– an open catalog and API to discover and use publicly available MCP servers. Finally, MCP servers can be discovered through a central catalog. Think of it as an App Store for scanning and searching your MCPs: → An open catalog + API for discovering MCP servers → One-click install in VS Code → Servers from npm, PyPI, DockerHub → Sub-registries possible for security and curation → Works across Copilot, Claude, Perplexity, Figma, Terraform, Dynatrace etc. Although it is still in preview and being worked on, it will definitely serve a major problem. Github link: https://lnkd.in/df-qTnYe

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Over 40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs, unclear business value or inadequate risk controls, according to Gartner.

My site: https://natebjones.com/My links: https://linktr.ee/natebjonesMy substack: https://natesnewsletter.substack.com/Takeaways 1. Software 3.0 Paradigm Sh...

𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗠𝗖𝗣 𝗶𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗼𝘀𝗲 𝗿𝗮𝗿𝗲 “𝗼𝗵 𝗱𝗮𝗺𝗻, 𝘁𝗵𝗶𝘀 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴” 𝗺𝗼𝗺𝗲𝗻𝘁𝘀! I’ve been in tech for years, and MCP (Model Context Protocol) is one of those rare innovations that deserves every bit of the hype. I really can’t believe how much smoother everything gets. 𝗜𝗳 𝗜 𝗵𝗮𝗱 𝘁𝗼 𝗯𝗲𝘁 𝗼𝗻 𝗼𝗻𝗲 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗶𝗻 𝗔𝗜, 𝗶𝘁’𝘀 𝗠𝗖𝗣. MCP sounds complex — but it’s really not. Think of it as a guide that helps your AI agents understand: → what tools exist → how to talk to them → and when to use them 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝟵 𝗳𝘂𝗹𝗹𝘆 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗲𝗱 𝗠𝗖𝗣 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗲𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱 𝘄𝗶𝘁𝗵 𝘃𝗶𝘀𝘂𝗮𝗹𝘀 & 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝗰𝗼𝗱𝗲 (𝘁𝗼 𝗴𝗲𝘁 𝘆𝗼𝘂 𝘀𝘁𝗮𝗿𝘁𝗲𝗱): ⬇️ 1. 100% Local MCP Client → Build a local MCP client using SQLite + Ollama — no cloud, no tracking. → Full docu: https://lnkd.in/gtaEGvFZ 2. MCP-powered Agentic RAG → Add fallback logic, vector search, and agents in one clean flow. → Full docu: https://lnkd.in/gsV62MDE 3. MCP-powered Financial Analyst → Fetch stock data, extract insights, generate summaries. → Full docu: https://lnkd.in/g2\_EaJ\_d 4. MCP-powered Voice Agent → Speech-to-text, database queries, and spoken responses — all local. → Full docu: https://lnkd.in/gweH8Rxi 5. Unified MCP Server (with MindsDB) → Query 200+ data sources via natural language using MindsDB + Cursor. → Full docu:https://lnkd.in/gCevVqKK 6. Shared Memory for Claude + Cursor → Build cross-app memory for dev workflows — share context seamlessly. → Full docu: https://lnkd.in/giDXdtXd 7. RAG Over Complex Docs → Tackle PDFs, tables, charts, messy layouts with structured RAG. → Full docu: https://lnkd.in/gMHqHvBR 8. Synthetic Data Generator (SDV) → Generate synthetic tabular data locally via MCP + SDV. → Full docu:https://lnkd.in/ghyUyByS 9. Multi-Agent Deep Researcher → Rebuild ChatGPT’s research mode, fully local with writing agents. → Full docu: https://lnkd.in/gp3EsrZ2 Kudos to Daily Dose of Data Science! 𝗜 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲𝘀𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀 — 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 — 𝗶𝗻 𝗺𝘆 𝘄𝗲𝗲𝗸𝗹𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. 𝗬𝗼𝘂 𝗰𝗮𝗻 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲: https://lnkd.in/dbf74Y9E | 49 comments on LinkedIn

