全部 AI 动态

让不同平台的个人 agent 相互通讯的极简方法：电子邮件！Grok Bot 先行一步，你可以给自己的 Bot 配置一个邮箱 xxx@mail.grokbot.com 这种，例如我现在要让我的 Grok Bot 和我的 Muse Agent 之间相互发指令，可以拿 Gmail 当中介，设置一个 Gmail 的别名（xxx+muse@gmail.com）给 Muse 使用，然后通过邮箱和签名来验证，最后让你的 agent 信任这个邮件地址，发消息不需要通过你确认就可以了。 现在 Gmail 就是没法 push 消息，需要每 15 分钟检查一遍新邮件，所以说消息没法即时，其它都很方便！如果想用更强大即时的方案，可以用 @turingou 同学做的服务 http://yobiru.com 👀

dots tip: ask your dot to proactively look for areas that will improve quality of your life/ work and check in with you every x hours works wonders

I use a Grok bot called Fine Print to check any contract, lease, bill, etc and it breaks down what's actually in the paperwork. It looks for hidden fees, catches things I might miss, and can even write a dispute or alternate terms. Give it a try: https://x.ai/bot/Yetb1ei_7VvPfWlYqYwAM

Debating adding a GRILL.md file to customize how /grill-me works A few benefits: 1. /grill-with-docs gets removed, you just have 'invoke the domain-modeling skill' in your GRILL.md and use /grill-me 2. Customize what you care about/get asked about during planning (security, module strategy, etc) 3. Choose whether you want to be grilled one at a time or all together People seem really, really reticent to actually edit the skill themselves, so this seems sensible to me.

CS 329Z: Engineering AI Agents Stanford / Fall 2026 @stanfordnlp https://cs329z.stanford.edu/ 课程定位：从"模型"到"系统"的工程学 覆盖：简单 LLM 流水线 → 复合 AI 系统 → 自主 Agent。三位讲师的背景也高度互补： @Diyi_Yang（斯坦福 NLP 教授，人机交互与社会计算方向） @michaelryan207（DSPy 核心贡献者，自动评估 AutoMetrics 作者） @jyangballin（SWE-agent / SWE-bench / SWE-smith 作者，软件工程 Agent 领域最重要的研究者之一） # 课程主线：三大工程挑战 贯穿全课的三个核心问题——分解（decomposition）、数据（data）、评估（evaluation）。11 周的内容基本围绕这三条线展开，可以分为五个模块： 模块 1：构建基元（Week 2–3） · LLM 作为构建材料：API/SDK（litellm）、结构化输出、约束生成、解码策略、test-time compute、上下文工程、模型选型与成本/延迟权衡 · RAG：embedding、向量库、分块策略、混合检索、cross-encoder 与 ColBERT 后期交互 · 工具调用：函数调用 API、MCP（Model Context Protocol）、工具设计、代码沙箱、错误处理与重试 模块 2：框架与设计模式（Week 3–5） · 框架层：DSPy（signature / module / optimizer）、LangChain/LangGraph、LlamaIndex，重点是"框架抽象了什么 vs. 你手写了什么" · 设计模式：workflow vs. agent 的分类学，五种可组合 workflow 模式，ReAct / plan-and-execute / reflection，"scaffold（脚手架）本身就是设计决策" · 记忆架构：短期/长期记忆、记忆作为工具动作、文件系统作为外化记忆、跨 Agent 记忆（MemGPT、Mem0、Generative Agents） · 多 Agent 系统：编排模式、handoff 与状态传递，以及一个很有态度的对照阅读——既读 AutoGen，也读《Why Do Multi-Agent LLM Systems Fail?》和 Neubig 的《Don't Sleep on Single-agent Systems》 模块 3：优化（Week 5） · 从提示词到微调的全景：GEPA、MIPROv2、OPRO、TextGrad（提示优化）；LoRA/QLoRA、蒸馏、RLHF/DPO（权重优化）；test-time scaling（推理算力） · 核心问题是决策框架：什么时候优化 prompt、什么时候优化 weights、什么时候堆推理算力 模块 4：数据与评估（Week 6–8）——最有分量的部分 · 数据：trace、demonstration、feedback 三类数据；训练数据 vs. 评估数据；数据飞轮；合成数据；从 Agent 轨迹构建数据集（SWE-smith） · 评估基础：为什么 eval 难；4 元组框架（request / environment / stopping criteria / scorer）；好 benchmark 的性质；tinyBenchmarks · 评估基础设施：三类 grader、LLM-as-judge 的 prompt 设计与已知偏差、pairwise vs. pointwise、非确定性指标 pass@k vs. pass^k、harness 设计 模块 5：安全与前沿（Week 8–11） · 安全：工具访问的隐私风险、prompt injection（含间接注入）、红队、沙箱与权限模型、输出护栏、human-in-the-loop · Coding Agent：SWE-agent、Claude Code、OpenHands 的端到端架构对比 · 主动式 Agent：从 reactive 到 proactive，General User Models（GUM）、Next Action Prediction，以及"Agent 何时应主动、何时应等待"的 mixed-initiative 问题 · 开放问题：多模态/web/计算机使用 Agent、科学 Agent、长时运行架构、生产可观测性（tracing、monitoring、成本管理） # 作业设计：一手建、一手评 HW1：从零构建 Agent 系统（10%） 给定论文库，构建能检索并推理回答科学问题的 Agent。 · Part A：只用 litellm 手写 RAG + 工具调用 + ReAct 式 Agent 循环 · Part B：用 DSPy 重建关键组件，并反思框架抽象了什么 HW2：评估一个 Agent（10%） 给定一个预构建 Agent，设计完整评估套件：代码型 grader、至少一个 LLM-as-judge、用 4 元组框架构建 benchmark 任务、错误分析。

