Kun (Kevin) SUN @Sharp_K_Sun
Tenured Full Professor @Tongji University, Professorial Research Fellow @ Tuebingen University , and interested in LLMs, NLP, and computational cognition . kevsuncl.github.io Tübingen, Baden-Württemberg Joined December 2016-
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Free & Open Source Shadcn Components
NVIDIA 丟了一個只有 0.6B 的語音辨識模型。 叫 Nemotron-3.5-ASR。 支援 40 種以上語言,即時串流輸出。 純 CPU 就能跑,不需要 GPU。 速度是官方 Nemo runtime 的 2.5 倍,辨識結果卻完全一致。 離線環境直接用,還能無縫整進你的 agent pipeline。 語音這塊,本地 agent 又多了一個又小又快的選擇。
🚨 我靠,大消息! 那个前OpenAI研究员阿申布伦纳,一年前投了闪迪 1300 万美金 14 个月,暴涨 7500%!市值直接干到 3120 亿! 他现在手里的股票,值 11 亿美金!! 这特么是史上最牛的一笔投资!!
1992年录像,洛杉矶暴动中韩国人暴打美国佬
关于Codex Computer Use的神奇用法,大家对它的挖掘程度绝对不到百分之一。。。 我冰山一角的用法: 1.自动开发了一款左键选中谷歌文档英文单词/句子,自动把单词或句子发送到Gemini插件进行翻译的浏览器插件,并且完成chrome的端到端测试。 2.打开Obsidian,开发md文章到公众号和X的插件,并且自动完成端到端测试。 3.打开华为开发者平台和阿里云,自动完成备案和审核,只有需要购买服务器和域名的时候,我才动动手。 4.在我登录好的法信、北大法宝等网站,按照需求,自动完成检索,生成法律检索报告word,保存到桌面。 5.帮我打开karing,自动帮我配置网络,解决了Dmit不能登录grok.com的问题。 6.帮我把访达里面的APP安装包自动上传到百度网盘和夸克网盘对应的文件位置。 7.帮我检索国产十大动漫电影精彩片段,并且从浏览器下载到了本地。 8.帮我完成了Codex App教程里面的截图任务,并且自动完成了敏感信息打码、重要信息特别标注的额外任务。 9.帮我打开各种软件的官网,直接下载我在工作过程中所需要用到的工具。 10.帮我打开剪映尝试导入素材并且剪辑。这一步我试过,成功了,但是会比较耗费Token。。。 11.帮我从零到一上架了第一款表情包,根据网站的指示,所有的素材全部调用Codex内置的GPT2-image生成,然后自动从访达上传。 ...... 还有些失败的:部分网址computer use被禁用了,比如微信公众号、拼多多后台和很多电商后台,这个时候,就可以试试playwright MCP,包C的朋友!!!
人生第一款App完成了网站、App备案,终于到最后一步华为审核了。全程都使用Codex完成信息填写、备案,我坐在一边刷短视频的感觉太爽了啊! 这款App已经有上百位付费用户支持,但是作为我第一款上架到应用市场的App,如果成功,就将免费开放给大家。
兄弟们,一个挪威神经科学家花了整整20年,就为了证明一件事: 手写笔记和打字,在大脑里是完全不同的两件事。 她叫Audrey van der Meer,在Trondheim经营脑科学研究实验室。 2024年她在Frontiers in Psychology上发的那篇论文,直接把争议画上了句号。 实验很简单:36名大学生戴上256通道EEG帽。 屏幕上闪出一个单词。 一半人用数字笔在触屏上手写,另一半直接在键盘上打字。 结果惊人: 手写时,大脑全亮。 记忆区、感觉整合区、新信息编码区全部同步激活,像整个皮层网络同时醒过来。 打字时呢? 大部分脑区瞬间安静,刚才那些连接几乎全消失。 同一个单词、同一个大脑、同一个人,两套完全不同的神经事件。 原因藏在微动作里:手写每一个字母,都是上千个微小动作和眼睛实时配合的空间问题。 手指、手腕、视觉、空间定位全在协同工作。 打字呢? 每按一个键都是完全相同的动作,大脑几乎不需要整合,也没什么问题要解决。 十年前普林斯顿的另一组研究用完全不同的方法也得到了相同结论:手写笔记的学生在真正理解题上完胜,打字组只记住了表面内容。 因为手写逼你必须听、必须选、必须用自己的话重新组织。 打字让你像复印机一样狂敲,却几乎没经过大脑加工。 我看完后最大的感受是: 我们以为打字是效率,手写是老派。 其实手写才是真正让知识扎根
A Norwegian neuroscientist spent 20 years proving that the act of writing by hand changes the human brain in ways typing physically cannot, and almost nobody outside her field has read the paper. Her name is Audrey van der Meer. She runs a brain research lab in Trondheim, and
CPU vs GPU vs TPU vs NPU vs LPU, explained visually: 5 hardware architectures power AI today. Each one makes a fundamentally different tradeoff between flexibility, parallelism, and memory access. > CPU It is built for general-purpose computing. A few powerful cores handle complex logic, branching, and system-level tasks. It has deep cache hierarchies and off-chip main memory (DRAM). It's great for operating systems, databases, and decision-heavy code, but not that great for repetitive math like matrix multiplications. > GPU Instead of a few powerful cores, GPUs spread work across thousands of smaller cores that all execute the same instruction on different data. This is why GPUs dominate AI training. The parallelism maps directly to the kind of math neural networks need. > TPU They go one step further with specialization. The core compute unit is a grid