99% 𝗼𝗳 𝗽𝗲𝗼𝗽𝗹𝗲 𝗴𝗲𝘁 𝘁𝗵𝗶𝘀 𝘄𝗿𝗼𝗻𝗴: 𝗧𝗵𝗲𝘆 𝘂𝘀𝗲 𝘁𝗵𝗲 𝘁𝗲𝗿𝗺𝘀 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗮𝗻𝗱 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗶𝗻𝘁𝗲𝗿𝗰𝗵𝗮𝗻𝗴𝗲𝗮𝗯𝗹𝘆 — 𝗯𝘂𝘁 𝘁𝗵𝗲𝘆 𝗱𝗲𝘀𝗰𝗿𝗶𝗯𝗲 𝘁𝘄𝗼 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝗹𝘆 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀! ⬇️ Let’s clarify it once and for all: ⬇️ 1. 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀: 𝗧𝗼𝗼𝗹𝘀 𝘄𝗶𝘁𝗵 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆, 𝗪𝗶𝘁𝗵𝗶𝗻 𝗟𝗶𝗺𝗶𝘁𝘀 ➜ AI agents are modular, goal-directed systems that operate within clearly defined boundaries. They’re built to: * Use tools (APIs, browsers, databases) * Execute specific, task-oriented workflows * React to prompts or real-time inputs * Plan short sequences and return actionable outputs 𝘛𝘩𝘦𝘺’𝘳𝘦 𝘦𝘹𝘤𝘦𝘭𝘭𝘦𝘯𝘵 𝘧𝘰𝘳 𝘵𝘢𝘳𝘨𝘦𝘵𝘦𝘥 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯, 𝘭𝘪𝘬𝘦: 𝘊𝘶𝘴𝘵𝘰𝘮𝘦𝘳 𝘴𝘶𝘱𝘱𝘰𝘳𝘵 𝘣𝘰𝘵𝘴, 𝘐𝘯𝘵𝘦𝘳𝘯𝘢𝘭 𝘬𝘯𝘰𝘸𝘭𝘦𝘥𝘨𝘦 𝘴𝘦𝘢𝘳𝘤𝘩, 𝘌𝘮𝘢𝘪𝘭 𝘵𝘳𝘪𝘢𝘨𝘦, 𝘔𝘦𝘦𝘵𝘪𝘯𝘨 𝘴𝘤𝘩𝘦𝘥𝘶𝘭𝘪𝘯𝘨, 𝘊𝘰𝘥𝘦 𝘴𝘶𝘨𝘨𝘦𝘴𝘵𝘪𝘰𝘯𝘴 But even the most advanced are limited by scope. They don’t initiate. They don’t collaborate. They execute what we ask! 2. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜: 𝗔 𝗦𝘆𝘀𝘁𝗲𝗺 𝗼𝗳 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 ➜ Agentic AI is an architectural leap. It’s not just one smarter agent — it’s multiple specialized agents working together toward shared goals. These systems exhibit: * Multi-agent collaboration * Goal decomposition and role assignment * Inter-agent communication via memory or messaging * Persistent context across time and tasks * Recursive planning and error recovery * Distributed orchestration and adaptive feedback Agentic AI systems don’t just follow instructions. They coordinate. They adapt. They manage complexity. 𝘌𝘹𝘢𝘮𝘱𝘭𝘦𝘴 𝘪𝘯𝘤𝘭𝘶𝘥𝘦: 𝘳𝘦𝘴𝘦𝘢𝘳𝘤𝘩 𝘵𝘦𝘢𝘮𝘴 𝘱𝘰𝘸𝘦𝘳𝘦𝘥 𝘣𝘺 𝘢𝘨𝘦𝘯𝘵𝘴, 𝘴𝘮𝘢𝘳𝘵 𝘩𝘰𝘮𝘦 𝘦𝘤𝘰𝘴𝘺𝘴𝘵𝘦𝘮𝘴 𝘰𝘱𝘵𝘪𝘮𝘪𝘻𝘪𝘯𝘨 𝘦𝘯𝘦𝘳𝘨𝘺/𝘴𝘦𝘤𝘶𝘳𝘪𝘵𝘺, 𝘴𝘸𝘢𝘳𝘮𝘴 𝘰𝘧 𝘳𝘰𝘣𝘰𝘵𝘴 𝘪𝘯 𝘭𝘰𝘨𝘪𝘴𝘵𝘪𝘤𝘴 𝘰𝘳 𝘢𝘨𝘳𝘪𝘤𝘶𝘭𝘵𝘶𝘳𝘦 𝘮𝘢𝘯𝘢𝘨𝘪𝘯𝘨 𝘳𝘦𝘢𝘭-𝘵𝘪𝘮𝘦 𝘶𝘯𝘤𝘦𝘳𝘵𝘢𝘪𝘯𝘵𝘺 𝗧𝗵𝗲 𝗖𝗼𝗿𝗲 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲? AI Agents = autonomous tools for single-task execution Agentic AI = orchestrated ecosystems for workflow-level intelligence 𝗡𝗼𝘄 𝗹𝗼𝗼𝗸 𝗮𝘁 𝘁𝗵𝗲 𝗽𝗶𝗰𝘁𝘂𝗿𝗲: ⬇️ 𝗢𝗻 𝘁𝗵𝗲 𝗹𝗲𝗳𝘁: a smart thermostat, which can be an AI Agent. It keeps your room at 21°C. Maybe it learns your schedule. But it’s working alone. 𝗢𝗻 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁: Agentic AI. A full smart home ecosystem — weather-aware, energy-optimized, schedule-sensitive. Agents talk to each other. They share data. They make coordinated decisions to optimize your comfort, cost, and security in real time. That’s the shift = From pure task automation to goal-driven orchestration. From single-agent logic to collaborative intelligence. This is what’s coming = This is Agentic AI. And if we confuse “agent” with “agentic,” we risk underbuilding for what AI is truly capable of. The Cornell University paper in the comments on this topic is excellent! ⬇️ | 186 comments on LinkedIn