I just told bot literally "go do this for me" this was the result

here's how to make a cozy fishing game solo with Claude Opus 5.5 + Blender + 3D AI + Unity 6... the trick is giving every tool one job, but there's a smart way to set it up: - step 1: generate every 3D model with Tripo Smart Mesh P2: palms, rocks, the pier, the boat, the hut, the character - step 2: connect Claude Code to Blender through MCP - step 3: Opus 5.5 sets up each model in Blender: scale, pivots, materials, clean export - step 4: Opus animates in Blender too: the character's walk, the cast, the rod, the boat on the water - step 5: bring everything into Unity 6 and build the world: forest, river, waterfall, beach, pier - step 6: add the systems one at a time: walking, fishing, bait choice, a time of day - step 7: play it after every step and fix one thing at a time who does what: > Tripo: the 3D models > Opus 5.5 + Blender: setup and animation > Unity 6: the world and the game one person, one stack, one game you'd actually want to play

I'm a newcomer to @poteto's plist; But it seems to me that there is some very significant wisdom encoded in there. I suspect you could teach a master class on the principles.

if you sell AI training data, see how you compare: we found 159 companies selling data, RL envs and evals, every fact sourced plus 38 programs that pay companies for their data: http://whosellsdata.com 8 of the 14 that pay companies directly publish a dollar range. most of the rest set terms per deal. unknowns are marked and nothing is ranked the other page: what each program wants, who qualifies and what it offers: http://whosellsdata.com/buying missing something, or something wrong? fix it on the site, a person reviews every change we built this at @Secludy. if you buy, broker or license company data and need the names out before it reaches a lab, dm me

Our sandbox, SPACE, handles millions of executions a day with an error rate below 0.0002%. We’re sharing how it uses cryptographic identity to keep requests tied to the right execution as sandboxes pause, resume, and move. Read more: https://www.perplexity.ai/hub/blog/how-space-routes-requests-to-the-right-sandbox-execution

周末适合沉下心来系统性的学习基础知识 CMU 秋季最新课程 11-768: AI Agents 主讲：@dan_fried @gneubig 课程：https://www.cmu-agents.com/ 视频：https://www.youtube.com/watch?v=UwfjzyLnvMg&list=PLSN0qpDfUvTM

Made this 8-second beverage ad with AI. No shoot. No studio. Want to recreate it? The prompt is in the replies 👇 Just use this video as your reference and add an image of your own drink. Would you run this as a real ad? Try it in Professional Creation → https://growth.pixverse.ai #PixVerse #Commerical #MarketingStrategy #Creativeads

Hi, I'm Felix. I'm an eng lead working on Claude. I love watching people take flight and do things they never thought possible. We often describe that as "being more ambitious" when using AI, which is tricky advice to follow. I wrote a tutorial: https://felixrieseberg.com/how-to-be-more-ambitious-a-tutorial-for-humans-with-models/

I just gave my a Grokbot four years of past tax returns and financial files. Just a dump of folders and unlabled files. K-1s, 1099s, mortgage statements, HSA records, and every email with our CPA. 266 files. I just said, please double check my CPA's work. A few minutes later, it had found a lot of mistakes. Mortgage interest left off our 2022 return entirely. HSA payroll contributions counted twice, four years in a row, which created a penalty tax that never should have existed. A missing IRA form for Jen that would have cost us. On this year's return: home office errors, two K-1s missing, an outdated 1099, and startup losses we never claimed. And a retirement contribution the return already deducted...that we hadn't actually deposited. I sent it all to our CPA. They sent back an amended return. The bot reviewed that too. One fix went the wrong direction. The explanation had typos. Three versions later, all in one afternoon, it said ready to sign. Just a few hours start to finish. With about 90 seconds of my time and attention required. I was just the meat proxy hitting send as Grokbot watched the replies in my email. Our CPA is great and turned every fix around the same day. We chatted afterwards. He wanted to know what I was using - just normal ole' grokbot. I asked him what he was using? No AI tools. I set him up with @magnetictax for next season (we're an investor). All CPAs should be using Magnetic Tax or something like it. Every single one. All individuals should be double checking the work and past work with Grokbot or something like that. Want to copy the exact the prompt I used? it was just this: "Please do a thorough review of my taxes and help me understand if my CPA made any mistakes" "Think deeply" Or just tag your bot into this thread.

New on our Engineering blog: Creating in-house source control. In May, we merged around 2,500 code changes per day. In September, we merged more than 30,000 per day. As a result, we became bottlenecked by developer infrastructure built for human (rather than agent) scale. Here, we share how we built a new version control system—and fully migrated our monorepo from git—in five months: https://www.anthropic.com/engineering/redesigning-source-control