Mike Palmer

Jina finding a home in Elastic marks the third acquisition/pivot in this space just this year. Many of these «embedding model» companies have presented themselves as that, but in practice have been selling «enterprise search» and consulting for years. Offering managed endpoints for embedding inference wasn’t a good business model after the initial post-chat moment (See link for a great read on that) Jina acquired by Elastic, Voyage acquired by Mongo, and Mixedbread pivoted to managed no-code search endpoints. Everything changes https://lnkd.in/dv7wx9-b

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MIT just published a State of AI in business 2025 report. Worth a read, tl-dr: "For organizations currently trapped on the wrong side, the path forward is clear: stop investing in static tools that require constant prompting, start partnering with vendors who offer custom systems, and focus on workflow integration over flashy demos."

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Excited to announce that Ksolves will be showcasing at Community Over Code 2025, the The Apache Software Foundation Software Foundation (ASF) Conference, in Minneapolis from September 11–14! 🎉 Our CTO, Manish Gurnani, will be presenting on September 13, focusing on simplifying and automating NiFi Data Flow Deployments with Complete CI/CD Integration. He will delve into the topic of "No More NiFi UI" and provide valuable insights. Attendees will also have the opportunity to witness a demo of Data Flow Manager, our flagship Apache NiFi product. This tool serves as a robust control hub for Apache NiFi, facilitating seamless, code-free flow management from design to deployment, eliminating the need for the NiFi UI. Visit our booth to engage with live demonstrations and discover how we are reshaping the landscape of big data automation. Don't miss out on experiencing the future first-hand! #ApacheNiFi #ApacheFoundation #BigData #Gartner #ApacheSpark #DataScience #Ksolves #Python #CXO #PlatformEngineer #DevOps

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Today marks Six months in as SOCPAC CTO, and it somehow feels like both yesterday and six years ago. When I stepped into this role, we were behind the curve. Legacy systems, siloed data, and slow processes held us back. In just half a year, we’ve laid a Foundational Technical Architecture that gave us a modern backbone for data, AI, and autonomy. Since then, we’ve: 1. Unified our digital foundation for secure, scalable operations 2. Embedded AI and LLMs into daily workflows to speed decisions 3. Deployed containerized AI agents across all networks 4. Integrated autonomy at the edge, moving toward true man–machine teaming 5. Shifted our culture from cautious adoption to championing innovation The result? We’re no longer catching up, we’re setting the pace. What once felt like a distant goal is now part of our daily reality. All of this happened in six months, which is why it feels so surreal. It took countless late nights, cross-functional sprints, and the relentless dedication of an amazing team. I'm humbled by the trust the organization placed in me to drive this transformation, and I'm grateful to every person who rolled up their sleeves to make it real. This was truly a team effort, from the junior analysts using our new AI tools to the senior leaders who empowered us to take bold risks. And we’re just getting started. The foundation is in place, but now we get to build on it. In the next chapter, we'll be refining and scaling these capabilities, drilling deeper into AI-enhanced decision-making, and pushing the envelope on autonomous operations. The technology will keep evolving, and so will we. Our goal remains the same: give our operators a decisive edge, making it an extension of the warfighter and keep innovating responsibly. I’m humbled by what the team accomplished in such a short time, and excited for what’s next. The foundation is in place. Now we build on it. Six months down, and the journey is just beginning.

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I’ve been asked a few times lately: “𝐇𝐨𝐰 𝐝𝐨 𝐀2𝐀 𝐨𝐫 𝐌𝐂𝐏 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐭𝐢𝐞 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐋𝐋𝐌? 𝐃𝐨𝐞𝐬 𝐭𝐡𝐞 𝐋𝐋𝐌 𝐢𝐭𝐬𝐞𝐥𝐟 𝐦𝐚𝐤𝐞 𝐭𝐡𝐞 𝐜𝐚𝐥𝐥?” Here’s the quick breakdown 👇 ➡️ 𝐇𝐨𝐰 𝐋𝐋𝐌𝐬 𝐬𝐞𝐞 𝐭𝐨𝐨𝐥𝐬: When you call the model’s API (e.g. OpenAI), you send: * the system prompt * the user prompt * a list of tools (𝘨𝘦𝘯𝘦𝘳𝘪𝘤 𝘧𝘶𝘯𝘤𝘵𝘪𝘰𝘯𝘴 with descriptions + parameters). The LLM doesn’t know about MCP/A2A/APIs or services, it just sees a set of 𝘨𝘦𝘯𝘦𝘳𝘪𝘤 𝘧𝘶𝘯𝘤𝘵𝘪𝘰𝘯𝘴 it can call. ➡️ 𝐖𝐡𝐞𝐫𝐞 𝐭𝐡𝐞 𝐚𝐠𝐞𝐧𝐭 𝐜𝐨𝐦𝐞𝐬 𝐢𝐧: Your agent code (or framework) decides what those functions really represent. That is, they decide what to "bind to". In practice, they’re just API calls. MCP provides a thin, standard way to define these tools. That makes it easy for your agent to map MCP-defined tools into the LLM’s function call API. ➡️ 𝐖𝐡𝐚𝐭 𝐚𝐛𝐨𝐮𝐭 𝐀2𝐀? It’s the same idea. You take an agent-card.json (A2A’s format) and map it into function calls that the LLM understands. The LLM only knows how to call 𝐠𝐞𝐧𝐞𝐫𝐢𝐜 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬. It’s your agent (or agent framework) that decides how those map to MCP or A2A. These specs just give us a clean, standard way to describe the bindings. HTH! 

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