asking how to transform their enterprises to keep pace with AI. But few truly realize that this irreversible process has already begun. As this massive technical wave hits, we must be prepared to act, but we must also start thinking seriously about how to regulate it.

@BTC99M 想知道他怎么保养自己的

中国男网红李二狗的奢靡生活， 聚会汇集数百位网红美女。

@wanyiqijglvsss 这个脚好想上贡啊啊啊啊啊啊啊

#女s 还在南宁

@Slady8888 想看

买了新的瑜伽裤 短得堪比我的内裤 知道它短 没想到这么短

@kkmaster0 掩耳盗铃这一块儿

@sheriyuo You will like our arxiv.org/pdf/2510.18554?

BREAKING NEWS: You Can Steal DeepSeek Training Data By Questioning This <｜begin▁of▁sentence｜> <｜sft▁begin｜> <think> Remember to close Search #deepseek

Big leap in Object Detection with Qwen3.6-35B-A3B! 🚀We are excited to showcase the new "Instruction-Oriented Object Detection" capability on ModelScope. Demo 👉modelscope.ai/studios/Qwen/O… 📈 Performance: ODinW score jumped from 42.6 (Qwen3.5) to 50.8! 🧠 Beyond standard detection, Qwen3.6 leverages LLM reasoning to: 1️⃣ Identify fine-grained objects, such as PCB components and reference designators. 2️⃣ Detect small and occluded cars in aerial-view parking lots. 3️⃣ Handle dense scenes multi-scale objects. 🤖 Download model: modelscope.ai/models/Qwen/Qw… #Qwen36 #ObjectDetection #ComputerVision #OpenSource

Welcome to DS4, a specialized inference engine for DeepSeek v4 Flash. github.com/antirez/ds4 This project would have been impossible without the existence of llama.cpp and GGML and the work of @ggerganov and all the other contributors. Thanks!

外国女生难得清醒发声！直言部分来华外籍男士资质平平，却意外受到不少人追捧，这番真实吐槽太敢说了，引人深思。你觉得她说得有没有道理？

New blog post! Wrote about how SFT, RL, OPD relate to generalization and catastrophic forgetting :)

@xiaojiejie3832 长得本来就不咋好看

杨超越

@Slady8888 这个肉腿看得我想过门了

天热了 大部分就是肉色

从0训练一个0.1B的端到端全模态模型，一个权重搞定文字、语音、图片输入，输出文字和流式语音。 github.com/jingyaogong/mi… MiniMind-O 是一个只有0.1B参数的全模态模型，Thinker-Talker 双路径设计，支持文字/语音/图片输入，输出文字和流式语音。 这项目把代码、权重、训练数据和技术报告全部开源，核心算法用 PyTorch 从0写，一张3090两小时就能跑通 mini 数据集训练。

中国软件著作权申请材料 生成器 Skills meta.appinn.net/t/topic/85159

@DD8Xii 男的还做美甲呀，有点恶心

弟弟，想逃出我的黑丝脚吗？ 哼～如果你不交出来的话，那是不可能的～！ #黑丝 #美脚