I frequently see conversations where terms like LLMs, RAG, AI Agents, and Agentic AI are used interchangeably, even though they represent fundamentally different layers of capability. This visual guides explain how these four layers relate—not as competing technologies, but as an evolving intelligence architecture. Here’s a deeper look: 1. 𝗟𝗟𝗠 (𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹) This is the foundation. Models like GPT, Claude, and Gemini are trained on vast corpora of text to perform a wide array of tasks: – Text generation – Instruction following – Chain-of-thought reasoning – Few-shot/zero-shot learning – Embedding and token generation However, LLMs are inherently limited to the knowledge encoded during training and struggle with grounding, real-time updates, or long-term memory. 2. 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻) RAG bridges the gap between static model knowledge and dynamic external information. By integrating techniques such as: – Vector search – Embedding-based similarity scoring – Document chunking – Hybrid retrieval (dense + sparse) – Source attribution – Context injection …RAG enhances the quality and factuality of responses. It enables models to “recall” information they were never trained on, and grounds answers in external sources—critical for enterprise-grade applications. 3. 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 RAG is still a passive architecture—it retrieves and generates. AI Agents go a step further: they act. Agents perform tasks, execute code, call APIs, manage state, and iterate via feedback loops. They introduce key capabilities such as: – Planning and task decomposition – Execution pipelines – Long- and short-term memory integration – File access and API interaction – Use of frameworks like ReAct, LangChain Agents, AutoGen, and CrewAI This is where LLMs become active participants in workflows rather than just passive responders. 4. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 This is the most advanced layer—where we go beyond a single autonomous agent to multi-agent systems with role-specific behavior, memory sharing, and inter-agent communication. Core concepts include: – Multi-agent collaboration and task delegation – Modular role assignment and hierarchy – Goal-directed planning and lifecycle management – Protocols like MCP (Anthropic’s Model Context Protocol) and A2A (Google’s Agent-to-Agent) – Long-term memory synchronization and feedback-based evolution Agentic AI is what enables truly autonomous, adaptive, and collaborative intelligence across distributed systems. Whether you’re building enterprise copilots, AI-powered ETL systems, or autonomous task orchestration tools, knowing what each layer offers—and where it falls short—will determine whether your AI system scales or breaks. If you found this helpful, share it with your team or network. If there’s something important you think I missed, feel free to comment or message me—I’d be happy to include it in the next iteration. | 119 comments on LinkedIn

Watch CEO Satya Nadella unveil all the biggest product moves, including Copilot and Azure updates, developer tools, and more, from Seattle.0:00 Intro0:14 Bui...

