Victor Darboux-Galli

Thrilled to announce that SurrealDB now has an official integration with n8n ⚡ This is a big step for our ecosystem. Developers can now: ✅ Run SurrealQL queries directly inside n8n workflows ✅ Orchestrate AI pipelines across 350+ services ✅ Trigger automations from live SurrealDB events SurrealDB’s mission is to be the unified data layer for AI and modern applications. Connecting with a leading automation platform like n8n brings us closer, enabling teams to build production-ready pipelines without glue code or fragile scripts. Docs → https://lnkd.in/evzjreEW Repo → https://lnkd.in/ePEnnFRs Blog → https://lnkd.in/eEbKXaJV

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Prompt engineering isn’t dead — it just got a major upgrade. A year ago, we built Latitude to help people design better prompts and workflows. Thousands used it. But we saw the same problem again and again: 👉 everyone wanted to build agents, but building by hand was too fragile, too technical, too slow. So today, we’re launching Latitude 2.0 on Product Hunt. The first agent that builds agents. Here’s the shift: ⚡ From writing endless prompts → to describing what you want, once. ⚡ From fragile, manual setups → to production-ready AI agents in minutes. ⚡ From “prompt hacking” → to true agent engineering. This isn’t the end of prompt engineering. It’s the new way of doing it. ⏳ We’re live today only on Product Hunt. Every upvote helps signal the future: 👉 https://lnkd.in/dkmYFnMs #AI #PromptEngineering #AgentEngineering #ProductHunt #Automation

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Remember our amazement with GPT-3 in 2020? Back then, we thought AI writing articles was already magical... Today, AI can assemble teams and collaborate autonomously. This isn't just an upgrade. It's a fundamental shift in how AI works. 🚀 Four Stages of AI Evolution 𝐒𝐭𝐚𝐠𝐞 1: 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐋𝐋𝐌 → Pure text generation with stateless processing → Lightweight & fast, but lacks memory and contextual awareness → Best for: Simple conversations, content creation, basic Q&A 𝐒𝐭𝐚𝐠𝐞 2: 𝐑𝐀𝐆 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐦𝐞𝐧𝐭 → Dynamic document retrieval, improve factual accuracy → Real-time data access with multimodal retrieval support → Best for: Domain-specific Q&A, knowledge-intensive tasks 𝐒𝐭𝐚𝐠𝐞 3: 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 → Integrates planning, memory, and external tool calling → Multi-step workflow automation execution → Best for: Research assistants, report generation, API-driven workflows 𝐒𝐭𝐚𝐠𝐞 4: 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 → Multiple specialized agents collaborating → Self-organizing AI teams with continuous learning and optimization → Best for: Cross-departmental coordination, large-scale automation operations 💡 Key Transformation The fundamental leap from "passive response" to "proactive collaboration": ✅ Memory Evolution: Short-term → Long-term → Episodic memory ✅ Decision Capability: Simple replies → Complex reasoning chains → Multi-agent negotiation ✅ Tool Integration: None → Single tool → Tool ecosystem ✅ Autonomy: Fully dependent → Semi-autonomous → Fully autonomous 🔮 Real Application Comparison 𝐋𝐋𝐌: "Help me write a market analysis report." 𝐑𝐀𝐆:  "Write an analysis report based on the latest market data." 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭: "Collect data, analyze trends, generate a report, and send to relevant personnel." 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈: "Assemble research teams, collaborate on tasks, continuously track market changes, and update strategies." 🤝 Bottom Line: Choosing between LLM, RAG, AI Agent, or Agentic AI isn't about hierarchy. It's about fit. Match the solution to the scope of your problem.

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A few years ago, 90% of our revenue came from Airtable customers. We lived in constant fear they'd destroy us overnight. For years, this drove our strategic decisions: • Tried to diversify away from Airtable • Built our own data platform • Constantly worried about the day they'd pull the rug Then it happened. Airtable changed their pricing and API terms. We scrambled. Made changes. Warned customers. And... mostly everyone was fine. Some customers had to switch plans. But the impact was nowhere near what we'd imagined. Then Airtable built portals - one of our main features. We thought this was the end. But people still come to us. Our portals work better. Our pricing makes more sense. Even after they built what we already had, we kept winning. Big lesson there: The existential threats you spend years worrying about usually aren't as bad as you think. And the defensive moves you make because of those fears often hurt more than the actual threat would. We wasted time and energy building around a risk that turned out to be manageable. Focus on making customers happy instead of protecting against imaginary disasters.

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🚨 The new AI Agent update in n8n just changed the game for no-code orchestration. You can now build fully modular agent armies — where each AI agent lives inside your main agent as a tool. No more scattered workflows. No more Execute Workflow nodes. Just clean, centralized AI orchestration. In my latest tutorial, I show you how I built a system that: 🤖 Uses one master orchestration agent to control all sub-agents 📩 Automates emails, calendars, expense tracking, and company knowledge 🧠 Assigns memory, models, and tools per agent — inside a single workflow 📂 Removes the need for multiple builds by letting agents act as tools 📈 Scales perfectly for client work, personal use, or AI assistant businesses Watch the full build + demo here 👉 https://lnkd.in/gDgfRfW6 Want to learn how to create AI automations like this — and package them into real client offers? Join the AI Workshop community — where I share full blueprints, sales strategies, and how to turn automations into income. 👉 https://lnkd.in/gnNJqH9u #AI #Automation #n8n #nocode #AIagents #VoiceAI #AIWorkshop #OpenAI #Claude #Gemini #AgentOps #WorkflowDesign

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