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关注数据科学 关注科技行业 关注人工智能 关注一切促进人类生活美好的新技术 业界主流大模型列表:https://nitter.cf/t.co/H4FUDd7Gfb 国产开源大模型生态现状:https://nitter.cf/t.co/q5KU9WhPuE

Hefei, China
Joined March 2023
Photo by DataLearner (@DataLearnerAI)Photo by DataLearner (@DataLearnerAI)Photo by DataLearner (@DataLearnerAI)Photo by DataLearner (@DataLearnerAI)Photo by DataLearner (@DataLearnerAI)Photo by DataLearner (@DataLearnerAI)Photo by DataLearner (@DataLearnerAI)Photo by DataLearner (@DataLearnerAI)Photo by DataLearner (@DataLearnerAI)Photo by DataLearner (@DataLearnerAI)
DataLearner retweeted
software engineers are going to have to tackle a new set of problems to unlock powerful multiagent systems to solve the world's hardest problems - right now is one of the most interesting moments in history to be an engineer.
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DataLearner retweeted
In Codex / Claude Code we can talk to the agent while it’s still working in 4 ways: 1) Steer: change what it does next 2) Queue: add work for later 3) BTW: ask something without interrupting 4) Interrupt: stop it Looks like a small UX feature, but changes the architecture.
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DataLearner retweeted
Who says it won't reset in a while 👀
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DataLearner retweeted
🙇‍♂️ 辛苦大佬们帮忙扩散给自己的私域圈
🤠 Kimi Code也在招人,感兴趣直接发我邮箱 [email protected] 感谢大佬们帮忙扩散 捧场
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DataLearner retweeted
早上,我让 Codex 从昨天开始执行的“goal”整整跑了 21 小时 14 分钟,共消耗 9 亿 Token,终于进入了“Goal archived”状态。期间完全没有任何人工干预,它就是不停解决问题、验证结果…不断循环这个过程。 大家可能会好奇我用 Codex 做的什么任务,简单地说,我让它解决的是前后端数据字段的大重构问题。随着产品的不断迭代,早期的一些设计不再适应,需要对数据库建模(model)进行一些重构以适应未来发展。 我的产品后端 PostgreSQL,前端 SwiftData,加上自己实现的同步机制(local-first)。早期 model 还少时,重构会简单的多,现在 model 很多,关系又错综复杂,除非停止迭代,重构如果不一次性完成,任务非常繁杂。但好处是,只要定好标准和方向,重构又是相对容易验收的。 于是我想到了最近 Codex(命令行版本)推出的“/goal”功能(也就是 Codex 版本的“raphl loop”)。通过简单的提示词,指向一份文档让它去实现,就这样它连续跑了 21 小时。 最后验收的效果还不错,除了一些 App 端的小问题(这是我原本就测试没覆盖到的地方,没工作也正常),基本很满意。有了这次经历,我对 Codex(或其他 AI 产品)去解决大重构等长任务越来越有信心了。为了防止 AI 写的代码越来越屎山,也必须需要周期性的去重构、清理架构和代码。
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DataLearner retweeted
btw just fyi: new models releases from openai are normally on Tuesdays or Thursdays
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DataLearner retweeted
🥹 再打扰一下 bro 们
😢😢😢 打扰一下兄弟们,再发一次 月之暗面(kimi) 的招聘,急急急 感兴趣邮箱: [email protected]
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DataLearner retweeted
🚀 Zhipu AI just open-sourced GLM-4.1V-9B-Thinking—a 9B-parameter multimodal model that matches 72B-sized rivals & even beats GPT-4o in some tasks! 💡 **MIT-licensed** → **Free for commercial use!** huggingface.co/THUDM/GLM-4.1…
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DataLearner retweeted
Huge drop from Baidu From 0.3B to 424B Multiple checkpoints (Pretrained, Post-trained, and base models) @kalomaze Competitive with Qwen3 and Deepseek V3 0324 Trained on Kunlun III chips using Paddle Paddle framework (Baidu's CUDA)
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Since it's summer, and more or less internship and tech interview season, I made all 30 chapters of my Machine Learning Q and AI book freely available for the summer: sebastianraschka.com/books/m… Hope it’s helpful! Happy reading, and good luck if you are interviewing!
