What if you could ask a chatbot a question the size of an entire encyclopedia—and get an answer in real time? Multi-million token queries with 32x more users are now possible with Helix Parallelism, an innovation by #NVIDIAResearch that drives inference at huge scale. 🔗

Blog v2.0 @AgentOpsAI now live! bringing you one step closer to the world of agent observability, infra, and ops

We're advancing agentic AI development with Firebase Studio. Get the details of the latest update ↓

Meet Context, the first AI office suite. Humanity spends 2.5 trillion hours a year on office work. Context can one-shot most of it. Welcome to the era of vibe-working. Sign up today or tag @contextsuite with a prompt.

Introducing Orchids - the world's first AI tool that lets you chat with AI to build apps and websites that don't look and feel "AI generated". On internal benchmarks, Orchids performs close to 3x better on general app and website creation tasks than any other tool on the market. If you don't believe us, give it a try at orchids [dot] app :) Comment “orchids” and we'll give you 2 days of unlimited credits.

We’ve open-sourced Trae-Agent. You can all `git clone` `cd trae-agent` now

如果你想大大加快你的发货迭代，你必须使用 playwright mcp 并告诉你的代理如何在你的（或光标/克劳德/双子座规则）中使用它 巨大的解锁

使用 Google Agent Development Kit 构建具有结构化输出的客户支持工单代理。 100% 开源代码和分步教程：

人工智能的用户体验还不存在。 想象力、品味和对代理-人类反馈循环的痴迷——这就是我们如何做到正确的。了解@tryramp进入代理编排的第一步。 剧透警告，这不仅仅是聊天。

这个 Claude MCP AI 代理取代了您 $200K+ 的运营团队。 我可能不应该免费共享确切的系统...... 当我试图在凌晨 3 点在 Pokemon Go 上捕捉皮卡丘时，它审核了我的整个业务，发现了 12 个瓶颈，并为我构建了 5 个生产就绪的 n8n 代理 效率提升绝对是疯狂的 大多数创始人花费数月时间聘请每小时收费 500 美元的运营顾问，只是为了告诉你出了什么问题 这个代理系统在几分钟内完成了他们的全部工作 部署它时会发生以下情况： →在 3 分钟内运行完整的商业智能审计（顾问需要数周时间） → 识别 12+ 个降低效率的工作流程瓶颈 → 为您的业务量身定制定制代理系统 → 构建 5+ 个具有高级错误处理功能的自主 AI 代理 →创建智能编排层，将所有内容同步在一起 →可在 10 分钟内实现完整的运营转型 顾问现在收取 50 美元的整个审计在 10 分钟内完成 需要零技术知识 无需昂贵的顾问 零个月的来回 只需描述您当前的设置，然后观看它建立您的代理帝国 数学很愚蠢，很简单： 200 美元的运营团队工资与一次性 MCP 部署 每月节省 16,600 美元 该系统包括： - 智能业务堆栈分析器 - 瓶颈检测人工智能 - 定制代理架构师 - 自主代理构建器 - 完整的部署文档 这正是系统构建 7 位数的运营基础设施 而且你是免费获得的 关注 + 转发 + 评论“MCP”，今晚我会向您发送完整的设置指南 别睡这个 您每周等待的每周都是 30+ 小时的手动工作，您永远无法挽回

Here’s my 1hr tutorial on how to use Claude Code for notes & research. 10x your notes with: - core agentic flows - custom commands - automated tags/links - subagents - cloud usage - stt The goal is to “agent-pill” you on the future of work. Watch for 10 tips + demos in 61min.

This NEW AI Operating System is INSANE! 🤯 Want the full guide? DM me.

MiniMax is the James Bond of AI agents. I tested every AI website builder. Only one created something I'd actually use. Minimax M1 Quality Indicators: → Pixel-perfect design execution → Functional interactive elements → Professional multimedia integration → Responsive layout optimization → Real content generation Save this evaluation, it will guide your tool selection 📐 Want the full guide? DM me. 📥

🤖 New on the Nebius blog: Agent 101 – Launching production‑grade AI agents at scale From proof‑of‑concept to production-level systems, we break down the full stack: ✅ LLMs ✅Frameworks: @crewaiinc, @LangChainAI, @Google ADK, @AgnoAgi ✅Observability: @helicone_ai, @AgentOpsAI, @keywordsai ✅Real-time grounding: @Linkup_platform ✅Memory, tool usage, evals & more 🧠All powered by Nebius AI Studio – 30+ open-source models, fast inference, cost-effective tiers, and seamless drop-in compatibility.