of multiply-accumulate (MAC) units where data flows through in a wave pattern. Weights enter from one side, activations from the other, and partial results propagate without going back to memory each time. The entire execution is compiler-controlled, not hardware-scheduled. Google designed TPUs specifically for neural network workloads. > NPU This is an edge-optimized variant. The architecture is built around a Neural Compute Engine packed with MAC arrays and on-chip SRAM, but instead of high-bandwidth memory (HBM), NPUs use low-power system memory. The design goal is to run inference at single-digit watt power budgets, like smartphones, wearables, and IoT devices. Apple Neural Engine and Intel's NPU follow this pattern. > LPU (Language Processing Unit) This is the newest entrant, by Groq. The architecture removes off-chip memory from the critical path entirely. All weight storage lives in on-chip SRAM. Execution is fully deterministic and compiler-scheduled, which means zero cache misses and zero runtime scheduling overhead. The tradeoff is that it provides limited memory per chip, which means you need hundreds of chips linked together to serve a single large model. But the latency advantage is real. AI compute has evolved from general-purpose flexibility (CPU) to extreme specialization (LPU). Each step trades some level of generality for efficiency. The visual below maps the internal architecture of all five side by side. 👉 Over to you: Which of these 5 have you actually worked with or deployed on?
Stanford 这堂 2 小时 AI 系统构建课,直接把 Claude 所有教程和 Prompting Thread 秒了! 强烈推荐: “比你刷过的所有 Claude 教程都实用 10 倍” 里面讲的不是 prompt 技巧,而是 Stanford 真正教工程师如何从零构建可靠 AI 系统的完整方法论。 周末就刷这一个,绝对是你这周最有生产力的事! 我已经将其翻译为中英 文双语视频直接戳这里👇
微软,总算出了个好东西。 开发了一款工具, 可以将几乎任何格式pdfs,word,docs,excel,PowerPoint,Audio,YouTube 网址 转换为Markdown文件, 众所周知Markdown 是AI 大语言模型最青睐的格式。 没有自定义解析器。没有损坏的布局。没有混乱的文本。 只是干净、结构化的标记, 只需一次安装 github.com/microsoft/mark…
10.1017/langcog.2026.10072
GitHub上10个最有价值的AI学习资源库 大多数AI学习者浪费数月在随机教程上。 下面是 10 个最有价值的 AI 学习仓库: 1️⃣ Generative AI for Beginners(⭐105k) github.com/microsoft/gene… 微软出的系统课程, 教你 从零构建真实的 GenAI 应用。 2️⃣ LLMs from Scratch(⭐83k) github.com/rasbt/LLMs-fro… 一步一步 自己实现 GPT 类模型。 想真正理解 LLM,这是必看项目。 3️⃣ AI Agents for Beginners(⭐49k) github.com/microsoft/ai-a… 系统学习 AI Agent 的核心能力: 工具调用、记忆、规划、工作流。 4️⃣ ML for Beginners(⭐83k) github.com/microsoft/ML-F… 26 节课的 机器学习基础完整课程。 非常适合 ML 入门。 5️⃣ OpenAI Cookbook(⭐71k) github.com/openai/openai-… 大量 生产级 API 示例, 教你如何真正把 AI 用到产品里。 6️⃣ Python 100 Days(⭐177k) github.com/jackfrued/Pyth… 非常扎实的 Python 基础训练营。 想做 AI,这个基础很重要。 7️⃣ LLM App Templates(⭐54k) github.com/pathwaycom/llm… 提供 可部署的 LLM 应用模板, 包含完整的 RAG 流程示例。 8️⃣ Python Data Science Handbook(⭐46k) github.com/jakevdp/Python… 经典数据科学教程: NumPy、Pandas、Scikit-Learn。 9️⃣ Stable Diffusion(⭐72k) github.com/CompVis/stable… 著名 扩散模型的原始代码仓库。 想理解 AI 生成图像,这个必须看。 🔟 Segment Anything(⭐53k) github.com/facebookresear… Meta 发布的 通用图像分割模型 SAM。 视觉领域里非常重要的项目。 如果你真的想 系统学 AI, 这 10 个仓库基本能覆盖: Python → ML → LLM → Agent → GenAI → CV。
