PipesHub Cloud Early Access Launches July 27

Context is the real bottleneck in AI, not the models. Excited to see PipesHub Cloud go live in early access on July 27, a fully managed context layer means teams can finally skip the plumbing and just build. If you're working on AI agents, this is worth a look. 

AI, data and cloud solutions provider TEKenable Ltd will create at least 30 jobs over the next 12 months with the launch of a new partnership programme. Kieran Kenefick https://lnkd.in/gPQaJSgR

🚀 Why I Prefer Self-Hosted n8n Over Cloud When building AI automations, many developers start with n8n Cloud. But after working on real-world workflows, I realized that Self-Hosted n8n (Docker + VPS) is a much better long-term approach. n8n Cloud ✅ Quick to get started ✅ No server management ✅ Great for beginners But: Monthly subscription cost Workflow limits depending on the plan Less infrastructure control Self-Hosted n8n ✅ Full control over your automation platform ✅ Lower long-term cost ✅ Deploy on your own VPS ✅ Easy to scale as your client base grows ✅ Perfect for agencies managing multiple client automations My Workflow 💻 Develop & Test locally using Docker ⬇️ 🧪 Validate workflows with the client ⬇️ ☁️ Deploy to a VPS for 24×7 automation This approach lets me build locally, test thoroughly, and move to production only when everything is ready. For anyone planning to build an AI Automation Agency, learning Docker and self-hosting is a valuable investment. How are you running your n8n projects—Cloud or Self-Hosted? #n8n #Automation #AIAgents #Docker #SelfHosted #DevOps #OpenSource #AI #WorkflowAutomation #SaaS #Tech

At Atlassian Team '26 Europe, adoption is just step one. What comes next for enterprise teams? AI that actually works, developers who move faster, and fully connected, aligned systems. The Adaptavist team is officially heading to Amsterdam for Atlassian Team '26 from 6–8 October. We're showcasing how enterprise organisations can go beyond cloud adoption to build resilient, high-performing digital ecosystems. Whether you're looking to get more from Rovo AI, optimise developer workflows or align and integrate your complex systems, our experts are ready to connect. ⤵️ #AtlassianTeam26 #Team26Europe #Adaptavist #AI #DevOps #Cloud

My second post on Substack is about defining the mission. Following my first posts here and on Substack about why it was time to part ways with Microsoft it makes sense to summarize what comes next. I am not looking to start a battle with them. My opinion of how they have gone to market with Azure for more than 15 years is no secret. The nature of big companies does mean that the people who need to hear those opinions usually don't so off we go. Microsoft has reduced Azure to "the best place to run AI applications." Rather than whinging about that I want to be part of the solution. I think the best way to do that is seek out business executives who feel something is very wrong with where their cloud journey has taken them. You were promised better, faster, cheaper and got same old, same old. Maybe some bits are worse. This post titled "Why Are We Here?" lays out the groundwork for restarting the cloud journey. Not SaaS. That is a different story. This will be about leveraging cloud economics to run enterprise applications. The era of agentic engineering is bringing new possibilities for building your own applications. But without revisiting how they are deployed and managed the outcomes will be less than satisfying. Yes, cloud is kind of old news. But the value is still there waiting to be harvested. Link to the post is in the first comment. Experts tell me that is the best way to do it.

Most cloud tools hand your team more dashboards. Your customers don't pay you for dashboards. 🚫 Insights are table stakes now. The MSPs pulling ahead aren't the ones with the most data, they're the ones who turn it into action the fastest. That's the shift MontyCloud is built for. As the trusted autonomous CloudOps platform, MontyCloud AI doesn't stop at "here's what's wrong." Ask CloudOps Assistant a question in plain language and it acts across the tools you already run: AWS, JIRA, Datadog, and more through the CloudOps MCP Server. A Well-Architected Review becomes findings, a remediation plan, and a JIRA ticket. A cost anomaly becomes a customer-ready report. Every action is tenant-scoped, auditable, and governed. ✅ Insight to outcome in minutes instead of weeks, without adding headcount. That's what trusted autonomous CloudOps looks like. Request a demo here: https://hubs.la/Q04s1J0C0 #AgenticAI #CloudOps #MSP #AWS #MCPServer

