Jamie Roberts

🚀 Build Powerful AI Agents with Just 20 MCP Servers If you think you need hundreds of tools to create advanced AI agents… think again. You only need these 20 MCP Servers to unlock automation, intelligence, and execution at scale. Here’s the quick breakdown 👇 1️⃣ Sequential Thinking MCP – Breaks complex problems into clear, step-by-step logic for smarter decisions. 2️⃣ GitHub MCP – Syncs AI tools directly with GitHub for live code ops & automated workflows. 3️⃣ Serena MCP – Turns LLMs into coding agents for faster, automated software development. 4️⃣ DesktopCommander MCP – Lets AI control your desktop: run commands, manage files, and processes. 5️⃣ File System MCP – Secure file & directory operations across platforms. 6️⃣ Docker MCP – Launch & manage containerized AI tools for safe, isolated execution. 7️⃣ Supabase MCP – Natural language access & management for Supabase databases. 8️⃣ Puppeteer MCP – Automates browser navigation, clicks, scraping, and data extraction. 9️⃣ Playwright MCP – Cross-browser automation & remote debugging for testing/monitoring. 🔟 Firecrawl MCP – AI-powered scraping & structured data extraction from dynamic sites. 1️⃣1️⃣ DuckDuckGo MCP – Adds privacy-first web search to AI agents. 1️⃣2️⃣ Memory Bank MCP – Centralized project memory management for instant access. 1️⃣3️⃣ Knowledge Graph Memory MCP – Persistent, structured AI “memories” via local knowledge graphs. 1️⃣4️⃣ Markdownify MCP – Converts content into clean Markdown for easy editing/sharing. 1️⃣5️⃣ Graphiti MCP – Manage, search, and extract insights from custom knowledge graphs. 1️⃣6️⃣ Perplexity MCP – Real-time research & best-practice lookups via Perplexity AI. 1️⃣7️⃣ Magic UI MCP – Dynamic, AI-generated interfaces for interactive apps. 1️⃣8️⃣ Zen MCP – Minimal, distraction-free workflow orchestration for AI agents. 1️⃣9️⃣ Figma MCP – Programmatic design edits & reads in Figma files. 2️⃣0️⃣ MCP Compass – Your AI navigator for choosing the right tool/workflow instantly. 💡 Whether you’re building research bots, coding assistants, or autonomous workflows — these 20 MCPs cover the full stack. CC: Om Nalinde #AI #MCP #Automation #Agents #DevTools #Productivity

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What a difference a day makes… Overnight, we’ve seen landmark announcements that will accelerate the UK’s AI leadership at pace. 🟢 Nscale is investing heavily in UK AI infrastructure with a range of leading US technology companies, including Microsoft, NVIDIA and OpenAI 🟢 CoreWeave is committing £1.5 billion to power UK AI growth and innovation in partnership with NVIDIA and DataVita 🟢 A new UK-US pact on AI and life sciences is set to create tens of thousands of jobs Together, these announcements reinforce the UK’s position as a global hub for AI and digital innovation. All of this news coincided with our Partnering for AI event which took place this week (more to follow), where the ecosystem was brought together to discuss how, together, we can grow the UK AI market. At Schneider Electric, we’re delighted to see this progress – and welcome the Government’s focus on AI Growth Zones, particularly across the North East and Greater Manchester, with Careers at the Department for Science, Innovation and Technology (DSIT) continuing to play a central role. This moment represents an opportunity to deliver AI at scale while ensuring sustainability, energy resilience, and efficiency remain front and centre. By bending the energy curve of AI, we can ensure this wave of innovation brings long-term benefits for business, communities, and the planet. #AI #Tech #DataCentres

