Théo Carrive

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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Matthieu Jonglez from Progress talking about how to use AI with #semantic #agentic #RAG on structured and unstructure data from a companies’s private systems “The greater the context, the greater the insights” #progressdatasummit

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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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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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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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AI keeps getting better at benchmarks — but what happens when you change the form of the problem without changing the logic? Across domains — vision, math, and medicine — the pattern is the same: Change the surface, keep the logic → accuracy collapses. This isn’t “reasoning” breaking down. It’s pattern-matching hitting its limits. And the fix isn’t just bigger models, but architectures with symbolic grounding. I've written (and likely keep writing) something about this topic: 📄 Read it here → https://lnkd.in/eweJ7rbt

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Forget plug-and-play AI. We’re entering the era of agent-native systems and this is the new stack. 👇 Let’s break it down: 🔹 The Agent-Native Stack (MCP Edition) 1️⃣ Interfaces → Where agents live → Claude, LibreChat, Cursor, Windsurf → Not just chat — structured workflows, tool use, memory 2️⃣ MCP Servers → Where agents work → Databases (Supabase, Clickhouse) → Email (Resend), Docs (Notion), Tickets (Linear), Payments (Stripe) → All agent-accessible. All programmable. 3️⃣ Execution Environments → Where stuff gets done → Browserbase, ScrapNARA, E2B → Agents browse, click, extract, interact — securely and at scale 4️⃣ Coordination & Monitoring → What makes it reliable → AgentDesk, Braintrust, Grafana → Track flows, manage fallbacks, handle complexity 5️⃣ Marketplace Layer → Plug-ins + deployment infra → Mintlify, Glama, MCP.so, Smithery → Launch-ready agents + serverless hosting 🧠 If you're building ops-ready automation: → Don't stop at “can it call an API?” → Ask: Can it run workflows, recover, monitor, adapt? This isn’t just “tools with AI.” It’s AI that runs your tools. End-to-end. Autonomous. Coordinated. 📌 Save this stack. 📥 DM if you're exploring agent infra, orchestration, or automation beyond the prototype stage. Follow Dennis for weekly drops on building smart systems that scale.

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