Did you see the Atlassian announcement? They're going to start training AI on customer data across Jira, Confluence, and their other cloud tools by default. You know how many of those you aren't seeing? Atlassian is a giant. They went above and beyond with a blog post on top of the policy update. And even then, unless you're paying for the top tier, you can't fully opt out. Most of your vendors aren't Atlassian. They won't put out an announcement before switching on AI over your data. Every vendor quietly turning on AI over your data is a risk you never vetted or even heard about. This is exactly the kind of exposure Lema can help you surface: like a vendor training AI on your data when nothing in their paperwork mentioned AI at all. Book a demo. We'll show you what your vendors changed without telling you. https://lnkd.in/eMpCdEaD

🏠 Still deciding on Atlassian Cloud? Here's what to check before you sign. 📅 Earlier this week, we looked at companies already on Atlassian Cloud, facing mandatory AI data collection starting August 17, 2026. This week: if you haven't moved yet, you're in a much better position — but only if you ask the right questions now. Before you commit to Cloud, get clear answers on: 🔒 Data residency vs. data usage. Where your data is stored and how it's used for AI training are two different things. Atlassian's own residency options cover data at rest — they don't necessarily cover AI training pipelines. Ask explicitly, get it in writing. 💰 The real cost of control. On Standard and Premium tiers, metadata contribution isn't optional. Full opt-out only exists at Enterprise level. Factor that into your total cost comparison — "control over your own data" is effectively a paid tier, not a default right. 📉 What "de-identified and aggregated" actually means for you. Project names, workflow labels, sprint content — even de-identified, this can reveal business strategy. If you're in a regulated industry, this is a compliance conversation, not just an IT one. ⏳ The Data Center clock. Atlassian Data Center support ends in 2029. If you're moving away from Server/DC anyway, Cloud isn't the only direction — it's just the default one, because it's the one being pushed hardest. 🚀 The on-prem option you might not have fully considered. Self-hosted, on-premise platforms exist that give you full control over deployment, data residency, and AI usage — without the tiered privacy structure. If data sovereignty matters to your organisation, this deserves a proper look, not a footnote. 🤔 The decision you're making now will shape your data governance for years. What's the biggest factor in your decision — cost, compliance, or control? ➡️ Next week, we'll dig into Data Centre itself — what the 2029 end-of-life actually means in practice, and what your options really are. #DataSovereignty #OnPremise #AtlassianDataCenter #CloudDecisions

At the start of 2026, software stocks lost roughly $1 trillion in combined market value in a matter of weeks. The driver was a re-pricing of risk. Investors started asking how many of the point-solution SaaS tools sitting in every enterprise stack are replaceable by an AI agent that can just do the task, without a dedicated app and login. Most SaaS tools weren't purchased irrationally. Each one solved a real problem for the team that bought it. The average enterprise now runs somewhere around 100+ of them. Nobody sat down and planned for a hundred-app stack — it accumulated, one reasonable decision at a time, over a decade of cheap cloud deployment. Now AI needs one authoritative, structured answer instead of a hundred separate, half-synced ones. That's a data model story. Are you consolidating toward fewer apps with the same fragmented data underneath, or toward a data model built for whatever queries it next — human or machine?

Google Cloud has published a structured readiness framework for enterprise agent deployment. The questions span governance, orchestration, tool use, data access, and control. This is vendor-led standardisation arriving before most enterprises have internal standards of their own. The gap between platform capability and enterprise readiness is the defining execution risk of 2026. Readiness frameworks matter because they force specificity. Vague agent strategies do not survive contact with production environments. Which of those 20 questions does your organisation currently have no answer for? Morichella Associates Limited https://morichella.com https://lnkd.in/dBCZNpYN