10🦊 @bblocklocklock
#fuzzyfox Joined July 2012-
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投了deepseek的简历,他真敢面我真敢去,弄一个史诗级客户端出来。😂
9B 的模型,把比它大好几倍的模型干下去了。 Ai2 和华盛顿大学那拨做 OLMo / Tülu 的人,放出一篇新论文叫 Tmax,自称是目前最强的开源「终端 agent」RL 训练配方。 成绩:一个 9B 模型在 Terminal-Bench 2.0 上拿到 27%,超过此前一批参数大得多的模型。配方本身简单到反常——纯用结果做奖励(outcome-only),不上花哨的过程监督。 最有意思的是,胜负手根本不在模型大小,也不在 RL 算法,而在「怎么造训练数据」。 他们用一套 taxonomy 批量造终端环境:难度控制 + 人设(personas)+ verifier 多样化,廉价生成海量可训练任务,攒出的终端 agent 数据集比之前公开过的最大那个还大 2.5 倍。 这说明一件事:终端 agent 的能力越来越是「环境喂出来的」,不是「参数堆出来的」。谁能便宜地造出大量可验证的任务,谁就能训出强 agent。 而且数据、模型、代码全开源了。 开源追前沿,这次追得很近。
Introducing autoresearch for GitHub repos Change 'Github' to 'ARGithub' in any repo URL Research artifacts extend beyond papers. Autoresearch is especially useful for experimenting on existing codebases that move fast and outpace their own publications. With one URL change you can now deploy an agent to orient itself on the codebase, resolve setup issues, and iterate on experiments.
llm_wiki v0.5 版本更新发布🎉🎉🎉 本次更新是一个大的功能更新,llm_wiki 正式从 RAG 形式修改为 Agent 形式运行 现在的 chat 对话,背后流程是一个完善的 agent 智能体处理流程,以知识库为基础,引入模块化的 tools 调用,以前每次对话都会进行 RAG 召回,现在改为 AI 智能判断,是否检索知识库、还是调用 Web 搜索 同时优化了界面显示,未来将会不断引入 skills 以及更多内置 tools 能力,让 llm_wiki 更加强大更加智能。 同时 star 数也来到了 12.6k ,感谢大家支持 项目地址:github.com/nashsu/llm_wiki
Google 当年用搜索引擎把人类写的内容变成全人类可查的知识库,而这篇论文问:当 AI Agent 产生的实验结果、推理轨迹、解决方案越来越多,我们有没有对应的「Agent 版搜索引擎」? Multi-Agent Transactive Memory 提出的基础设施,就是为了回答这个问题:让不同 Agent 的输出(实验结果、推理步骤、中间产物)被系统性地索引,供其他 Agent 在未来任务中检索复用,而不是每次都从零开始推理。 核心设计类比人类的「分布式记忆协作」(团队里每个人记住自己专长领域,需要时相互调取):Agent 群体中,每个 Agent 负责特定任务,产出的知识存入共享索引,下次不同 Agent 面对相关任务,直接从这个索引里取。这比 RAG(检索人类文档)更进一步,检索的是 Agent 同类产出的经验。 我觉得「为 Agent 群体建索引」这个视角最有启发性。RAG 是给 Agent 投喂人类知识,而 Multi-Agent Transactive Memory 是让 Agent 之间形成知识流通网络。随着 Agent 群体变大,这个基础设施的价值会指数级增长。第一个 Agent 解决了的问题,后面所有 Agent 都不用再重复解决。 未来真正值钱的,可能不是单个超级 Agent,而是能把整个 Agent 群体经验共享起来的知识基础设施。 arxiv.org/pdf/2606.19911
OCR but with a working memory...?! This paper, Unlimited OCR, replaces decoder full self-attention with Reference Sliding Window Attention, giving the model a working memory where each token attends to the fixed visual and prompt references plus only the most recent output tokens, making decode KV cache constant instead of linear in generation length. Combined with DeepEncoder’s 16x visual compression, this working-memory-style attention enables one-shot multi-page OCR with stable memory and latency, while improving OmniDocBench performance over DeepSeek OCR.
多和其他团队聊聊你就会发现你憋了很久的技术产品已经很牛逼了,大胆宣传推广吧
发现一个现象:一旦把能力强的员工和能力差的员工长期放在一起,最后先走的一定是能力强的那个 经验解读 我不知道大家懂不懂,高手是很怕被钝化的。 跟同级别的高手过招,一个眼神、一个逻辑的抛出,直接切中要害,非常丝滑的爽感。 但是你让牛人去带混子,好家伙,一个复杂的架构问题能把脑子里的高维逻辑全部碾碎嚼烂了一口一口喂给他,主要是喂完他还不一定能听懂。 最后硬生生被逼成了给小学生辅导算术题的暴躁家长,一天下来,正经活没干多少,全在解释为什么。
高级感拉满!太丝滑了!! 我怎么现在才发现! 以防有人不知道,再分享一下! animejs,能给你的网站或视频添加炫酷动画! 而且,直接丢进ClaudeCode或者Codex就能用!! CSS、SVG、数值变化、时间轴、交错动画、拖拽交互,各种动画真的是应有尽有。
以前我们不理解,为什么苏联会落入大炼科技的陷阱。现在轮到中国,就一目了然了。 哪怕是在推特上,依然有无数人会抱着只要电动汽车出口一切都会好起来,只要中国大模型成为SOTA就会好起来的态度,忽视民生,对社零消费已经转负的历史性节点视而不见。
我认为这是学术圈的Cursor时刻。 斯坦福 REAP 团队出手,CoPaper.AI 正在批量终结传统实证论文的体力劳动。 链接:copaper.ai/landing 如果说之前用大模型写论文只是帮你润色和编参考文献,那斯坦福罗斯高教授团队搞的这个 Project算是在社科和经济学实证圈扔下了一颗核弹。 大道至简,重剑无锋。它的功能很干脆。 喂进去原始数据集,30分钟内,就可以创造出包含全套 Stata/R 代码和 publication-quality 图表的完整 DOCX 论文。 它把 EDA、变量定义、计量模型构建(从 OLS 到高级的 DID、断点回归、因果森林)全部用 Agent 链串了起来。 跑出的每一张图表,底层都附带了 100% 可重复的 Stata、R、EViews 源码。这会砸掉了多少劣质论文代写和数据民工的饭碗? 数据民工、论文代写集体失业正在倒计时。 因为此后,对于社科类论文,AI 负责了所有的熵增拉扯,人类只需要负责定义问题。
Google ha acabado con la mafia de las GPU 💀 VS Code ahora se conecta directamente a Google Colab. → Obtienes una GPU T4 gratuita dentro de tu editor. → Tus archivos locales. Su potencia de cómputo.
İşte internette ücretsiz olarak bulabileceğiniz 9 mentor: 1. Naval Ravikant (Zenginlik) 2. Jordan Peterson (Sorumluluk) 3. David Goggins (Dayanıklılık) 4. Andrew Huberman (Sağlık) 5. Chris Williamson (Bilgelik) 6. Jocko Willink (Liderlik) 7. Nassim Taleb (Olasılık) 8. Robert Greene (Strateji) 9. Kendi Başarısızlıklarınız (Deneyim)
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