𝗔𝘁 𝗜/𝗢 2025, Google 𝘀𝗵𝗼𝘄𝗲𝗱 𝘂𝘀 𝘄𝗵𝗮𝘁 𝗔𝗜-𝗳𝗶𝗿𝘀𝘁 𝗥𝗘𝗔𝗟𝗟𝗬 𝗺𝗲𝗮𝗻𝘀. 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗚𝗼𝗼𝗴𝗹𝗲 𝗮𝗻𝗻𝗼𝘂𝗻𝗰𝗲𝗱: ⬇️ The company's flagship developer event Google I/O 2025 was held last night in Mountain View, California. 𝗧𝗟𝗗𝗥: Google is turning Gemini into the AI operating system for everything — with agents now embedded across Search, Chrome, Workspace, Android, and more. If you don’t have time for the full event, here’s a curated 𝘀𝘂𝗽𝗲𝗿𝗰𝘂𝘁 of the highlights that really matter. 𝗞𝗲𝘆 𝗺𝗼𝗺𝗲𝗻𝘁𝘀 𝗳𝗿𝗼𝗺 𝗚𝗼𝗼𝗴𝗹𝗲 𝗜/𝗢 𝟮𝟬𝟮𝟱: 0:00 𝗜𝗻𝘁𝗿𝗼 – AI-native from the ground up 0:11 𝗚𝗲𝗺𝗶𝗻𝗶 𝗽𝗹𝗮𝘆𝘀 𝗮 𝗣𝗼𝗸𝗲𝗺𝗼𝗻 𝗴𝗮𝗺𝗲 — memory, reasoning, and code 0:30 𝗚𝗼𝗼𝗴𝗹𝗲 𝗕𝗲𝗮𝗺 – Real-time 3D video chat with AI 1:08 𝗚𝗼𝗼𝗴𝗹𝗲 𝗠𝗲𝗲𝘁 – Speech-to-speech translation, live 1:27 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗠𝗮𝗿𝗶𝗻𝗲𝗿 – AI agents that book, plan, filter, decide 2:07 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 – Gemini gets memory and task awareness 2:40 𝗚𝗲𝗺𝗶𝗻𝗶 𝟮.𝟱 𝗣𝗿𝗼 + 𝗙𝗹𝗮𝘀𝗵 – New SOTA models, LMArena leader 4:57 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗔𝘀𝘁𝗿𝗮 – Multimodal, fast-response agent that sees and hears 5:32 𝗔𝗜 𝗠𝗼𝗱𝗲 – Overlay for restaurants, bookings, prices, events 7:10 𝗦𝗵𝗼𝗽𝗽𝗶𝗻𝗴 – Track, compare, and auto-buy with Google Pay 8:34 𝗚𝗲𝗺𝗶𝗻𝗶 𝗟𝗶𝘃𝗲 – Screen sharing + live AI guidance 8:59 𝗗𝗲𝗲𝗽 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗔𝗴𝗲𝗻𝘁 – Upload files, get insights 9:12 𝗖𝗮𝗻𝘃𝗮𝘀 – Live, collaborative AI whiteboard 9:31 𝗚𝗲𝗺𝗶𝗻𝗶 𝗶𝗻 𝗖𝗵𝗿𝗼𝗺𝗲 – AI understands and acts on any webpage 9:51 𝗜𝗺𝗮𝗴𝗲𝗻 𝟰 – Next-gen image generation 10:23 𝗩𝗲𝗼 𝟯 – Ultra-realistic video model 11:01 𝗟𝘆𝗿𝗶𝗮 𝟮 – AI-powered music composition 11:56 𝗙𝗹𝗼𝘄𝘀 – Multimodal, promptable AI video creation 12:39 𝗔𝗻𝗱𝗿𝗼𝗶𝗱 𝗫𝗥 – AI-first spatial computing 12:57 𝗦𝗮𝗺𝘀𝘂𝗻𝗴 𝗠𝗼𝗼𝗵𝗮𝗻 – Google’s XR headset revealed 13:16 𝗟𝗶𝘃𝗲 𝗴𝗹𝗮𝘀𝘀𝗲𝘀 𝗱𝗲𝗺𝗼 – Gemini + XR = real-time AI overlay Super insightful and forward-looking: Google’s AI strategy just went full stack. Even if some of these projects don’t make it past the prototype stage, the direction is obvious: AI is being integrated into everything. LLMs — Gemini, in this case — are rapidly becoming the new operating system and everything will be powered by AI Agents across all products. Full keynote: https://lnkd.in/dPFFtyZ9 Supercut: https://lnkd.in/d-eBNGjw Enjoy watching! | 61 comments on LinkedIn

