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.

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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.

I dropped a video on how I build n8n workflows. After building for 32 different clients and making 10k in the last 6 months. I decided to document my method so that once and if I hire somebody in the future, I can direct them to this video. Because I break down how I go about building, why I do certain things and why I build the way I do. The idea with the video is that I should not need to explain how to build. Granted I do not go into how nodes works, or data tables. What I walk through is a way to not get stuck while building. How to approach system thinking and have a working system. That it's easy to manage. I hope that y'all find it useful :)

Heyy today I just want to be very raw and real and share my experience based on the clients I've acquired specifically, why automation systems fail First of all, what you need to understand is this, people don't get that there are two types of clients I keep running into. The first one thinks AI is cheap or even free. The second one thinks AI is way too expensive and completely denies it. The problem comes down to the same path. Everyone is denying it And what they need to understand is this: "it's not a plug-and-play system" That's why it's not cheap or expensive it's a service. That's why we're calling it an automation 'agency' People ask me, "How much do you charge?" I mean, if I don't know the numbers, if I don't know the data, if I don't know anything about what I'm working with, I cannot give you a summary of what I'll charge. It will either be overpriced or underpriced, and the project won't be done properly. So yeah, that's a big problem I see everywhere. People tend to think, "OK, just give me a price. Just give me a quick average price." AAaahhhhh But what they need to understand is that's not how this works. It works based on 'how' you're going to solve the problem, what it's going to cost, what tools you need and what systems I need to build Let me give you an example If a restaurant is handling thousands of calls a day and I'm building an AI voice agent for them, versus another restaurant handling 500 calls per day that's a totally different game. The numbers might both seem huge, but they're still different. So the bill is not going to be the same. The cost is not going to be the same