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Did anyone receive this as well? Linux kernel vulnerability disclosed (CVE-2026-31431) A security flaw called "Copy Fail" (CVE-2026-31431) has been found in the Linux kernel. It affects nearly all major Linux distributions – including Ubuntu, Debian, RHEL, Amazon Linux, SUSE, Fedora, and more – on any kernel built between 2017 and today. The vulnerability lets a local user gain full admin access to a server. It can also affect containerized environments. The fix takes just a few minutes. Option 1: Update your kernel (recommended) - For Ubuntu/Debian: sudo apt update, sudo apt upgrade -y - For RHEL-based systems: sudo yum update Then reboot your server. Option 2: Disable the affected module (temporary fix) If updating right now isn't an option, disable the vulnerable module to reduce exposure: echo "install algif_aead /bin/false" > /etc/modprobe.d/disable-algif.conf rmmod algif_aead 2>/dev/null || true This won't affect SSH, TLS, LUKS, or OpenSSL. If you have any questions, our support team is available 24/7.

SORRY FOLKS: This post was designed for start up /improvers in Tony, Dean and IGOR’s AI Advantage Club: I was going to take it down BUT: It could still be useful here… WHY: These folk are your future customer targets: Those that do AI, but struggle with the AUTOMATION side: TELL ME IF ITS NOT RELEVANT and I will take it down… You do not need to be a coder to work well with AI. You do not need to speak in technical language. You do not need to build complicated automations before you understand what you are actually trying to achieve. You need to talk to AI clearly. Tell it what you do. Tell it what you want. Tell it what success looks like. That is the bit many beginners miss. A lot of people approach AI like they are trying to solve one tiny puzzle piece at a time: “What prompt do I use for this?” “What command do I type?” “What tool do I connect?” “What automation should I build?” That can work, but it can also create confusion very quickly. A better way, especially for creative thinkers, is to start with the whole picture. Think of a jigsaw puzzle. If someone gives you a thousand pieces but does not show you the picture on the box, you might still make progress, but it will be slow, frustrating, and full of guesswork. Now imagine the picture is the Titanic. If the Titanic is still in Southampton, you have useful context. You can see the ship, the dock, the land, the colours, the structure. You can start to understand where the pieces belong. But if the Titanic is halfway across the Atlantic, surrounded by sea and sky, everything starts to look the same. Blue above, blue below, no landmarks, no clear edges. That is what happens when you ask AI for isolated pieces without giving it the picture. The AI may still help, but it is guessing with you. So my biggest learning is this: Do not start by asking AI for one puzzle piece. Start by showing it the picture on the box. Say something like: “I am trying to build a simple workflow that helps me create, organise, and publish content without overwhelming myself. I am not a coder. I want low friction, clear steps, and reusable prompts. Success means I can use this every week without getting lost.”

Hey everyone 👋 Question for those of you building AI chatbots for clients (or your own product): When you deploy a chatbot on a website, are you actually GDPR-compliant — or are you hoping nobody notices? I’m based in Austria, building AI automation solutions for European SMBs, and the #1 question I get asked isn’t “how smart is your bot?” — it’s: “Where is the data going? Who processes it? Do you have an AVV?” Most US-built chatbots fail this conversation immediately because: • No signed Data Processing Agreement (Art. 28 GDPR requirement) • Subprocessors (OpenAI, Pinecone, Vercel) not disclosed • No Datenschutzerklärung (privacy notice) in the local language • EU AI Act transparency requirements ignored (mandatory from August 2026) Curious — how are you handling this for European clients? Or are you avoiding the European market entirely because it feels too complicated? Happy to share what I’ve built (a 10-point GDPR transparency package) if anyone wants to dig in 🙌

Hey everyone, After 3+ years building AI automation systems, I realized something: the bottleneck isn't the model. It's context and resources. Better context = better agents. But here's the real problem: everything is segmented. Your agents are in Pydantic AI. Your workflows are in n8n. Your knowledge is scattered. Your execution is fragmented. So I built AgeniusDesk, a unified platform that manages ALL of it: agents, workflows, knowledge, resources. One command center. No silos. What is it? A unified management layer for AI platforms, agents, and workflow frameworks (n8n, Pydantic AI, Flowise, etc.): - Multi-instance n8n visibility + control from one dashboard - AI agents (Pydantic, Claude, OpenAI, local models) as first-class citizens - Real-time error detection + AI diagnostics (catches issues before they blow up) - Agent Lab: write and debug code with AI, deploy instantly - Encrypted secrets vault (never plaintext, never exposed) - Shared resource layer: context, guardrails, execution contracts - Full local/self-hosted control (no vendor lock-in) Built on Python + FastAPI + Vanilla JS. Docker compose ready. Why I'm posting this here: This community gets it. You're not asking for another no-code builder or magic button. You're building real systems, running agents in production, managing multiple deployments. AgeniusDesk is built for that. What I want from you: Drop a comment and tell me: - → Are you managing multiple n8n instances or agents right now? - → What's your biggest pain point? (visibility? errors? scale?) - → Would you test-drive this if it solved that problem? I'm open-sourcing the whole thing. No strings. Just want to build something the community actually needs.