Dimitris Gkanatsios

The Apache Knox team is proud to announce the release of Apache Knox v2.1.0! Apache Knox is a REST API Gateway for providing secure access to the data and processing resources of Hadoop clusters. More details on Apache Knox can be found at: https://knox.apache.org/ The release bits are available at: https://lnkd.in/dzkwBD-2 This release contains over 180 changes: upgrades, improvements, and bug fixes! We would like to thank all of the contributors who made the release possible and to invite others interested in helping out to engage the community on the dev@ and users@ lists!

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Hiring more engineers won’t speed you up. Maor Shlomo argues that adding people only adds overhead. With AI agents, a single developer can move faster than a team. Tune into the full conversation on Tessl.

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Progressive Disclosure using predicates in Google ADK agents! In my latest article, I break down a high-level architecture for Java Agents that solves this using semantic filtering. I walk through how to intercept tool selection using Vector Embeddings and a custom ToolPredicate to dynamically filter tools based on user intent. https://lnkd.in/g5H-m-r5

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Vinodh Kannan Sadayamuthu, Akhil Melakunta and Vignesh Selvam wrote a great walkthrough on migrating JMS applications to Amazon MQ for RabbitMQ this week. https://lnkd.in/gtA9wjsy The short version: your business logic doesn't change. You swap the connection factory, update the endpoint, and your producers, consumers, and listeners stay as-written. What I think is really interesting is what happens after the migration. Because the RabbitMQ JMS Client translates JMS calls into AMQP 0-9-1, non-Java services can now consume from the same queues using native AMQP clients. A Java-centric messaging layer becomes something any service can participate in. The blog covers the full path — broker setup, dependency changes, a real before/after ActiveMQ migration — and it's a surprisingly small diff. Worth a read if you're running JMS workloads. #AmazonMQ #RabbitMQ #JMS

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Jensen Huang just called OpenClaw "the most important release of software, probably ever." When it comes from the man who defined the GPU era, then people listen ( after discounting the vested interest obviously :) While people focus on his quote, we should understand that the real story is adoption speed of Openclaw: it took 3 weeks to reach Linux's 30-year milestone. That's not growth, but an explosion. OpenClaw isn't a tool. It's the operating system for agents that do work. When NVIDIA's CEO calls a project THE thing, every CTO adds it to their roadmap. ByteDance, Alibaba, Tencent are integrating OpenClaw —not for GitHub stars, but because they're terrified of falling behind. On March 6, 2026, nearly 1,000 people queued outside Tencent's Shenzhen headquarters for free, on-the-spot installation of OpenClaw AI. That shows the desperation to be in-step with the technology. In many orgs, OpenClaw is already in production. JPMorgan handles fraud detection with it, Walmart wired it into loan approvals. These are real systems with real money and stakes. The Agenci AI market is fragmented: LangGraph, CrewAI, AutoGen, LangChain, Semantic Kernel, smolagents, PydanticAI. Seven frameworks fighting. But the outcome is obvious. Whoever controls the execution layer controls the decade ahead. The builders who shipped agents in 2024 when LLMs were not ready for it, they already know the significance of these events in 2026 . Which side of this are you on?

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My fellow principal engineer Arron Bailiss wrote an excellent blog post about the design principles of Strands Agents 🧬, what the agentic "model-driven approach" means, and guidance for how to think about multi-agent architectures. https://lnkd.in/gh5p5UXi

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See why OCI leads the AI infrastructure market with RoCE-optimized networks, demonstrated scalability to over 32,000 GPUs, and ultralow latency at scale. https://lnkd.in/dnt_ksDb

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