Reliability is the name of the game for agents, and it's unlikely to be solved purely at the model layer for the foreseeable future. This is creating green shoots for infrastructure builders, with a few interesting trends starting to emerge: 1. Simulation as CI for agents: a) The most valuable piece of data today is trajectory data i.e. collections of task (P) -> {t1, t2... tk} mappings. With more trajectory data, agents can be improved with techniques like RFT. b) Since these trajectories can be quite specific to a company's underlying data (D), you need to be able to actually simulate the behavior of agents within your environment vs. rely on 3P trajectory data. So, how might you do this? - Maintain an agent and MCP registry for an enterprise, and a staging environment. Bootstrap a metadata layer that contains the objective of each agent, the tools it has access to, the scope of each agent vis.a.vis each tool etc. Your SDK may need to generate MCP servers on the fly for certain internal applications. - Execute scenarios in staging for each agent by providing prompt / task variations, inspecting the tool calls produced and evaluating performance against a multi-objective reward function (e.g. performance against the objective, minimization of tool invocations). - A critical component is accurately providing quantifiable reward functions for each agent that unlock high-fidelity evals and close the loop for reliable CI. - All of this needs to be productized: easy-to-adopt infrastructure that developers can extend, but with batteries included. You can start to see a new paradigm forming—not unit tests for code, but simulation harnesses for agents. What happens when you get trajectory data? 2. Enterprises will move to "context lakes": - An evolving, queryable memory layer that serves as a hub for agent trajectories enriched by enterprise data stored in the delta lake / SNOW. A potent mix of a knowledge base, a semantic cache, and an execution log. - Extremely fast reads for inference-time retrieval that supports high QPS. - As mentioned in a prior post, the semantic cache (really interesting opportunity for startups) will cluster task–trajectory pairs (e.g., via k-means), enabling fast retrieval and “result fusing” during planning or tool selection. Agents will dip into the context lake constantly. High QPS, low-latency context fetch will become as important as fast embedding search is today. 3. Agent authentication becomes a first-class concern: -Traditional OAuth and API key models break down when agents act on behalf of users and themselves, across long-lived sessions. -You need a framework for agent identity, delegation, and scoping—one that supports things like tool level permissions, task bound credentials and delegation graphs. We’re entering an era where testing software means simulating behavior, querying software means retrieving context, and securing software means authenticating autonomous agents.

为什么您的编码代理不再需要 RAG 来自 Cline 的 Nik Pash 解释了为什么他不再推荐 RAG 用于自主编码代理，他的观点比我预期的要强烈。 应用层正在缩小。随着模型的改进，我们围绕 LLM 构建的所有巧妙工程都变得过时。 RAG 发生了什么： 上下文窗口显着扩展，无需嵌入搜索 编码代理在直接文件访问中比分块嵌入效果更好 当您将温度设置为 0 时，幻觉甚至不是问题 嵌入存储的安全问题很重要 像 Klein 这样的现代编码代理没有使用 RAG，而是使用 Nik 所说的“叙事完整性”。让代理通过 grep 等工具有机地探索代码，完整读取文件，并遵循自己的思路。这模仿了高级工程师的实际工作方式。 就连 Cloud Code 的 Boris 也承认他们尝试过 Rag 并放弃了它。模式很清楚。 当 rag 仍然有意义时： 预算限制（嵌入搜索使用更少的令牌） 海量非结构化数据湖 一些非编码用例 但对于严肃的工程团队来说呢？停止通过嵌入搜索分散您的编码代理的注意力。让他们直接阅读代码，自然地建立理解，并专注地执行。 真正的问题不在于 Rag 是否已经死去，而是当更简单的方法现在效果更好时，你是否仍然坚持过时的解决方案。

AgentOps is ready to start onboarding projects for our agent hosting. DM if youre looking to productionize your agent