程序员圈估计要炸了, 有人把全网从零开始造轮子教程都汇总到了一起, 这个叫自建XX的项目,现在已经是GitHub星标最高的仓库了, 46.6万颗星,超过React,超过TensorFlow,比所有热门工具库的星标都多。 它本身不是什么代码库,就是一套教程合集,一步步教你怎么从零开始,把你天天用的技术亲手再做一遍。 费曼说过一句话:凡我不能亲手创造的,我便无法真正理解。 这就是它为什么火, 喵个咪,内容真的吓人: 从从零克隆Redis,到500行C写Docker,自己实现Git,不调框架写神经网络,C语言写操作系统,从解释器开始做编程语言,逐像素做光线追踪渲染器...还有Web服务器、浏览器、搜索引擎、区块链、BT客户端,什么都有🥹 每个项目都有多语言版本,主流语言基本覆盖了, 很多人没看懂,但这真不是给新手找活干的教程, 可以说是软件工程师最高阶的学习材料了, 当你从零重建一遍系统,你就得直面当年原作者做的每一个技术决策——没有依赖库可以躲,没有封装好的接口可以调用,错了就是错了,跑不起来就是跑不起来,无处可逃😂 现在AI生成代码越来越方便了,但这种知识反而越来越值钱,AI能帮你调库写接口,但它替不了真正懂系统本质的人。 46.6万人标星,4万多人复刻分支,所有内容全免费,不需要注册,不需要付费。 理解一件事最好的方式,就是亲手把它做出来, 100%免费,100%开源,真的牛逼, GitHub链接放评论区了 👇
OpenDataLoader PDF — 一个专门给 AI/RAG 场景做的本地 PDF 结构化抽取器 不是“PDF 转 Markdown”,而是它会重建标题、列表、表格、图片和阅读顺序。 输出直接给 JSON / Markdown / HTML,明显就是冲着 LLM、向量检索、RAG 去的。 github.com/opendataloader…
Google just solved a theoretical physics problem using Gemini! Google, Harvard, and CMU built a neuro-symbolic system using the Gemini Deep Think model and a tree-search framework to autonomously discover complex mathematical proofs. The agent functions like a digital scientist, testing different analytical paths and using numerical feedback to refine its logic until it finds a perfect solution. This approach successfully solved a major open problem regarding gravitational radiation from cosmic strings, outperforming previous AI attempts by delivering full analytical solutions where others only found partial approximations.
Ai2绝对是我见过最无私、开发的ai lab 不仅开源模型,还开源预训练、微调脚本、训练代码(infra codebase)、训练数据、数据处理代码等等 是入门ai research的宝藏
What's in the release: 🔹 Pretraining & fine-tuning scripts (SFT + long-context SFT) 🔹 Multi-node distributed training 🔹 Data download, preprocessing, & visualization utilities 🔹 Single-task & multi-eval scripts with caching Built for reproducibility & new experiments.
一个宝藏理科学习网站:PhET 免费的教育网站,支持中文,提供上百个覆盖物理、化学、生物、地球科学和数学的互动模拟实验,无需任何设备和费用,直接在浏览器里通过互动的小游戏,利用拖拽、滑块等方式操控实验参数、实时观察结果,把抽象的科学概念变得可触摸、可探索。 phet.colorado.edu
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Nancy K @karaoglu_asli
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Laurens van der Maate... @lvdmaaten
4K Followers 2K Following Member of Technical Staff at Anthropic. Ex-Meta. t-SNE. Llama 3. DenseNet. Web-scale weakly supervised vision. CrypTen.
Shuran Song @SongShuran
14K Followers 546 Following Assistant Professor @Stanford University working on #Robotics #AI #ComputerVision
Xiaolong Wang @xiaolonw
22K Followers 1K Following Research Director, @Meta Superintelligence Labs Co-founder of ARI Associate Professor @UCSDJacobs Postdoc @berkeley_ai PhD @CMU_Robotics
Angjoo Kanazawa @akanazawa
21K Followers 624 Following Assistant Professor at @Berkeley_EECS, @berkeley_ai. KAIR, @nerfstudioteam. Amazon Scholar @ FAR. Previously advised @WonderDynamics and @LumaLabsAI. she/her.
Maarten Grootendorst @MaartenGr
9K Followers 38 Following 🧑💻 AI @GoogleDeepMind 📖 Author of "Hands-On LLMs" (https://t.co/BcSDNMOnWq) 🧙♂️ Open Sourcerer (BERTopic, PolyFuzz, KeyBERT) 💡 Demystifying AI
Yuke Zhu @yukez
22K Followers 475 Following Associate Professor @UTCompSci | Director @NVIDIAAI Co-Leading GEAR | CS PhD @Stanford | Building generalist robot autonomy in the wild | Opinions are my own
Jun-Yan Zhu @junyanz89
13K Followers 682 Following Assistant Professor at Generative Intelligence Lab @CMU_Robotics @CarnegieMellon. Understanding and creating pixels.







