I've covered an explainer of AI agents for non-techies before, see the comments for a link to that. For most non-techies, AI is viewed as one entity doing every thing on its own. With agents, we can create a team of specialists. That’s the idea behind multi-agent AI systems This image (from the brilliant folks at LangGraph) shows different ways you can set up teams of AI “agents.” Think of each agent like a little digital worker with a specific role - one plans, another checks facts, one executes tasks, and another reviews the results. Like any good team, they talk to each other, share ideas, and back each other up. Now, let's explain that image: 1️⃣ Single Agent This is your classic setup with one AI model doing all the work. It can use tools, but it’s working solo. Smart, but overworked. 2️⃣ Network Here, agents all talk to each other like a group chat. Everyone’s sharing, checking, and helping out. Great for collaboration, but can get noisy. 3️⃣ Supervisor This is the manager model where one central AI supervises others. It gives instructions and checks in. A bit like a project lead guiding a team. 4️⃣ Supervisor as Tools Flip it around: the main AI treats the others as tools. It doesn’t chat with them it just uses them to get stuff done. Efficient, but not very democratic. 5️⃣ Hierarchical This is like an org chart. Big boss on top, middle managers below, then the doers. Neat, structured, scalable. 6️⃣ Custom Everything everywhere all at once. No strict structure—just doing what works to get the job done. It can look a bit messy, but it’s great for handling tricky tasks that don’t fit in a neat box. → So why does this matter? Traditional AI is like having one brain trying to do everything. But now, we can build teams of AIs, each focused on a task—planning, checking, executing, or reviewing. Multi-agent systems might sound like Sci-Fi but they're already at work today. ↳ Image Credit: Google Agents Companion & LangGraph Multi-agent systems 📔 Source: Agents Companion Report 2025 by Google #education #artificialintelligence #learninganddevelopment

A research-backed AI scenario forecast.

Catch the top moments from the Google Cloud Next keynote presentation, featuring CEO Thomas Kurian on AI breakthroughs, along with key announcements and real...

"𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝘄𝗶𝗹𝗹 𝗿𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗲 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀!" 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 𝗗𝗿𝗲𝗮𝗺: ➜ Deploy AI Agents ➜ Automate everything ➜ Enjoy efficiency 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 𝗥𝗲𝗮𝗹𝗶𝘁𝘆: ➜ Messy, siloed, unreliable data ➜ AI hallucinations & compliance nightmares ➜ Enterprise AI initiatives stall as organizations spend more time fixing data issues than realizing AI-driven value. The Hard Truth: AI (agents) aren't failing—data strategies are. AI Agents are only as effective as the data beneath them. Without governed, high-quality data, AI adoption becomes an expensive experiment instead of a strategic advantage. Important to fix the data first. Kudos for this image to Armand Ruiz! | 258 comments on LinkedIn

We’re evolving our platform to help developers and enterprises build useful and reliable agents.

