Hubert Schallebaum

Stop using one-line prompts. Instead, structure them into 4 parts: 1️⃣ Identity – define the role of your AI agent 2️⃣ Style – decide how it should format and respond 3️⃣ Response guidelines – keep it accurate and within limits 4️⃣ Task or goal – what the agent needs to accomplish Get this right, and your AI agents become smarter and more reliable.

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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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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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🚨 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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If you're working on AI agents, Claude workflows, or prompt tuning — you should check this. Anthropic just released 8 free guides packed with practical help on AI agents, Prompt engineering, coding, prompt evaluations, and more. 1. Prompt Engineering → Learn how to write better prompts for clearer, context-aware responses. 🔗 https://lnkd.in/d3bdkNgp 2. Claude Code: Best Practices for Agentic Coding → Tips for writing clean, reusable, and structured code using Claude. 🔗 https://lnkd.in/d99hhYzC 3. Building Effective Agents → Understand patterns, decision flows, and tools to create scalable agents. 🔗 https://lnkd.in/dKkkhXRv 4. The AI Fluency Framework → A beginner-friendly course to build confidence in using AI tools responsibly. 🔗 https://lnkd.in/dw5Rg7wz 5. Build with Claude → Official tools, API docs, and setup guides for developers building with Claude. 🔗 https://lnkd.in/dyVPbMyE 6. Claude for Work → Use cases and workflows for teams using Claude in daily operations. 🔗 https://lnkd.in/dJ8Fyy85 7. Anthropic API Fundamentals → A GitHub-based guide to learn Claude API basics and integration tips. 🔗 https://lnkd.in/d9x7vguc 8. Prompt Evaluations → Test and score prompts to make your outputs more reliable and accurate. 🔗 https://lnkd.in/dw8TkPbs 💾 Save this for later 🔁 Repost if your team or friends are building with Claude 👀 Follow Shubham Vora for more AI agent and prompt tips that actually help

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We need to talk about how ChatGPT is shaping the way our brains work. MIT’s Media Lab just ran one of the first neuroscience studies on generative AI — and the results are concerning. The Study • 54 participants, ages 18–39 • Wrote SAT-style essays in three conditions: solo, with Google, or with ChatGPT • Brain activity tracked via EEG across 32 regions • Essays reviewed by educators for quality The Findings 1. Lowest brain engagement. ChatGPT users showed the weakest neural activity — consistently underperforming at neural, linguistic, and behavioral levels. 2. Memory loss in action. When asked to re-write essays without AI, most ChatGPT users couldn’t remember their own work. Brain waves tied to memory and creativity (alpha/theta) stayed low. 3. Declining effort. Over months, AI users got progressively lazier — many defaulted to “copy-paste” prompts and editing instead of writing. 4. Writing quality suffered. Teachers called their essays “soulless,” “formulaic,” and “lacking depth.” By contrast, the Google and solo groups wrote with more originality and reported higher satisfaction. 5. Critical thinking risk. Researchers warn heavy reliance on LLMs could weaken neural pathways that build resilience, memory, and creativity — especially for developing brains. Why It Matters Lead researcher Nataliya Kosmyna released the findings early, before peer review, because she feared policymakers might rush to integrate AI into education (“GPT kindergarten”) without understanding the risks. She argues: developing brains are most at risk. Other studies from the Media Lab have also linked heavy AI use to increased loneliness and reduced motivation. Early signals suggest the same patterns may hold in fields like software engineering — where critical thinking and problem-solving are essential. The Takeaway AI can boost speed and convenience. But the trade-off may be memory, depth, and engagement. The best strategy isn’t avoiding AI — it’s using it intentionally: • Start with your own thinking • Bring AI in later to polish or expand • Use AI as a tool, not a substitute for thought This is less about banning ChatGPT — and more about deciding what kind of thinkers we want to become. Read here: https://lnkd.in/erug8TSc #AI #Leadership #CriticalThinking #Neuroscience

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Processing 5M+ reviews taught us that volume reveals what prototypes hide. Small-scale tests with clean data work perfectly. Production scale with messy reality exposes every architectural weakness. → Inconsistent data formats multiply: PDFs, images, handwritten text, emails ↳ Single-format prototypes miss 60% of production complexity ↳ Multi-modal extraction becomes mandatory, not optional → Performance degrades non-linearly: 100 documents work, 10K documents break ↳ Sequential processing creates bottlenecks ↳ Parallel multi-agent orchestration becomes the only viable approach → User expectations shift: "Why can't it handle this edge case?" ↳ Every exception requires graceful handling ↳ No-code platforms enable business users to adapt without engineering This is why SimplAI built no-code multi-agent orchestration as core capability. Enterprise adoption requires platforms that non-technical users can customize and deploy. We've seen 4000+ merchants and 40+ use cases built by teams without AI engineering backgrounds. When business users can build and deploy AI workflows, adoption accelerates 10X. Are your AI platforms accessible to the people who understand the workflows best? 🔥 Want more breakdowns like this? Follow along for insights on: → No-code agentic platforms that democratize enterprise AI → Platform design decisions that enable business user adoption PS: Whenever you're ready, take SimplAI for a spin at our website or book a demo: https://lnkd.in/g3JkSJHb

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