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🚀 Unlocking Agentic AI: From MCP to A2A, ACP, ACNBP & Beyond Agent-based systems are no longer theoretical. They're here—and protocols are the glue that makes them collaborate, delegate, and scale. If you're building with AI agents, here’s a quick guide to the evolving communication stack that powers next-gen autonomy 👇 🔌 MCP (Model Context Protocol) Think of this as the USB-C port for agents. It standardizes agent-to-tool communication via JSON-RPC, letting agents access APIs, databases, or search—ideal for stateless tool use and injecting external knowledge. 🤝 A2A (Agent‑to‑Agent Protocol) Built for collaboration across boundaries. Developed by Google, now open under the Linux Foundation, A2A enables task delegation, capability discovery, and real-time coordination between agents via HTTP/SSE and JSON-RPC. 🧠 ACP (Agent Collaboration Protocol) Structured conversations between agents. ACP introduces intent-based messaging (like REQUEST, INFORM, PROPOSE), shared ontologies, error handling, and multi-modal payload support—ideal for high-trust, real-time interactions. 🔐 ACNBP (Agent Capability Negotiation & Binding Protocol) Enabling smart agent matchmaking. This newer protocol supports secure discovery, negotiation, and commitment exchange between agents—essential when agents need to select trustworthy collaborators on the fly. 🌐 ANP + AG‑UI (Emerging standards) ANP handles agent discovery in dynamic ecosystems. AG‑UI powers real-time agent-to-user streaming (SSE/WebSockets), making agents feel alive in UI environments. 🧩 How it all fits together Imagine an agent workflow: Agent uses ANP to discover peers Agents negotiate with ACNBP Tasks are delegated via A2A External tools are accessed over MCP Conversations flow using ACP UIs stream results in real-time using AG‑UI Each protocol plays a unique role—but together, they enable resilient, intelligent, multi-agent systems. 🎯 Why this matters ✅ Modular: Plug in/out tools and agents 🛠️ Secure: Discovery, binding, identity baked in 📡 Scalable: Works across local, cloud, and hybrid systems 💬 Multimodal: Text, code, audio, and visuals 📈 Enterprise-ready: Backed by industry adoption Are you exploring multi-agent architectures yet? Curious how MCP, A2A, or ACP could reshape your enterprise workflows? Let’s connect and compare notes—agentic design is moving fast, and collaboration protocols are the key to making it work. #AgenticAI #A2A #MCP #MultiAgentSystems #AIProtocols #AIArchitecture #AIEngineering #FutureOfWork #EnterpriseAI #LLMops #AutonomousAgents

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Before we built the 5C™ Framework, we kept seeing the same pattern. Clients had the right pieces, but no way to coordinate them. One system for energy, another for access, a third for analytics. And every time something went wrong, they had to call five vendors and hope for cooperation. That’s no way to run a property. We developed the 5C™ Framework: Clarify, Connect, Collect, Coordinate, and Control as a way to bring order to the chaos. Think of it as a daily checklist. Clarify what you have, connect the systems, collect the data, coordinate the workflows and control the outcomes. Every project starts there now. It doesn't matter if it's a small apartment building or a major office tower, the approach remains the same. What I like about it is that you don't need to be a tech expert to make this work. You just need to follow the process and make decisions based on actual data instead of guesswork. The real payoff goes beyond just fixing operations. When you control your systems, your whole team starts thinking differently, more strategically, more proactively. You're building something instead of just keeping the lights on. Are you running your building, or are your vendors running you?

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For two decades, KDB+ has been the fastest, most performant, vector-native system in the world. It powers the world’s most prestigious and successful customers, running mission-critical systems across global banks, hedge funds, aerospace, and beyond. But the community asked for more: 1. Easier to learn and use 2. Accessible and affordable 3. A free version for real-world use 4. Open formats and interoperability 5. Natively support AI workloads and GPU acceleration With KDB-X, we’ve delivered. It keeps everything people love about KDB+ — and evolves it for the AI era: • Python and SQL built in, frictionless install “I installed it. It was flawless… curl, boom, download, done.” — Conor Hoekstra • A Community Edition, free for commercial use “This is really a huge step… you’ve changed the course of the company, essentially.” — Bob Therriault • Native support for Parquet, Iceberg, Python libraries “The installation process just worked out of the box, very smooth. You can even use it completely offline.” — Alex Unterrainer • Scale proven at 80B to 800B records per day • Full backward compatibility — nothing breaks • Module management: an App Store for data and AI • Acceleration: the Ferrari of data is now electric and turbocharged, with CPU+GPU architectures So what does the “X” stand for? Xceleration@Scale. Time series + sensor Structured & Unstructured Data & AI Agentic intelligence — powered by MCP servers. This is our iPhone moment for KDB: from vector-native to AI-native. From niche to accessible. From fast code to accelerated architectures. I want to thank our world-class KDB-X team, our customers who run the most demanding systems on earth, and the ArrayCast panel — Conor, Bob, Marshall, Adám, and Alex — for the thoughtful conversation. Listen to the episode here: https://lnkd.in/exAYKzuY The future of real-time intelligence isn’t just fast code. It’s accelerated architectures, at scale — open to everyone.