Wer einen Blick in die Kristallkugel bzgl. KI Agenten wagen möchte - seit wenigen Tagen geistert Manus AI durch meinen Feed. Warte noch sehnsüchtig auf den Access, aber was man bereits sehen kann in diversen Demos macht richtig Lust drauf: General AI Agent als quasi Mischung aus Claude Computer Use, Chat GPT Operator, Deep Research etc und das ganze extrem intelligent miteinander verknüpft. "Manus" als die KI "Hand", die einem tatkräftig im digitalen Alltag hilft. 💪

Tolle Grafik zu KI-Agenten! Insbesondere in den USA gibt es viele Beteiligte mit Tech-Hintergrund, die super Abbildungen erstellen und damit nicht selten sehr hohe Reichweiten erzielen. Genau diese Art der Wissenschaftskommunikation kommt besonders gut an. Zum Linkedin-Post https://lnkd.in/eq4mWQFd

Lessons Learned After 6 Months of Building AI Agents as a Non-Programmer (without the hype)👨‍💻 I just released a 43-minute deep dive on YouTube, breaking down what I’ve learned, the mistakes I’ve made, and cutting through the AI agent hype to share real insights on building AI systems—without a coding background. It’s a crazy time we’re living in. The barrier to entry for AI automation has never been lower, and no-code tools like n8n let anyone build AI-powered workflows faster than ever. 🧠Here are the 7 key lessons I’ve learned: 1️⃣ Build workflows first before jumping into AI agents. Many problems can be solved with simple rule-based automation. 2️⃣ Always wireframe before building. Skipping this step is like trying to solve a puzzle without seeing the picture. 3️⃣ Context is everything. AI performance depends on predefined logic, user context, and real-time data. 4️⃣ Know when NOT to use a vector database. They aren’t magic, and structured data is often better suited for relational databases. 5️⃣ Prompting AI agents is an art. Write your own prompts and refine them reactively—don’t rely on auto-generated prompts. 6️⃣ Scaling AI agents is a nightmare. A few hallucinations may not matter for one user, but at scale, they become huge problems. 7️⃣ No-code tools like n8n are powerful, but they have limits. They’re great for MVPs and internal automations, but scaling sometimes requires a hybrid approach with custom code. 🔗 Watch the Full Video Here: https://lnkd.in/gArmrd8p 🔗 Join the Best Community to Learn How to Build No-Code AI Agents: https://lnkd.in/dqVsX4Ab | 28 comments on LinkedIn

17x interne KI Chatbots / CompanyGPTs eingeführt. Diese 11 Lektionen habe ich gelernt: 𝗗𝗮𝘀 "𝗪𝗮𝗿𝘂𝗺" 𝘇𝗮̈𝗵𝗹𝘁. Was ist das Ziel der KI-Initiative?… | 54 Kommentare auf LinkedIn

🔍🌟 Entdecke, wie du mit einem selbstgebauten KI-Assistenten deinen Arbeitsalltag als Learning Professional vereinfachen kannst! In diesem Video zeige ich d...

WHAT'S HAPPENING RIGHT NOW Measurable Current Changes: • Google reports 25% of all new code is now AI-generated • Meta has announced plans to replace mid-level engineers with AI tools in 2025 • 92% of companies plan to increase AI investments in the next three years ++++++++++++++++++++++++ DIRECT STATEMENTS FROM INDUSTRY LEADERS Sam Altman, OpenAI: • AI agents will begin transforming the workforce as soon as 2025 • These systems will perform tasks similar to early-career software engineers • Could be deployed across thousands or millions of instances Dario Amodei, Anthropic: • Projects AI systems will be broadly better than humans at most tasks by 2026-27 • Anticipates transformation across multiple sectors including most workplace technologies ++++++++++++++++++++++++ DOCUMENTED INDUSTRY SHIFTS Current Market Changes: • 25% of global digital jobs becoming fully remote • Growth sectors identified: technology, green energy, human-centric roles • First AI agents actively joining workforce operations in 2025 ++++++++++++++++++++++++ WHAT TO DO: This isn't just another strategic planning exercise. Here's what actually works: 1. Comprehensive Upskilling - Not Just Tools   • Tools don't create transformation - behavior change does   • Just like giving someone a treadmill doesn't get them in shape, giving them AI tools doesn't create innovation   2. All-Organization Approach   • Best solutions often come from unexpected places   • Every department will be transformed - this isn't like digital marketing that affects one area      3. Strategic Integration   • This affects everything: strategy, delivery, operations, legal, HR   • Your front-line workers will know best AI can make the biggest impact   • Success requires both top-down support and bottom-up innovation ++++++++++++++++++++ When your company is ready, we are ready to upskill your workforce at scale. Our Generative AI for Professionals course is tailored to enterprise and highly effective in driving AI adoption through a unique, proven behavioral transformation. It's pretty awesome. Check out our website or shoot me a DM. | 161 comments on LinkedIn