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The race for LLM "cognitive core" - a few billion param model that maximally sacrifices encyclopedic knowledge for capability. It lives always-on and by default on every computer as the kernel of LLM personal computing. Its features are slowly crystalizing: - Natively multimodal text/vision/audio at both input and output. - Matryoshka-style architecture allowing a dial of capability up and down at test time. - Reasoning, also with a dial. (system 2) - Aggressively tool-using. - On-device finetuning LoRA slots for test-time training, personalization and customization. - Delegates and double checks just the right parts with the oracles in the cloud if internet is available. It doesn't know that William the Conqueror's reign ended in September 9 1087, but it vaguely recognizes the name and can look up the date. It can't recite the SHA-256 of empty string as e3b0c442..., but it can calculate it quickly should you really want it. What LLM personal computing lacks in broad world knowledge and top tier problem-solving capability it will make up in super low interaction latency (especially as multimodal matures), direct / private access to data and state, offline continuity, sovereignty ("not your weights not your brain"). i.e. many of the same reasons we like, use and buy personal computers instead of having thin clients access a cloud via remote desktop or so.
I’m so excited to announce Gemma 3n is here! 🎉 🔊Multimodal (text/audio/image/video) understanding 🤯Runs with as little as 2GB of RAM 🏆First model under 10B with @lmarena_ai score of 1300+ Available now on @huggingface, @kaggle, llama.cpp, ai.dev, and more
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Yet another rewarding week in Open Source AI: 1. Google dropped Gemma 27B & 9B - The best open (commercially permissive) LLM out there according to LYMSYS. 2. Mars5 TTS - Text to Speech with insane prosodies control & voice cloning. 3. Meta shipped LLM Compiler - beats GPT 4 on code optimisation and compiler reasoning. 4. Arcee-Spark - Qwen2 7B (w/ merging) fine-tuned further to beat GPT 3.5 on MT Bench. 5. Gemini Nano out in the wild in Chrome - On device LLM with just 2 lines of code (fully offline) 6. Fal released a fully Open Source GAN based Super-Resolution model (with second version already cooking) 7. NYU release Cambrian 1 - Vision Multimodal LLM that beats pretty much all other closed source competition 8-34B model size And.. much more like Open LLM Leaderboard got a major update, LYMSYS released Chat Vision Arena, OpenAI released a paper on CriticGPT! What a lovely week, can’t wait for the next to see what the community is up to! Put it down in comments if I missed something 🔥
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DataLearner retweeted
Replying to @philpax_
already on azure marketplace
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DataLearner retweeted