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The fastest way to waste $150,000 on data? Hire one person and expect them to do everything. We see this mistake all the time in mid-market companies. The CEO, CIO, or CFO says: “We need data.” They spend $10k on JD, for some engineer, and then... This is how it usually plays out: - Day 1, they’re asked to “set up the warehouse from the ground up, build ETLs, pipelines, dashboards, AI, and get leadership real insights.” - Within weeks, they’re buried in ad-hoc requests and Jira tickets. - The data becomes an endless project or a duct-taped solution. 12 months later, execs still don’t trust the numbers, and your first data hire is burned out or gone. This isn’t a talent issue. It’s a hiring issue. Here’s how we guide mid-market clients (50–500 FTEs) to avoid the trap: 1. Define the Business Problem First. Don’t start with “we need Snowflake” or “build a dashboard.” Start with: What are the 2–3 business questions we can’t answer today that cost us money? Agree on the definitions aligning with company objectives. Data architecture without a data strategy is just a cost center. It's like building without a map. 3. Hire for the Right Skill at the Right Time. Not a “Data Scientist.” Not an “ML Engineer.” Your first role should almost always be an Analytics Engineer. A strong generalist who can wear a few hats and deliver quick wins but not carry the whole strategy alone. A big recommendation is engaging with fractional leaders or firms at the start. They’ll set you up with a foundation from the get-go and help avoid million-dollar mistakes. 4. Future hires Adding new labor won't make you data-driven if there is no strategy. So invest in fractional leadership, as most do it the other way when it's too late and deep in cloud costs and no guidance. Role #2 should be a Data Engineer or analyst most cases. Focused on integrations, scalability, and cost optimization. Only hire when there is enough logistical complexity and it is a core problem. 4. Set a 90-Day Delivery Window. First hire should not disappear for 12 months “building the stack.” They should deliver a trusted, high-value dataset model within the first few weeks. Within 90 days, assess if your data investments are “cost" or “growth.” Quick wins build trust → 5. Place Data in the Right Reporting Line. If your data hire reports into IT, they become ticket-takers. When they report to COO/CFO/CEO, they stay focused on ROI, not infrastructure. Honestly... Hiring one person to wear six hats isn’t efficient. But at the same time, hiring more engineers doesn't make you data-driven. It doesn't matter if there is no data strategy and leadership. Most data issues are related to process, adoption, and trust. Comment "Data Team" and I'll send a playbook on how to build data team for success. 🧬 Repost if you think wearing all the hats as a “role" is not ideal. → Follow for more insights on leveraging data and how to become a truly data-driven organization.

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Business leaders are increasingly being asked to do more with less. One area where this shows up clearly is in database strategy. Many organizations are adding PostgreSQL alongside Oracle; not because Oracle is going away, but because PostgreSQL offers flexibility, scalability, and cost efficiency. When that happens, your operations and infrastructure leaders need confidence that their teams can handle both worlds. That is why we designed the PostgreSQL for Oracle DBAs: One-Day Intensive Training. In just a day, your Oracle-skilled staff gain the cross-training they need to: - Support PostgreSQL environments without relying on trial-and-error - Reduce escalations by addressing production issues with confidence - Ensure uptime and stability as systems scale It is a practical way to expand capabilities, protect SLAs, and make the most of your database investments. 🔗 Full details here: https://lnkd.in/dqcK5rJT

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