Agentic AI "systems of automation" will transform static business operations into dynamic, context-aware, autonomous systems.

Many 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵𝘴 shared on LinkedIn are 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸𝘴 or 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯𝘴 in disguise. Here's why this distinction 𝘮𝘢𝘵𝘵𝘦𝘳𝘴: AI… | 706 comments on LinkedIn

🌟 Bereit für einen Einblick in die Zukunft der Arbeit und der Personalentwicklung? In diesem Video tauchen wir tief in die Welt der AI-Agents ein, jene digi...

🚀 Entdecke Microsofts revolutionäres KI-System, Magnetic-One! Diese bahnbrechende Multi-Agenten-KI ist nicht einfach nur ein weiterer Chatbot – sie führt wirklich Aktionen aus! 💻 Von automatisierter Websuche über Dateiverwaltung bis hin zur Codierung nutzt das System spezialisierte Agenten, um komplexe Aufgaben nahtlos zu bewältigen. Erfahre, wie der Orchestrator die Agenten Web Surfer, File Surfer, Coder und Terminal koordiniert, um beeindruckende Ergebnisse zu liefern. Egal, ob du Entwickler, Content Creator oder Technik-Enthusiast bist – dieses System könnte deine Produktivität revolutionieren! 🔍 Was wir in diesem Video behandeln: 1️⃣ Was ist Magnetic-One und warum ist es ein Game-Changer? 2️⃣ Wie spezialisierte Agenten zusammenarbeiten, um Aufgaben zu automatisieren 3️⃣ Praxisbeispiele und Anwendungsfälle 4️⃣ Potenzielle Risiken und Microsofts Sicherheitsmaßnahmen 5️⃣ So kannst du Magnetic-One noch heute ausprobieren! 🎬 Verpasse nicht die Zukunft der KI – abonniere, like und aktiviere die Glocke, um keine Updates zu verpassen! 🚀 👉 https://www.microsoft.com/en-us/research/articles/magentic-one-a-generalist-multi-agent-system-for-solving-complex-tasks/ Titel: NEUES Multi-Agenten-KI-System, Eigenständige Agenten Armee🚀 | Magnetic One von Microsoft #KI #Microsoft #MagneticOne #Autogen #KünstlicheIntelligenz #AIAutomatisierung #Produktivität #MultiAgentenSystem #TechnikNews #KIRevolutionDie 00:00 - Einführung: Microsofts bahnbrechende KI 🚀 00:45 - Was ist Magnetic-One? 01:33 - Die KI-Agenten: Web Surfer & File Surfer 02:25 - Coder & Computer Terminal: Automatisierung für Entwickler 03:15 - Praxisbeispiele & Anwendungsfälle 04:05 - Die Stärke des modularen Designs & Autogen 05:00 - Risiken & Microsofts Sicherheitsmaßnahmen 05:50 - Magnetic-One im Vergleich zu anderen Systemen 06:25 - Die Zukunft der KI: Wohin geht die Reise? 06:40 - Fazit Stimme ist meine eigene Stimme die ich für persönliche Nutzung einer KI angelernt habe und steht nicht zur Freien Verfügung.

Was war gestern für ein Tag ... so viele Ankündigungen und Updates… | 40 comments on LinkedIn

6 key elements for agentic AI system 1️⃣ Domain/Business Logic: Understanding specific processes, rules, and operations ensures relevant AI decisions…