🎉Dify 进入新篇章!🚀AI Workflow 同时登录云服务和开源社区版。现在,你可以基于众多模态模型构建复杂的 LLM 应用了。 在此次更新了超过 100,000 行代码的重大版本中,我们给全球开发者带来了: 1️⃣“这很 Dify”的全新 LLM 流程编排,我们打造了迄今为止最佳的应用开发体验。你可以基于 Dify 构建 Agentic Workflow,以独立应用或 API 的方式交互。 2️⃣在节点上进行可视化调试,所见即所得的进行 Workflow 编排,便于迅速定位问题和得到正确的输出预期。 3️⃣可插拔的 DSL,完全声明式的 Workflow 定义使你的应用编排可以自由地在开源社区或团队中分享。 4️⃣原生的代码运行时,支持 Python 和 JS 代码作为节点编排到应用步骤中。 5️⃣由原厂和社区开发者提供的超过几十种 Tools,可双向兼容 Workflow 和 Agent 应用。当然…你可以接上你自己的。 Dify 作为 LLM 中间件的“创新队长”,以体验、工程、开源为社区持续注入活力。🌟凭借我们在多模型、多模态、Agent 和 RAG 上的工程积累,已经成为众多 500 强企业和创新者的坚定选择📈。 身为品类标杆,Dify 有持续定义新标准的责任。期待与你们一同探索 GenAI 的能力边界🚀! 👉去 GitHub 中了解 Dify v0.6 github.com/langgenius/dify 👉访问 Dify 云服务 dify.ai #Dify #LLM #LLMOps #Agnet
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DataLearner retweeted
Claude 3 Opus + ChainForge,明显提升了 Prompt 编写效率。工作流: 1. 手动编写测试用例种子和原始 Prompt。 2. 让 Claude 3 根据 Prompt和种子,生成大量测试用例。 3. 在 ChainForge 上构建 workflow,尤其是测试。 4. 让 Claude 3 迭代 Prompt。 5. 不断的测试,拿到最好的效果。
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DataLearner retweeted
龙年祝大家生活在讲道理比讲主义更管用的世界里。遇到的每一份辛苦,都是自找的,不是人施的、天降的。
祝各位兔年有信心。满怀信心置业生娃,充满信心招工扩产,抱有信心走遍世界。
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DataLearner retweeted
Replying to @dotey
logprobs最直接的用处是计算模型当前输出的困惑度。简单的说就是模型输出的token概率越高,证明模型越确认,困惑度越低。 如果输出的token概率比较低,证明模型自己也比较困惑,此时可能出现幻觉的概率比较大。 你可以让模型输出N个回答,然后选其中困惑度最低为正确答案。
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重大新闻!我们的TypeScript文档现已提供15种语言版本✨,包括中文、法语和西班牙语!Wuhu! 我们不仅仅翻译了文档,我们还创建了一个全新的、由人工智能驱动的自动翻译文档工具。现在隆重介绍我们的自动翻译文档工具! 这个工具并不是一个快速且粗糙的hack工具: ✅ 文档经过翻译,然后由 LLM 严格检查,以确保长度、标题一致性、链接保留等。 ✅ 代码块被保留 ✅ 翻译已被缓存,因此当你更新文档时,只会重新翻译你更改的部分 请在我们的博客文章中了解更多有关自动翻译文档工具的信息:blog.llamaindex.ai/bridging-… 或直接前往我们全新的多语言文档:ts.llamaindex.ai/zh-Hans/ 我们的自动翻译文档工具包:npmjs.com/package/autotransl… 对于这整个想法和所有的工作,全部归功于@hexapode!
Big news! Our TypeScript docs are now available in ✨15 languages✨ including Chinese, French and Spanish! Wow! But we didn’t just translate our documentation, we built a whole new, AI-powered tool to automatically translate documentation: introducing autotranslatedoc! This isn’t some quick-and-dirty hack: ✅ The docs are translated and then rigorously checked by the LLM to ensure length, title consistency, link preservation and more. ✅ Code blocks are preserved ✅ Translations are cached, so when you update docs only your changes are re-translated Learn more about autotranslatedoc in our blog post: blog.llamaindex.ai/bridging-… Or head straight to our new, multi-lingual docs: ts.llamaindex.ai/ Autotranslatedoc package: npmjs.com/package/autotransl… Full credit to @hexapode for this whole idea and all the work on it!
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DataLearner retweeted
We are going to build something new & it will be incredible. Initial leadership (more soon): @merettm @sidorszymon @aleks_madry @sama @gdb The mission continues.
Replying to @sama
I’m super excited to have you join as CEO of this new group, Sam, setting a new pace for innovation. We’ve learned a lot over the years about how to give founders and innovators space to build independent identities and cultures within Microsoft, including GitHub, Mojang Studios, and LinkedIn, and I’m looking forward to having you do the same.
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DataLearner retweeted
i loved my time at openai. it was transformative for me personally, and hopefully the world a little bit. most of all i loved working with such talented people. will have more to say about what’s next later. 🫡
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